Amazon Web Services
Well-Architected delivery
- EKS, ECS & Lambda platform engineering
- Security Hub, GuardDuty & IAM hardening
- Landing Zones, FinOps & cost optimization
AI-Powered Security Operations — Trusted by 60+ enterprises
Secure.
Automated.
Intelligent.
anyops.ai unifies AI/ML, DevOps, DevSecOps, and Cybersecurity across AWS, Azure & Google Cloud into a single cohesive practice — helping regulated industries ship faster, safer, and smarter, with zero breaches across 14 months of managed operations.
$4.2M avg. breach cost avoided per client
60 days to SOC 2 audit-ready — evidence automated
40% avg. cloud spend reduction after platform pass
Aligned:
ISO 27001
SOC 2
HIPAA
PCI-DSS
NIST CSF
60+
Enterprise clients
98%
Retention rate
3×
Deploy velocity
0
Breaches · 14 mo
IT Operations at a Glance
One accountable team across every cloud you run
Certified engineers design, secure, and operate production workloads on AWS, Microsoft Azure, and Google Cloud — unifying AI/ML, DevOps, DevSecOps, and cybersecurity under a single practice, inside your accounts and your controls.
Microsoft Azure
Cloud & security operations
- AKS, App Service & Azure Functions
- Microsoft Sentinel, Defender & Entra ID
- Landing Zones & policy-as-code governance
Google Cloud
Data, AI & platform delivery
- GKE, Cloud Run & Anthos platforms
- Security Command Center & IAM design
- BigQuery, Vertex AI & MLOps pipelines
3
Public clouds under one practice
99.99%
Managed uptime target
24/7
SOC & on-call coverage
40%
Avg. cloud spend reduced
<15m
Median incident response
98%
Client retention rate
60+
Enterprise deployments
45%
Avg. MTTR reduction
3x
Faster deployment velocity
Integrates natively with industry-leading platforms
Amazon Web Services
GitHub
Kubernetes
HashiCorp Vault
Splunk SIEM
CrowdStrike XDR
Okta IAM
Google Cloud
Microsoft Azure
SIGNATURE PROGRAM
Forward Deployment
Engineering
Senior engineers embedded directly inside your organization — deploying production infrastructure, integrating security tooling, and training your team by doing. They ship working software, not slide decks. Zero vendor lock-in on exit.
2–12
Week engagements
100%
Embedded on-site or remote
Zero
Vendor lock-in on exit
Ships production infrastructure from day one
Deploys inside your security perimeter — data never leaves
Pairs directly with your engineers — teaches by doing
Full runbooks & ADRs handed over on exit
FDE Delivery Feed
LIVE
Production cutover complete — FinTech client · 6 wks, zero downtime
SOC 2 evidence automated — 312 controls · audit prep in 5 days
$380K annual cloud waste eliminated — SaaS engagement
Handover complete — client team operating solo · score 9.6/10
AI & Machine Learning
Intelligent Systems That Learn, Adapt, and Protect
From strategy to production deployment, we design and operationalize machine learning systems that generate real business value — and integrate natively with your security posture.
47+
Production ML models shipped for enterprise clients
94.3%
Median model confidence maintained in production
3–8×
Earlier incident detection vs. threshold-based alerting
<60 days
From roadmap sign-off to first model in production
AI Strategy & Roadmap
Most organizations have AI ambitions but lack a credible path to production. We bridge that gap — assessing your data infrastructure, talent, and tooling against your strategic goals to produce a phased, budget-conscious roadmap that actually gets executed.
- Data maturity & infrastructure readiness audit
- AI use-case prioritization by ROI and feasibility score
- Build-vs-buy analysis across model vendors and open-source
- AI governance, ethics, and risk management framework
- Executive alignment workshops and board-ready presentations
Typical outcome: 6–18 month phased AI roadmap with prioritized use cases and clear ownership.
What the phased roadmap looks like
P1
FoundationsWeeks 1–6
Data audit, governance baseline, and 1–2 quick-win automations that prove value early.
P2
Pilot & ProveMonths 2–6
Two to three prioritized use cases built to production — measured against agreed ROI baselines.
P3
Scale & EmbedMonths 6–18
Platformize shared ML infrastructure, upskill internal teams, and expand the use-case portfolio.
ML Model Development & MLOps
We engineer machine learning systems end-to-end — from raw data pipelines and feature engineering through model training, evaluation, and production deployment — with the operational discipline that keeps models reliable months after go-live.
- Custom model development: supervised, unsupervised, and LLM fine-tuning
- Production-grade feature stores with point-in-time correctness
- Automated retraining pipelines triggered by data or performance drift
- Model versioning, A/B testing, and shadow deployment frameworks
- Explainability and bias auditing for regulated industries
Typical outcome: Model deployment lead time reduced from weeks to hours with full audit trails.
Predictive Analytics & Anomaly Detection
Reactive monitoring catches problems after they hurt you. Our predictive analytics practice builds models that learn the normal behavior of your systems and people — surfacing deviations, forecasting failures, and flagging threats before they reach production or breach data.
- Time-series forecasting for infrastructure capacity and business KPIs
- Unsupervised anomaly detection across logs, metrics, and network flows
- Real-time scoring pipelines with sub-100ms inference latency
- Security-focused UEBA: detecting lateral movement and data exfiltration
- Seasonality-aware models resilient to traffic spikes and release cycles
Typical outcome: 40–70% reduction in false-positive alerts; incidents caught 3–8× earlier.
AI-Powered Observability Dashboards
Modern distributed systems produce more telemetry than humans can read. We build intelligence on top of your existing observability stack — unifying metrics, logs, and traces into a single pane, then layering AI to turn raw signals into clear, actionable ops decisions.
- Unified telemetry ingestion across cloud, containers, and on-prem (OpenTelemetry)
- AI-generated incident summaries with probable root-cause ranking
- Predictive alerting: fires before thresholds breach, not after
- Natural-language query interface — ask your ops data plain questions
- Cost attribution and waste surfacing across multi-cloud environments
Typical outcome: MTTR reduced by 45%; on-call engineer time cut by 30% within 90 days.
LLM & Generative AI Engineering
We take generative AI past the demo stage — building retrieval-augmented systems grounded in your private data, hardened against prompt injection, and measured against evaluation suites before anything reaches a customer or an auditor.
- RAG architecture over your private data — vector + hybrid search
- LLM fine-tuning and prompt optimization with regression eval suites
- Guardrails: prompt-injection defense, PII redaction, output filtering
- Agent workflows with human-in-the-loop approval gates
- Cost & latency engineering: caching, routing, model right-sizing
Typical outcome: grounded answer accuracy above 95% with hallucination rate under 2% on domain benchmarks.
