The infrastructure
that makes AI
production-ready.
Cloud architecture, DevOps pipelines, and MLOps infrastructure built with the same rigour we bring to defence and railway systems: reliability-first, cost-disciplined, and designed to be operated by your team after handover.
Ask AI about this service
Most AI projects fail not because the models are wrong but because the surrounding infrastructure cannot support them in production. Unreliable data pipelines, fragile deployment processes, absent monitoring, and cloud architectures that were never designed for sovereignty requirements all conspire to keep AI in the pilot stage. We design the platform layer that elevates AI from experiment to operational capability, and we build it so your team can run it, not so we can retain a support contract indefinitely.
What we build.
Cloud Architecture
Reliability-first cloud architectures with fault isolation, graceful degradation, and cost control designed in, and private, on-premises, or air-gapped options for data sovereignty.
Infrastructure as Code & DevOps
All infrastructure managed as code (Terraform, Ansible, and Helm) in version-controlled repos, with GitOps workflows that reconcile state, detect drift, and audit every change.
Kubernetes & Container Platform
Kubernetes clusters for cloud and on-premises deployments, designed for operational simplicity after handover and instrumented with Prometheus and Grafana from day one.
MLOps Platform
Platforms that standardise the full model lifecycle (experiment tracking, dataset versioning, training pipelines, model registry, deployment, and production monitoring), with prompt management and evaluation for LLMs.
Observability & Security
Full-stack observability (metrics, logs, and distributed traces) in place from the first workload, alongside network policies, least-privilege IAM, secrets management, and CI/CD vulnerability scanning.
A deployable, observable environment in 2 weeks.
From the day you engage us, not from a kickoff weeks later. Every milestone is a result you can see.
- Day 3
Target architecture and landing zone agreed
- Day 14
Infrastructure as code with CI/CD and monitoring live
- Day 30
First workloads migrated with runbooks handed to your team
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Fixed scope
Scope, price, and milestones are agreed before work starts.
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No surprises
Weekly demos and plain-language status. Changes are priced before they are built.
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Compliance from day one
Network policy, least-privilege access, secrets management, and vulnerability scanning ship with the first pipeline.
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You own the IP
Code, models, and documentation transfer to you on successful handover.
Know the price, the date,
and the exit before we start.
Fixed-scope project. A defined deliverable at a fixed price and date. Payment is released as each milestone is accepted. The same terms are written into every statement of work for ai platform engineering, so there is nothing to discover later.
- Quote Within 5 working days Price per milestone, dates, and named architect
- Priced One price per milestone Third-party costs listed separately, at cost. Invoiced in USD for international clients
- Paid On accepted working software Not on calendar dates or hours logged
- Changes Written and priced first Cost and date impact before work starts
You fund accepted work, never a promise.
Drag to move through the engagement. Money is released only as each gate is accepted, so what you have paid never runs ahead of what you can use.
- Milestone 1Day 3
Target architecture and landing zone agreed
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 20% - Milestone 2Day 14
Infrastructure as code with CI/CD and monitoring live
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - Milestone 3Day 30
First workloads migrated with runbooks handed to your team
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - HandoverAt final acceptance
Code, architecture documents, deployment scripts, and credentials transferred to you.
Accepted whenFinal acceptance signed. Defects then fixed under warranty at no charge.
Releases 20%
Illustrative split. The exact percentages and dates are fixed in your statement of work.
When something goes wrong, who pays?
Every project meets surprises. What matters is who carries them. Pick one and compare how three commercial models respond.
The hours keep running while the team investigates. You pay for the surprise.
A change request is raised, usually priced at a premium and argued over.
We verified it before quoting, and the risk register held an allowance. Any overrun beyond it is ours.
It is absorbed into the hours. The date slips quietly and you learn at the invoice.
A change fee arrives, often as a lump sum with little explanation.
You get a written amendment with cost and date impact. Nothing starts until you approve it.
You pay for the difference. The estimate was only ever a guess.
The vendor reopens scope to recover margin, or cuts corners to hold it.
The price stays fixed. The overrun is ours to carry, and you see the budget position at every demo.
