Your AI prototype works. Production is a different system.
We are an architecture-first engineering studio. We take AI systems from demo to production on AWS — designed for cost, secured by default, specified before it is written.
Reference production topology, six layers: edge (CloudFront and WAF); compute (ECS Fargate and Lambda); inference, highlighted (Bedrock and self-hosted vLLM on GPU); state (Aurora with pgvector); guardrail (eval gate and cost budget); and observability (OpenTelemetry traces and token spend).
WAF
Lambda
vLLM · GPU
pgvector
Cost budget
Token spend
A demo proves the idea. It doesn’t survive Monday.
The notebook that impressed the board has no identity model, no cost ceiling, no rollback, no evals, and one engineer who understands it. None of that is a coding problem. It’s an architecture problem, and it compounds every week it stays unsolved.
Closing that gap is the only thing we do.
Comparison. A prototype is one laptop, keys in a dotenv file, unknown cost, no rollback, evals by vibes, and one owner. Production is multi-AZ and defined in infrastructure as code, scoped and rotated IAM credentials, a budget with a per-call ceiling, blue/green deploys with revert, an eval gate in CI, and a runbook with on-call.
Four commitments you can hold us to.
Not values. Constraints we accept before writing a line.
Architecture first
We draw the system, name the failure modes, and agree the boundaries before anyone opens an editor. Diagrams are cheap; rewrites are not.
Spec-driven
Every non-obvious decision is written down with the alternatives we rejected. Your team inherits the reasoning, not just the repository.
Cost-aware
Inference and infrastructure cost are design inputs, not a surprise on the invoice. Budgets and per-call ceilings ship with the feature.
Secure by default
Least-privilege IAM, rotated secrets, and explicit data boundaries are the starting position — never a hardening phase scheduled for later.
Four stages. Named deliverables at every one.
You can stop after any stage and keep everything produced in it. Consulting, audits, and platform work are all entry points into the same sequence.
Where does this break at 100×?
We read the code, the infrastructure, and the bill. You get a written picture of what will fail first, what it will cost, and how large the blast radius is when it does.
Systems in production.
Client names withheld under NDA. Architecture described in full on request.
Open source: Blueprint Validator and internal platform tooling are public on GitHub.
Founder-led. Senior only. No bench.
The people who scope the work are the people who write it. There is no layer between you and the engineer holding the pager.
Oren Agent — guardrails for coding agents.
A GitHub App and editor plugin that catch cloud cost, token cost, and destructive actions before they land. Built because we needed it on our own engagements.
See Oren AgentTell us what has to reach production.
Thirty minutes on your system, your timeline, and what is currently blocking you.
Let’s Productionize TogetherA senior engineer takes the call — the same one who would do the work.
Prefer email? hi@jawadzaidi.com