Production ML Reference Pipeline
Every model we ship runs on this backbone — versioned, monitored, and auditable end to end.
Data Sources
Streams, lakes & warehouses
Feature Store
Point-in-time correct
Train & Evaluate
Tracked experiments
Model Registry
Versioned & signed
Serving
CI/CD & canary rollout
Drift Monitoring
Auto-retrain triggers
Responsible AI, Built In
Governance isn’t an afterthought — every engagement ships with the controls regulators and boards expect.
NIST AI RMF
Risk management framework mapped to every use case — govern, map, measure, manage.
EU AI Act
Risk-tier classification and conformity-assessment readiness for EU-exposed systems.
ISO/IEC 42001
AI management system alignment — policies, roles, and lifecycle controls documented.
Model Cards & Bias Audits
Documented lineage, explainability reports, and human-oversight gates for every model.
How We Deliver AI — From Idea to Operations
011–2 wks
Assess
Data readiness audit, use-case ROI scoring, and model risk review.
022–4 wks
Prototype
Offline model on your data — success metrics agreed before build starts.
033–6 wks
Productionize
CI/CD for models, monitoring, rollback paths, and full audit trails.
042–3 wks
Integrate
Wired into your applications, SIEM, and decision workflows.
05Ongoing
Operate
Drift detection, automated retraining, and quarterly value reviews.
Forward Deployment Engineering
Your Environment. Our Engineers.
Zero Friction.
Senior engineers embedded directly inside your organization — deploying, configuring, and iterating on anyops.ai capabilities within your infrastructure and security perimeter. They ship working software, not slide decks.
Embedded on-site or remote
Ships working software, not decks
Full knowledge transfer on exit
Zero vendor lock-in
120+
FDE engagements delivered across 14 regulated industries
9.2/10
Median client engagement score at handover
87%
Of clients extend or return for a second engagement
150+
Engineers trained through the FDE academy tracks
Why Forward Deployment Is the 2026 Delivery Model
The Forward Deployed Engineer went from one company’s competitive edge to the default operating model for serious AI and platform delivery. Enterprises stopped buying tools that sit unused — they now buy embedded teams that ship outcomes inside their own environment.
70%
Of enterprise AI initiatives stall between pilot and production
3×
Growth in demand for embedded “forward-deployed” engineering since 2023
#1
Delivery model adopted by today’s leading AI-native companies
2×
Faster path to production value vs. license-and-leave software
From Software to Outcomes
Pioneered by Palantir and now standard at the leading AI labs and data platforms, the model embeds senior engineers who deliver working systems — not licenses, not decks. You pay for shipped outcomes, measured against a definition of done.
AI Made Deployment the Bottleneck
Models and platforms are commoditizing fast; the hard part is integrating them safely inside a real, regulated enterprise. Forward-deployed engineers close the last mile that pilots never cross — from proof-of-concept to production traffic.
Regulated Industries Demand It
Data can’t leave the perimeter and compliance can’t be outsourced. Embedded delivery inside your cloud accounts is the only model that satisfies security, data residency, and speed at the same time — with your team owning it on exit.
The market signal: outcome-based, embedded delivery has become how serious AI work actually reaches production. anyops.ai built its entire practice around it — senior engineers inside your environment, fixed-scope outcomes, and full knowledge transfer so your team owns everything on exit.
For Organizations
Deploy Faster.
Secure Deeper.
Embed senior anyops.ai engineers directly in your team for 2–12 weeks. They ship production-grade infrastructure, integrate security tooling, and leave your engineers fully trained to own everything built.
- Production deliverables from day one
- Deploys inside your security perimeter
- Full compliance evidence wiring included
- Zero vendor lock-in on exit
For Engineers & Students
Build Real Systems.
Accelerate Your Career.
Work alongside senior FDEs on live enterprise deployments — not simulations. Build a portfolio of production infrastructure that proves you can ship, mentored by engineers with 8+ years of real-world experience.
- Real Fortune 500 & Series C production work
- 1-on-1 senior engineer mentorship
- Structured path: student → junior → senior
- Internship-to-hire track available
01
Discovery Sprint
FDE embeds with your team for 3–5 days to audit your infrastructure, toolchain, and current pain points. Outputs a prioritized technical execution plan.
02
Deployment & Integration
Live configuration of pipelines, security tooling, ML infrastructure, or observability stack inside your environment. Daily standups. Weekly demos to stakeholders.
03
Iteration & Hardening
Rapid feedback loops with your engineers. Edge cases addressed. Security hardened. Performance tuned. Documentation written alongside the build — not after.
04
Knowledge Transfer
Structured handover to your internal team: runbooks, architecture decision records, and live training sessions. Your engineers own it from day one.
What Ships — Week by Week
Every engagement is time-boxed with defined outputs. Here's what a typical 8-week implementation looks like.
Week 1
Discovery & Environment Audit
- Infrastructure inventory & dependency map
- Security gap analysis against SOC 2 / ISO 27001
- Toolchain assessment & integration plan
- Stakeholder alignment & prioritized backlog
Deliverable Technical execution plan + risk register
Weeks 2–3
Foundation Build — Infrastructure & Pipelines
- Terraform modules for your cloud (AWS / Azure / GCP)
- Kubernetes cluster bootstrapped with hardened defaults
- CI/CD pipeline with SAST + secrets scanning gates
- Secrets management (Vault / KMS) wired to pipelines
Deliverable Fully working IaC + pipeline — deployed to staging
Weeks 4–5
Security Layer & Observability
- SIEM integration & alert tuning (Splunk / Sentinel / Elastic)
- CSPM configured across all cloud accounts
- Policy-as-code gates (OPA / Rego) — SOC 2 controls mapped
- AI anomaly detection model deployed on your telemetry
Deliverable Live security dashboard + automated compliance evidence
Weeks 6–7
Hardening, Tuning & Team Training
- Production cut-over with zero-downtime deployment
- Incident response playbooks authored & tested
- Your engineers paired and owning every component
- Performance tuning + cost optimization pass
Deliverable Production-live infrastructure + trained internal team
Week 8
Handover & Ongoing Support Setup
- Architecture Decision Records (ADRs) finalized
- Runbooks tested in live incident simulation
- 30-day post-engagement office hours included
- Optional retainer for ongoing advisory
Deliverable Full documentation package — your team owns everything
FDE vs. the Alternatives
Most organizations choose between slow, expensive, or fragile. FDE is none of those.