It is billed on the calendar anyway. The status report says "on track".
The invoice follows the date in the contract, whether or not the work is done.
Payment for that milestone is held until it passes the written criteria. You do not fund a miss.
One price, built in the open.
The proposal itemises every feature or milestone, names its assumptions, and keeps a risk register with allowances. Allowances you never use are not billed.
- Number of workloads and environments
- Security and network policy depth
- Migration versus greenfield
- Observability and runbook scope
Cloud account and landing-zone access are provided on Day 1.
If falseDates move by the delay. The price does not change.
Workloads in scope are the ones listed in the SOW.
If falseEach additional workload is priced and approved before migration.
Your security team reviews on the cadence agreed.
If falseReview delays shift dates, never the fixed price.
- Cloud hosting
- Third-party licences
- Model and API usage
Invoiced in USD for international clients, with the currency fixed in the SOW. You own it. Code, models, documents, and credentials transfer on payment.
Built on a production-grade stack.
We pick tools proven in production and maintainable over long lifecycles - not whatever is trending. These are a few we reach for here; explore the full stack behind our engineering.
Explore our technology stack →
Articles on AI Platform Engineering
All articles→Designing for Failure: Timeouts, Retries, Queues and Recovery in Production Systems
Design production systems for partial failure with bounded retries, timeouts, idempotency, queue monitoring, reconciliation and recovery.
Read article →Private LLM, Enterprise API or On-Premises Model? Choosing the Right Deployment Architecture
Compare managed model APIs, private cloud and on-premises LLM deployment across data controls, quality, latency, operating cost and team capability.
Read article →Your Cloud Bill Is Rising: Cost Problem or Architecture Problem?
Diagnose rising cloud spend by linking cost to workload, utilisation, data transfer, reliability and unit economics before making changes.
Read article →What buyers ask before they commit.
Straight answers on timelines, price, risk, and ownership, the same terms that appear in every proposal.
01What is AI Platform Engineering?
AI platform engineering, cloud architecture, DevOps, MLOps, and platform infrastructure built to operate AI systems reliably at scale. On-premises, cloud, and hybrid deployments.
02How quickly does AI Platform Engineering deliver a first result?
A deployable, observable environment in 2 weeks. Typical milestones are Day 3: target architecture and landing zone agreed; Day 14: infrastructure as code with CI/CD and monitoring live; Day 30: first workloads migrated with runbooks handed to your team. Exact dates are written into the statement of work before you commit.
03How is AI Platform Engineering priced?
Fixed-scope project: one price per milestone, with a first-release date, itemised in a statement of work within five working days of scoping. Payment is released as each milestone is accepted against written criteria. Cloud hosting, licences, and model usage are passed through at cost. International clients are invoiced in USD. The main price drivers are number of workloads and environments, security and network policy depth, migration versus greenfield, observability and runbook scope.
04What happens if the scope changes or the work overruns?
The price stays fixed. A change to scope is quoted in writing, with its cost and date impact, and starts only after you approve it. If our own estimate was wrong, the overrun is ours, not yours. You see the budget position against milestones at every demo.
05Who owns the code, models, and documentation?
You do. Code, models, and documentation transfer to you on successful handover. Code, architecture documents, deployment scripts, and credentials transfer on payment, with no licence back.
06How are security and compliance handled?
Network policy, least-privilege access, secrets management, and vulnerability scanning ship with the first pipeline.
07Do you work with clients outside India?
Yes. We are based in Noida and work with clients around the world, delivering remotely. Calls and demos are scheduled in your time zone, proposals and contracts are in English, international clients are invoiced in USD, and every engagement follows the same fixed-scope terms regardless of where you are.
08When would you decline to fix a price?
If the scope is not yet defined, we do not guess a number. We propose a short, time-boxed discovery with its own fixed price, then quote the build.
Platform engineering that your team can run.
Whether you need a cloud migration with structured runbooks, an MLOps platform to operationalise your AI investments, or a DevOps pipeline your engineering team can own after handover, the starting point is understanding your operational requirements and data constraints.