Big 4 / SI Consulting
Hire In-House
anyops.ai FDE
Time to first deliverable
6–12 weeks (kick-off & planning)
4–9 months to hire & ramp
Week 2 — production builds in your env
Typical cost (6 months)
$400K–$900K
$180K salary + $60K overhead
Fixed engagement — no bloat
What you actually get
Slide decks & recommendations
1 person, 1 skill set
Working production infrastructure
Knowledge transfer
Dependency on vendor to operate
Limited — one person’s knowledge
Full runbooks, ADRs, paired training
Data & security risk
Consultants access your data off-site
Internal — but single point of failure
Deploys inside your perimeter — data stays
Vendor lock-in on exit
High — custom tooling & ongoing licenses
Risk of key-person dependency
Zero — all open standards, your team owns it
2×
Faster time-to-production vs. traditional consulting
40%
Average cloud cost reduction after FDE infrastructure pass
<5 days
SOC 2 audit prep time after FDE compliance wiring
100%
Of engagements ship production deliverables before week 3
Engagement Models & Team Composition
Scale the engagement to the problem — from a single embedded expert to a full platform pod. Every model is fixed-scope, fixed-price, with weekly executive reporting.
Solo FDE
1 senior engineer
Duration2–6 weeks
CadenceDaily standups · weekly demo
Best forSingle-system deployment or integration
- One focused deliverable — e.g. CI/CD security gates or SIEM tuning
- Direct pairing with your existing team
- Runbook + ADR handover included
Most common
FDE Pod
2–3 engineers · 1 lead
Duration6–10 weeks
CadenceDaily standups · weekly exec report
Best forFull platform or security program build-out
- Parallel workstreams — infra, security, and ML tracks
- Compliance evidence wiring (SOC 2 / ISO 27001)
- 30-day post-engagement office hours
Embedded Platform Team
4+ engineers · lead + architect
Duration10–12+ weeks
CadenceEmbedded in your sprint rituals
Best forEnterprise transformation or migration
- Multi-cloud / multi-region delivery capacity
- Dedicated architect for governance & design authority
- Structured hiring support to backfill your own team
Infrastructure Deployment
Stand up production-grade Kubernetes clusters, Terraform-managed cloud infrastructure, or GitOps pipelines directly inside your AWS, Azure, or GCP account — configured to your compliance and networking requirements.
- Cloud-native IaC from day one
- Hardened network policies & IAM
- Fully auditable state management
AI & ML Integration
Deploy anomaly detection, risk scoring, or UEBA models inside your data environment. Connect to your existing SIEM, data warehouse, or event streams — no data leaves your perimeter.
- On-prem and VPC-isolated deployments
- Custom model fine-tuning on your data
- Real-time inference pipeline setup
Security Pipeline Enablement
Embed SAST, DAST, secrets scanning, and policy-as-code gates into your existing CI/CD workflows. Configure severity thresholds, notification routing, and break-the-build rules tailored to your risk appetite.
- Zero disruption to existing developer workflow
- Incremental rollout & tuning
- SOC 2 / ISO 27001 evidence wiring
Team Enablement & Coaching
FDEs pair directly with your engineers — not just setting up systems but teaching the underlying principles. Secure coding practices, IaC patterns, incident response playbooks, and MLOps workflows are transferred by doing, not presenting.
- Hands-on pairing sessions
- Custom runbooks & ADRs
- Post-engagement office hours
The Stack We Deploy
Production-hardened, open-standard tooling — chosen to fit your environment, never to create lock-in.
Cloud & IaC
Containers & Orchestration
CI/CD & Supply Chain
Security & Compliance
Data & ML
Observability
Training Academy
anyops.ai Engineering Tracks
Structured programs for developers at every level — from students shipping their first pipeline to senior engineers mastering enterprise security architecture. All tracks include real production work.
DevSecOps Engineering
12 weeks · All levels
Student
Junior
Mid-Level
- CI/CD pipeline design with security gates
- SAST, DAST & SCA integration
- Policy-as-code with OPA / Rego
- Kubernetes security hardening
- Secrets management — Vault & KMS
Ship a production DevSecOps pipeline for a real client before graduation.
AI & ML Engineering
16 weeks · Junior to Senior
Junior
Mid-Level
Senior
- MLOps pipelines & feature stores
- Model training, evaluation & deployment
- LLM fine-tuning & RAG architecture
- Anomaly detection & UEBA models
- Production inference optimization
Deploy a production ML model inside a regulated industry environment.
Cloud Security Architecture
10 weeks · Mid to Senior
Mid-Level
Senior
- Zero-Trust architecture design
- SIEM / SOAR configuration & tuning
- Cloud security posture management
- Identity & access management (IAM/PAM)
- Incident response & digital forensics
Architect Zero-Trust controls for a cloud-native enterprise environment.
The FDE Career Ladder
A defined competency path from first pipeline to owning enterprise engagements — every level maps to real production responsibility, not time served.
L0
Student FDE
0–1 yrs
- Ships first production pipeline under mentorship
- Earns CKA or Terraform Associate certification
- Pairs daily with senior engineers on live client work
L1
Junior FDE
1–3 yrs
- Owns a single workstream inside an FDE pod
- Runs client standups and demo sessions
- Hardens CI/CD and IaC without supervision
L2
Mid-Level FDE
3–6 yrs
- Leads solo engagements end-to-end
- Designs security & platform architecture
- Mentors juniors and students on-site
L3
Senior / Lead FDE
6+ yrs
- Runs multi-pod enterprise programs
- Owns executive relationships & steering
- Sets technical strategy and governance authority
New Grad & Student FDE Program
Start Your Career on
Real Production Systems
No toy projects. No simulated environments. anyops.ai’s student FDE track places you alongside senior engineers on live enterprise deployments — building the portfolio that proves you can ship.
01
Real enterprise production work
Deploy infrastructure for Fortune 500 and Series C companies — work that goes live
02
1-on-1 senior mentorship
Paired with an FDE who has 8+ years of production experience across regulated industries
03
Certifications included
CKA, AWS Solutions Architect, or HashiCorp Terraform Associate — your choice
04
Hire track for top performers
High-performing graduates are offered full-time FDE positions at competitive salaries
Program at a Glance
Rolling applications — reviewed within 5 business days
Duration
10–16 weeks
Format
Remote & on-site rotations
Stipend
Paid — competitive rate
Prerequisites
CS degree or bootcamp + 1 project
Cohort size
8–12 engineers per cohort
Next cohort
Applications open now
We respond to every applicant — no ghosting, no automated rejections.
Industries We Transform
Regulated industries are our core — we understand the compliance obligations, data residency constraints, and risk profiles that generalist consultants don't.
Financial Services
Zero-Trust deployments for banks, fintechs, and insurance carriers. Fraud ML models deployed on-prem. Audit evidence automated for examiners.
Healthcare & Life Sciences
PHI-safe ML pipelines deployed inside VPC. HIPAA-compliant CI/CD with automated BAA management. Zero PHI leaves your environment.
Government & Defense
Air-gapped and GovCloud deployments for federal agencies and contractors. CMMC Level 2–3 preparation. NIST 800-53 control automation.
Series B–D Startups
Enterprise-grade infrastructure for fast-growing companies without a 20-person platform team. Ship your SOC 2 audit in 60 days, not 9 months.
Manufacturing & OT
OT/IT network segmentation, anomaly detection on industrial telemetry, and secure remote access for plant floor systems — without production downtime.
SaaS & Cloud-Native
Multi-tenant security architecture, supply-chain hardening, and automated GDPR/CCPA evidence collection for SaaS companies under enterprise procurement scrutiny.
Our Production Guarantee
If your FDE engagement does not ship working, production-grade code inside your environment by the end of week 2, we extend the engagement at no additional cost — no questions asked. We stand behind output, not effort.
What We Need From You to Start
FDE engagements are deliberately low-burden on your organization — four things, and we handle the rest.
1
An executive sponsor
One accountable stakeholder for a 30-minute weekly steering check-in — that’s the entire meeting load.
2
Environment access on day one
Least-privilege, time-boxed credentials that you issue and can revoke at any moment.
3
1–2 engineers for pairing
The people who will own everything after handover — they learn by building alongside the FDE.
4
A definition of done
We co-write measurable success criteria before work begins — the engagement is scored against them.
Frequently Asked Questions
The questions security and engineering leaders ask before an engagement.
How do FDEs access our environment securely?
Your rules, your perimeter. FDEs work under your IAM policies with least-privilege, time-boxed credentials that you issue and can revoke at any time. All work happens inside your cloud accounts and repositories — no code or data is copied out. NDAs and background checks are standard, and we can operate under your clearance requirements.
Who owns the code and infrastructure that gets built?
You do — 100%. Everything is committed to your repositories, under your organization, from day one. We deploy open standards (Terraform, Kubernetes, OPA) specifically so nothing depends on us after exit.
How fast can an engagement start?
Scoping call within 48 hours, engagement letter within a week, and an FDE embedded within 2–3 weeks of signature. For urgent, incident-driven work we maintain a rapid-response bench that can start in days.
Do FDEs work on-site or remote?
Both. Most engagements run embedded-remote inside your Slack/Teams, VPN, and sprint rituals, with on-site weeks at kickoff and production cut-over. Fully on-site engagements are available for air-gapped or classified environments.
How is pricing structured?
Fixed price per engagement, scoped against the week-by-week deliverables above — no hourly billing, no scope creep. If week-2 production delivery slips, the engagement extends at our cost under the Production Guarantee.
What happens after the engagement ends?
Every engagement closes with runbooks, Architecture Decision Records, and live handover training, plus 30 days of office hours. Optional advisory retainers are available — but our exit metric is your team operating everything without us.
Ready to deploy faster with an embedded expert?
Tell us your environment, timeline, and goals — we'll match you with the right FDE profile within 48 hours.
Quantum Computing
Prepare for the Quantum Era.
Protect What You Have Today.
Quantum computers will break today's RSA and ECC encryption within the decade. We help enterprises assess their cryptographic exposure, migrate to NIST post-quantum standards, and harness quantum-enhanced ML — before the threat window closes.
Post-Quantum Cryptography
Quantum Risk Assessment
Quantum ML Acceleration
Crypto-Agile Architecture
NIST PQC Standards
2030
NIST target to deprecate classical RSA / ECC cryptography
4
NIST PQC standards we implement — ML-KEM, ML-DSA, SLH-DSA, FN-DSA
100%
Cryptographic asset inventory coverage in assessment phase
3–6 mo
Typical timeline for a crypto-agile migration pilot
Post-Quantum Cryptography Migration
Harvest-now-decrypt-later attacks are already underway. We inventory your cryptographic assets and migrate RSA, ECC, and DH to NIST-standardized post-quantum algorithms before adversaries gain quantum capability.
- Cryptographic inventory & dependency mapping
- Migration to CRYSTALS-Kyber & CRYSTALS-Dilithium
- TLS 1.3 + hybrid PQC cipher suite deployment
- Certificate lifecycle management for PQC PKI
Typical outcome: Full PQC migration roadmap delivered in 4 weeks with zero service disruption.
Quantum Risk Assessment
Before you can defend against quantum threats you need to understand your exposure. We audit all systems to identify cryptographic assets at risk and prioritize remediation by business impact and data sensitivity.
- Crypto-agility posture scoring across all systems
- Data sensitivity classification & shelf-life analysis
- Quantum-vulnerable protocol inventory (SSH, VPN, PKI)
- Executive risk report with remediation timeline
Typical outcome: Complete quantum exposure map with prioritized remediation plan in 2 weeks.
Quantum-Safe Architecture Design
Future-proof your infrastructure at the design level. We redesign your key management, certificate authority, and secure communications to be crypto-agile — ready to swap algorithms as NIST standards evolve without full rearchitecting.
- Crypto-agile PKI and key management design
- Zero-Trust network with PQC-hardened tunnels
- HSM integration for quantum-resistant key storage
- Algorithm agility framework for rapid future migration
Typical outcome: Architecture designed for 20+ year quantum resilience with zero rework on next standard update.
Quantum-Enhanced Machine Learning
Quantum computers deliver exponential speedups for specific ML workloads. We identify where quantum advantage applies in your AI pipeline — optimization, sampling, and feature-space expansion — and prototype hybrid quantum-classical models.
- Quantum advantage opportunity assessment for your ML stack
- Hybrid classical-quantum model prototyping (IBM Q, AWS Braket)
- Quantum annealing for combinatorial optimization problems
- Quantum-enhanced anomaly detection for security workloads
Typical outcome: 10–100× speedup on qualifying optimization workloads with hybrid quantum-classical pipelines.
Quantum Readiness Program
A structured 90-day program that takes your organization from quantum-unaware to quantum-ready — covering executive education, engineering upskilling, inventory completion, and your first PQC migration milestone shipped to production.
- Executive quantum threat briefings & board reporting
- Engineering team PQC upskilling workshops
- Regulatory alignment (DORA, NIS2, NIST CSF 2.0)
- First PQC algorithm deployed to production by day 90
Typical outcome: Board-approved quantum migration plan and first PQC deployment live within 90 days.
Quantum Threat Intelligence & Monitoring
The quantum threat landscape evolves fast. We continuously track nation-state quantum advances, NIST standard updates, and algorithm breaks — alerting your security team with actionable guidance before your window to act closes.
- Continuous tracking of quantum capability advances
- Automated alerts on NIST PQC standard changes
- Nation-state quantum program intelligence briefings
- Monthly quantum threat horizon reports
Typical outcome: 6–18 month advance warning on quantum threats before they impact your cryptographic assets.
The Recording Has Already Started
“Harvest now, decrypt later” is an active adversary strategy: encrypted traffic is being captured today to be decrypted the moment quantum hardware catches up. If your data must stay confidential for 5+ years — contracts, health records, IP, citizen data — it is already exposed.
Board-level exposure: under DORA and NIS2, cryptographic risk is now a regulated, reportable obligation — “we’ll migrate when quantum arrives” no longer passes an audit.
2024Done
Standards Final
NIST publishes FIPS 203/204/205 — ML-KEM, ML-DSA, and SLH-DSA are production-ready.
2027Near
Federal Migration
US agencies mandated to begin PQC transition — suppliers must show credible roadmaps.
2030Deadline
RSA / ECC Deprecated
NIST timeline flags classical crypto as legacy — auditors and regulators follow.
2035Hard stop
Classical Disallowed
CNSA 2.0 requires full PQC across national security systems — and their vendors.
Student Track
Quantum-Safe Engineering Track
The industry has a five-year window to migrate the world’s cryptography — and almost no engineers trained to do it. This track makes you one of them: hands-on PQC engineering on real client migrations, mentored by practicing FDEs.
- Lattice cryptography foundations — why ML-KEM & ML-DSA work
- Cryptographic inventory tooling — find every key, cert, and cipher in an estate
- Hybrid TLS deployment — classical + PQC key exchange in production
- HSM & PKI integration for quantum-resistant key storage
- Capstone: migrate a live service to hybrid PQC under FDE review
Program at a Glance
Duration8 weeks
FormatRemote · cohort-based
LevelMid-level engineers+
PrereqsTLS & networking basics
OutcomeCapstone portfolio + certificate
Next cohortApplications open
Cybersecurity
Defense-Grade Security
for Cloud-Native Infrastructure
From Zero-Trust architecture to 24/7 AI-assisted threat monitoring, we help enterprises build and maintain a resilient security posture — fully aligned with SOC 2, ISO 27001, HIPAA, and PCI-DSS frameworks.
Zero-Trust Architecture
24/7 SOC Coverage
AI-Assisted Threat Triage
Compliance-Aligned Controls
SIEM & SOAR Driven
24/7
AI-assisted SOC coverage across all managed clients
<9 min
Mean time to detect across monitored environments
2.4M
Security events processed per minute at peak
0
Breaches across managed clients in the last 14 months
SOC & Threat Monitoring
SIEM/SOAR-driven security operations with AI-assisted alert triage, threat hunting, and 24/7 monitoring across cloud workloads, endpoints, and network traffic.
- AI-assisted alert triage & deduplication
- 24/7 threat hunting across all layers
- UEBA & behavioral baseline monitoring
- Automated playbook-driven response
Typical outcome: MTTD reduced from 4+ hours to under 25 minutes.
Zero-Trust Architecture Design
Design and implement Zero-Trust network architectures — micro-segmentation, identity-aware proxy, and continuous verification to minimize blast radius from compromised credentials.
- Network micro-segmentation design
- Identity-aware proxy (BeyondCorp model)
- Continuous device & user verification
- Lateral movement prevention controls
Typical outcome: Attack blast radius reduced by 70%+ in hybrid cloud environments.
Identity & Access Management
Privileged access management, just-in-time provisioning, MFA enforcement, and role-based access control for cloud and on-prem environments — with full audit logging.
- Privileged access management (PAM)
- Just-in-time access provisioning
- MFA enforcement & SSO integration
- RBAC & least-privilege audit trails
Typical outcome: 95% reduction in standing privileged access within 60 days.
Cloud Security Posture Management
Continuous CSPM across AWS, Azure, and GCP — misconfiguration detection, automated remediation, and real-time compliance dashboards aligned to CIS Benchmarks and NIST CSF.
- Multi-cloud misconfiguration detection
- Automated remediation workflows
- CIS Benchmark & NIST CSF alignment
- Real-time compliance drift alerts
Typical outcome: Critical cloud misconfigs resolved within 2 hours of detection.
EDR/XDR Integration
Deploy and manage extended detection and response across endpoints, cloud workloads, and network — unified into a single pane with cross-layer threat correlation.
- Unified endpoint & cloud XDR platform
- Cross-layer threat correlation engine
- Automated threat containment workflows
- Forensic timeline & root-cause analysis
Typical outcome: Mean time to contain (MTTC) under 15 minutes for high-severity threats.
Incident Response & Digital Forensics
Rapid incident containment, root-cause investigation, and post-incident reporting — with tabletop exercises and playbook development to reduce MTTR on future incidents.
- Rapid triage & blast-radius containment
- Digital forensics & evidence preservation
- Post-incident root-cause reporting
- Tabletop exercises & playbook authoring
Typical outcome: MTTR reduced by 50%+ with pre-built, environment-specific playbooks.
The 2026 Threat Economy — What Changed
Cyber risk moved from the IT budget to the board agenda. Four market shifts are driving security spend this year — and all four reward organizations that can prove their posture, not just describe it.
$4.9M
Average cost of a data breach — up 10% year over year
68%
Of breaches involve stolen credentials or a human element
3×
Surge in AI-generated phishing & deepfake fraud attempts
96 hrs
SEC-class incident disclosure windows now standard globally
Cyber Insurance Hardened
Insurers now audit controls before underwriting — MFA everywhere, EDR coverage, and 24/7 monitoring are prerequisites for a policy, not premium discounts.
Procurement Gates on Security
Enterprise buyers require SOC 2 / ISO 27001 evidence before signature. A provable security posture is now a revenue asset that shortens sales cycles.
Executive Accountability
NIS2 and SEC rules attach personal liability to management for cyber-governance failures — boards now demand evidence, not assurances.
The market signal: attackers industrialized with AI faster than most defenses did. The differentiator in 2026 isn’t more tools — it’s AI-assisted detection with automated evidence, which is exactly what we deploy.
Compliance Framework Alignment
ISO 27001
SOC 2 Type II
HIPAA
PCI-DSS
NIST CSF
Our security practice supports compliance initiatives aligned with each of these frameworks. Engagements include documentation and evidence artifacts that map directly to each standard's controls.
AI-First Security
Machine Learning at the Core
of Every Security Decision
Security tools generate enormous volumes of alerts. Our AI-first approach cuts through the noise — prioritizing real threats, accelerating response, and continuously learning from your environment.
2.4M
Events analyzed / min
70%
Alert fatigue reduction
<22m
Mean time to detect
-
Automated Threat Detection Real-time
ML models trained on threat intelligence feeds and your environment's baseline behavior detect novel attacks that signature-based tools miss.
-
Intelligent Alert Triage 70% reduction
AI correlates events across your SIEM, cloud logs, and endpoint telemetry to surface only high-confidence, actionable alerts — eliminating analyst fatigue.
-
Behavioral Analysis (UEBA) Continuous
Continuous profiling of users and service accounts — flagging lateral movement, privilege escalation, and data exfiltration patterns in real time.
-
Deployment Risk Scoring Pre-release
Every deployment is scored for security risk before it reaches production — analyzing code changes, dependency delta, and infrastructure impact with a trained model.
DevOps
Accelerate Delivery Velocity. Eliminate Deployment Risk.
We architect and automate the pipelines, infrastructure, and reliability practices that enable engineering teams to deploy with confidence — reducing lead time from commit to production from weeks to minutes, without compromising stability or compliance.
15×
More frequent deployments after pipeline rebuild
<1 hr
Lead time from commit to production — elite DORA tier
42 min
Median time to restore service across managed environments
<5%
Change failure rate after reliability engineering pass
CI/CD Pipeline Design & Automation
Fully automated pipelines on GitHub Actions, GitLab CI, or Azure DevOps — with parallel testing, artifact management, and gated deployments to staging and production environments.
Infrastructure as Code
Repeatable, auditable cloud infrastructure with Terraform and CloudFormation. Drift detection, state management, and module libraries for AWS, Azure, and GCP environments.
Containerization & Kubernetes
Docker-based application packaging, Kubernetes cluster design, Helm chart authoring, and GitOps workflows with ArgoCD or Flux for declarative, version-controlled delivery.
SRE & Cloud Cost Optimization
SLO/SLA definition, error budget management, chaos engineering, and FinOps practices to reduce cloud spend by 20–40% without sacrificing reliability or performance.
DevSecOps
Security Woven Into Every Stage of Delivery
We embed security as a first-class concern at every point of your software delivery lifecycle — so compliance is continuous, not a last-minute gate.
92%
Of vulnerabilities caught before they reach production
<8 min
Full security scan suite added to CI — no pipeline slowdown
100%
Compliance evidence auto-collected for SOC 2 / ISO 27001
65%
Fewer false-positive findings after policy & alert tuning
01
Plan
- Threat modeling
- Security stories
- Risk assessment
02
Code
- Pre-commit hooks
- IDE security linting
- Secure code templates
03
Build
- SAST & SCA
- SBOM generation
- Dependency audit
04
Test
- DAST scanning
- Container scanning
- Pen-test automation
05
Release
- Policy-as-code gates
- Secrets validation
- Compliance sign-off
06
Operate
- Runtime monitoring
- Compliance reporting
- Incident response
Secure CI/CD Pipelines
Integrate SAST, DAST, SCA, and dependency scanning directly into your pipelines — with configurable break-the-build severity thresholds that stop vulnerabilities before they ship.
- SAST & DAST gates on every PR
- Software Composition Analysis (SCA)
- Configurable severity thresholds
- Pipeline-native SBOM generation
Typical outcome: Critical vulnerabilities blocked at build — not discovered in production.
Policy-as-Code & Compliance Automation
Define security and compliance rules as versioned code using OPA/Rego or Sentinel — enforced automatically on every deployment with full audit trails for SOC 2 and ISO 27001.
- OPA/Rego policy authoring & testing
- Automated SOC 2 evidence collection
- Drift detection & remediation alerts
- Policy version control & rollback
Typical outcome: Audit prep time reduced from 6 weeks to under 5 days.
Secrets Management Integration
Eliminate hardcoded credentials with HashiCorp Vault, AWS KMS, and Azure Key Vault — including dynamic secret injection, automated rotation, and repo-wide secrets scanning.
- Dynamic secrets with zero-TTL leases
- Automated rotation pipelines
- Git repo secrets scanning & remediation
- Least-privilege access enforcement
Typical outcome: Zero hardcoded credentials in 90 days across all repositories.
Developer Security Guardrails
Pre-commit hooks, secure code templates, and IDE-integrated linting that surface vulnerabilities at the developer's keyboard — slashing the cost of remediation by an order of magnitude.
- IDE plugins for real-time vuln feedback
- Pre-commit hooks with auto-remediation
- Secure coding standards & templates
- Developer security training integration
Typical outcome: 80% of security findings caught before code review — not in production.
Industry Solutions
Built for the Industries
That Can't Afford to Fail
Real-world engagements for organizations where security, compliance, and delivery velocity are simultaneously non-negotiable.
🏭 FinTech & Payments
☁ Cloud-Native SaaS
🏥 Healthcare & Life Sciences
🏢 Enterprise & Manufacturing
FinTech
6 wks → 4 days
Audit prep time
Continuous PCI-DSS Compliance for a Payments Platform
Challenge
A payments platform deploying dozens of times daily had a 3-week lag between changes and compliance evidence — manual audits couldn't keep pace with their release velocity.
Solution
Policy-as-code gates in CI/CD, automated PCI-DSS evidence collection, and an AI model that flags compliance-impacting changes before merge — zero disruption to engineer workflow.
- ✔ Compliance evidence auto-generated on every deploy
- ✔ Audit preparation: 6 weeks → 4 days
- ✔ Zero compliance gaps across 14 months of CD
Cloud-Native
4.5 hrs → 22 min
Mean time to detect
AI Threat Detection for an 800-Microservice Kubernetes Platform
Challenge
A SaaS platform's SIEM generated 50,000 alerts/day — analysts were triaging less than 15%, leaving real threats buried in noise across 800+ microservices.
Solution
ML-based alert correlation engine trained on historical data, enriched with commercial CTI feeds, integrated with existing SIEM via API — deployed in 3 weeks, no rip-and-replace.
- ✔ Alert volume reduced 68% through intelligent deduplication
- ✔ MTTD improved: 4.5 hours → 22 minutes
- ✔ Analysts handle 3× more true positives with same headcount
Healthcare
3 wks → 2 days
Model deployment time
HIPAA-Aligned MLOps Platform for Healthcare Analytics
Challenge
A healthcare analytics provider needed to train ML models on PHI data with full HIPAA compliance, per-version audit trails, and zero risk of data exposure across the pipeline.
Solution
HIPAA-aligned MLOps platform with encrypted feature stores, differential privacy controls, model lineage tracking, and automated access auditing on Zero-Trust Kubernetes.
- ✔ All HIPAA technical safeguards addressed in the ML pipeline
- ✔ Deployment cycle: 3 weeks → 2 days
- ✔ Zero PHI exposure events since platform launch
Enterprise
Quarterly → Weekly
Deployment frequency
DevSecOps Modernization for a Fortune 500 Manufacturer
Challenge
A Fortune 500 manufacturer shipping monolithic apps quarterly with no automated security testing — critical vulnerabilities were routinely reaching production environments.
Solution
Phased modernization: containerization, Git-based CI/CD with SAST/DAST gates, developer secure coding training, and continuous CSPM across their AWS migration workloads.
- ✔ Deployment frequency: quarterly → weekly sprints
- ✔ High-severity production vulnerabilities down 60%
- ✔ Developer security training: 94% completion rate
Verified Client Results
Proven Outcomes at Scale
Real numbers from production engagements — not benchmarks, not best-case projections. Verified with client sign-off.
60+
Engagements delivered
98%
Client retention rate
$0
Production breaches across all clients
4.9/5
Average client satisfaction score
🏚 Insurance
4-month engagement
45%
Faster incident response
Before
2.7 hrs avg MTTR
After
<90 min avg MTTR
Global Insurance Carrier
Integrated AI-assisted triage into a legacy SOC workflow. Analysts now resolve critical incidents in under 90 minutes on average — SOAR playbooks fully automated 55% of tier-1 responses.
- ✔ SOAR automated 55% of tier-1 responses entirely
- ✔ Alert triage time reduced by 70%
- ✔ Zero P1 SLA breaches in 12 months post-deployment
☁ Cloud SaaS
3-month engagement
3.2x
Deployment velocity increase
Before
18 days lead time
After
<6 hours lead time
Series C SaaS Platform
Rebuilt CI/CD pipelines on GitHub Actions with Terraform-managed infrastructure and Kubernetes GitOps. Lead time dropped 3.2× with a 40% reduction in post-deploy incidents.
- ✔ Lead time: 18 days → under 6 hours
- ✔ Post-deploy incidents reduced 40%
- ✔ Infrastructure drift eliminated with GitOps
🏭 Banking
6-month engagement
60%
Fewer critical vulnerabilities
Before
No automated security
After
Full DevSecOps pipeline
Regional Banking Group
Implemented DevSecOps across 12 development teams: SAST/DAST in pipelines, secrets scanning, and a central policy-as-code engine. Critical vulnerabilities fell 60% within 6 months.
- ✔ Critical vulns in production down 60% in 6 months
- ✔ 12 teams onboarded with zero pipeline downtime
- ✔ SOC 2 audit evidence generated automatically
How It Works
One Platform, Four Disciplines, Continuous Loop
anyops.ai operates as a continuous intelligence loop — ingesting signals, analyzing them with ML, automating responses, and feeding learnings back into the system.
Ingest
Collect code commits, pipeline events, cloud logs, network flows, and endpoint telemetry from across your environment — normalized into a unified data schema.
- GitHub, GitLab, Azure DevOps
- CloudTrail, VPC Flow Logs, K8s audit logs
- SIEM connectors (Splunk, Sentinel, Elastic)
Analyze
ML models and rule engines process incoming data streams in real time — detecting anomalies, scoring risk, correlating events, and generating threat hypotheses.
- Anomaly detection models
- Behavioral baselines (UEBA)
- Deployment risk scoring
Automate
Policy-as-code engines and SOAR playbooks act on analysis results — blocking risky deployments, isolating compromised workloads, or triggering remediation workflows.
- OPA/Rego policy enforcement
- SOAR playbook execution
- Auto-remediation via IaC
Respond
Human-in-the-loop escalation for high-confidence threats — with AI-generated summaries, enriched context, and recommended actions that cut analyst decision time by 60%.
- AI-enriched incident briefings
- Slack / PagerDuty / Jira integration
- Post-incident model retraining
↻ Outcomes feed back into model retraining — the system improves with every incident.
Engagement Models
Start Where You Are. Scale as You Grow.
Every organization is at a different point in its security and DevOps maturity. We offer flexible engagement models that meet you there.
Discovery
4–6 weeks
Discovery & Strategy Engagement
Best for: Teams evaluating their security posture
A time-boxed assessment of your current security posture, DevOps maturity, and AI readiness. Deliverables include a prioritized roadmap, gap analysis, and executive presentation.
- ✔ Security posture assessment
- ✔ DevOps pipeline audit
- ✔ AI/ML opportunity analysis
- ✔ Prioritized roadmap
Most Popular
Implementation
3–6 months
Implementation Projects
Best for: Teams ready to build & ship production infrastructure
Hands-on delivery of a defined scope: a DevSecOps pipeline build-out, Zero-Trust architecture implementation, or ML platform deployment — with knowledge transfer to your team.
- ✔ Defined scope & deliverables
- ✔ Embedded delivery team
- ✔ Documentation & runbooks
- ✔ Team training & enablement
Managed
12+ months ongoing
Managed DevSecOps & Security Operations
Best for: Orgs that need a continuous security operations capability
Ongoing managed service for security operations, compliance monitoring, and DevOps platform management — with SLA-backed response times and monthly reporting.
- ✔ 24/7 security monitoring
- ✔ Continuous compliance reporting
- ✔ Pipeline health management
- ✔ Monthly executive reviews
AI Co-Pilot
Per-seat · Monthly
AI Co-Pilot for DevOps Teams
Best for: Engineering teams that want AI insights without a full engagement
A managed ML-driven recommendation layer that sits alongside your existing tools — surfacing deployment risks, infrastructure anomalies, and cost optimization opportunities in your daily workflow.
- ✔ Deployment risk scoring
- ✔ Anomaly alerts in Slack/Teams
- ✔ Cost optimization recommendations
- ✔ Integrates with existing toolchain
Resources
Knowledge for Security-Minded Engineers
Practical guides, playbooks, and research — no marketing fluff, just content written by practitioners.
Guide
The DevSecOps Playbook
A 60-page practitioner guide to embedding security into every stage of your CI/CD pipeline — with tool recommendations, policy templates, and a maturity model.
Download Free →
Whitepaper
AI for Cloud Security: Separating Signal from Noise
Research paper on applying ML-based anomaly detection to cloud environments — covering data requirements, model selection, and common failure modes to avoid.
Download Free →
Webinar
Zero Trust in a Hybrid Cloud World
On-demand webinar: how to design and incrementally implement a Zero-Trust architecture across AWS, Azure, and on-prem — without a "rip and replace" approach.
Watch Free →
Guide
MLOps for Regulated Industries
Step-by-step guide to building HIPAA- and PCI-DSS-aligned ML pipelines — covering data governance, model auditing, and compliant feature store architectures.
Download Free →
Webinar
Kubernetes Security Hardening in Practice
Live walkthroughs of RBAC configuration, network policy design, admission controllers, and runtime security with Falco — for teams running production Kubernetes.
Coming Soon
Whitepaper
SBOM as a Security Control
How software bill of materials generation, continuous analysis, and vulnerability correlation can become an operational security control — not just a compliance artifact.
Coming Soon
Client Testimonials
Trusted by Security-First Teams
From Fortune 500 enterprises to Series C hypergrowth platforms — hear directly from the engineering leaders who've deployed with us.
★★★★★
“anyops.ai didn’t just deliver a report — they shipped production infrastructure on day one of the engagement and handed it to our team fully documented. Their FDE model is genuinely different from traditional consulting. Three months in, our deployment velocity is 3× what it was, and we haven’t had a single P1 security incident since.”
★★★★★
“We went from deploying quarterly to shipping multiple times a day. The anyops.ai team built the entire CI/CD and security pipeline inside our environment — fully documented and owned by our engineers on day one.”
★★★★★
“Building a HIPAA-aligned MLOps platform on PHI data is genuinely hard. anyops.ai solved it in 10 weeks and handed us a complete, auditable system. Our model deployment cycle went from 3 weeks to under 48 hours.”
About anyops.ai
Engineers Who Ship.
Security That Holds.
anyops.ai exists to close the distance between security ambition and what teams can actually ship. Regulated organizations were investing heavily in tooling and consulting decks, yet still couldn’t move production systems safely. So we built a different operating model — senior engineers embedded inside your environment, delivering production-grade software and transferring complete ownership to your team on exit. Today we unify AI/ML, DevOps, DevSecOps, and cybersecurity into a single accountable practice, trusted by 60+ enterprises across 14 regulated industries where getting it wrong is not an option.
2021
Founded by security & platform engineers
80+
Engineers & forward-deployed specialists on staff
9
Countries with active client engagements
98%
Client retention — most return for a second engagement
Manjusha K
Founder
Founder’s Vision
I started anyops.ai after watching capable teams get stuck — not for lack of ambition, but because security and delivery were treated as someone else’s problem, handed over in slide decks that never became working systems.
I wanted to build a company where engineers stand inside the problem alongside our clients: shipping real software, owning the outcome, and leaving every team stronger than we found them. Not vendors on the outside — partners on the inside.
That belief still drives every hire we make and every engagement we take. If we can help even the most regulated organization move fast without fear, we’ve done our job — and we are only getting started.
Manjusha K
Founder, anyops.ai
Our Vision
To make world-class security and AI engineering accessible to every regulated organization — not as slide decks and license fees, but as working systems their own teams own and operate.
We see a future where shipping fast and staying secure are the same decision — where critical infrastructure is built by engineers trained on real production work, not simulations, and where no organization is forced to choose between velocity and trust.
Our Mission
We embed senior engineers directly inside our clients’ environments to deliver production-grade AI, DevOps, DevSecOps, and cybersecurity outcomes — measured against a shared definition of done, with full knowledge transfer on exit and zero vendor lock-in.
In parallel, we train the next generation of forward-deployed engineers through hands-on academy tracks — closing the talent gap that leaves most security and platform programs understaffed, one production engagement at a time.
What We Stand For
Four principles that shape every engagement, every hire, and every line of code we ship.
Ship, Don’t Slide
We’re measured on working software running in production — never on decks, hours logged, or effort.
Your Perimeter, Your Data
Every engagement runs inside your accounts under your controls. Nothing leaves your environment — ever.
Own It On Exit
Open standards, full runbooks, and paired training mean your team owns everything we build — zero lock-in.
Train the Next Generation
We grow engineers on real client work — a student-to-senior ladder that rebuilds the talent pipeline.
Leadership Team
The founders and board steering anyops.ai — supported by an operating team that has spent its career shipping secure systems inside the world’s most demanding environments.
Manjusha K
Founder
Founder of anyops.ai. Set out to replace security theater with engineers who ship — building a company where regulated organizations get production outcomes and full ownership, never dependency. Sets the vision, culture, and standard the entire team is measured against.
Ritvik Krishna K
Co-Founder
Co-founder driving how anyops.ai builds and scales. Owns the engineering model that places senior talent inside client environments — open standards, zero lock-in, and production-grade delivery from day one.
Vimala Devi K
Chairman
Chairman of anyops.ai. Provides the governance, guidance, and long-term perspective that keep the company accountable to its clients and its mission — steady leadership behind a fast-moving engineering team.
S Anjali Devi
Chief AI Officer
Leads anyops.ai’s applied AI and machine-learning practice — the anomaly-detection and LLM systems that run inside regulated data perimeters, never outside them. Sets how models are built, evaluated, and shipped to production.
P Shanthi
Chief Information Security Officer
Owns security strategy and compliance at anyops.ai — the controls, standards, and Zero-Trust posture that every engagement is measured against across regulated industries.
V Prashanthi
VP, Forward Deployment Engineering
Leads the forward-deployment practice — pairing senior engineers with client environments and standing behind production delivery from first commit to full handover.
Monogram avatars shown — ready to be updated with team headshots.
Get in Touch
Let's Solve Your Security
& Delivery Challenges
We work with organizations serious about security, compliance, and engineering excellence. Describe your situation — we'll give you an honest assessment of fit and respond within one business day.
- No sales pressure — honest assessment of fit
- First conversation is always free
- Speak directly with engineers, not account managers
Email us directly
info@anyops.ai
Response time
Within 1 business day
First call
Always free — no obligation
Who you'll talk to
Senior engineers, not sales reps
What Happens After You Submit
-
01Research & Context ReviewWe review your message, research your company, tech stack, and current challenges before reaching out.
-
02Engineer Reaches OutA senior engineer — not a sales rep — contacts you directly with context-aware questions tailored to your situation.
-
0330-Min Discovery CallAn honest, no-obligation conversation. We assess fit together — if we're not the right match, we'll say so and recommend who is.
-
04Engagement ProposalIf there's a strong fit, we send a clear scope, timeline, and approach document within 48 hours. No surprises.
★★★★★
"anyops.ai didn't just deliver a report — they shipped production infrastructure on day one of the engagement and handed it to our team fully documented."
Request received!
We'll review your message and reach out to you within 1 business day. Keep an eye on your inbox.