Architecture & Infrastructure
Overall system design, data flow, service boundaries, model orchestration, deployment, scalability assumptions, failure modes. Is this suitable for production or only for a demo?
Production Readiness Review · 2 weeks · From $15K
Is your AI system real, risky, or fixable?
A senior technical review of your existing AI architecture, retrieval quality, hallucination risk, infrastructure, and security — with a clear roadmap to production. Done by an architect who has shipped production ML at scale, not a junior consultant working from a checklist.
When this is for you
Most AI demos are not production systems.
Your prototype works in a controlled demo. Production is different. Real users ask messy questions. Retrieval fails silently. LLM costs grow every week. Compliance reviewers ask questions nobody prepared for. Investors want to know whether the architecture is real.
This review finds those risks early — before they become customer escalations, failed pilots, or painful rewrites.
- Your AI demo works, but you're not sure it'll scale.
- Your RAG system gives inconsistent or unverifiable answers.
- Your LLM bill is growing faster than usage.
- You're preparing for enterprise pilots or investor diligence.
- You inherited AI code from an agency or a rushed MVP.
- You need a senior technical opinion before committing to a rebuild.
What we review
Four modules across the full AI stack.
Not just prompts. We look at the system: data, retrieval, model behavior, infrastructure, cost, security, evaluation, and operational ownership.
Retrieval & Grounding
Ingestion, chunking, embeddings, vector search, metadata filters, reranking, observability, source attribution. Are answers grounded — or filled in by the model?
Cost & Latency
Model selection, prompt size, context bloat, caching, batching, retries, fallback routing, cost-per-workflow. Where money leaks. Where latency spikes hide.
Security, Privacy & Operations
Tenant isolation, secrets, logging, audit trails, PHI/PII handling, monitoring, incident workflows. Can your team debug a bad answer? Will this survive a security review?
What you get
A roadmap, not a vague assessment.
- Production readiness scorecardWhere your system is strong, weak, risky, or not ready.
- Risk registerPrioritized by severity and business impact.
- Architecture recommendationsConcrete, not abstract.
- RAG & cost findingsWhere retrieval is failing, where token spend is bloated.
- 30 / 60 / 90-day roadmapWhat to fix now, next, and later.
- Optional implementationWe can fix what we found, or hand off to your team.
Process
Fast, practical, no scope fog.
- Step 01Technical fit call30 minutes with Manmeet to understand your system, stage, and risk. We'll tell you honestly whether the review is useful.
- Step 02Artifact collectionWe agree on minimum needed: diagrams, walkthroughs, anonymized traces, prompts, outputs, cost data. No full codebase access required to start.
- Step 03Technical review7 to 10 days. Architecture, retrieval, evals, cost, security, compliance, operations. Architect-led — Manmeet does the work.
- Step 04Findings workshopWe walk your team through the findings, explain severity, separate urgent fixes from nice-to-haves.
- Step 05Roadmap or implementationYou receive the roadmap. If useful, we move into implementation on the highest-priority fixes.
$15K – $30K· 2 reviews / month · Architect-led delivery
How this differs
Production Readiness vs Architecture Sprint.
Both start with a 30-minute conversation. Both produce a build-ready output. The difference is which problem they solve.
Production Readiness Review
For teams with an existing AI system that needs to be evaluated. Deliverable: scorecard + risk register + roadmap to production.
Architecture Sprint
For teams designing a new AI system from scratch. Deliverable: build-ready architecture, data assessment, working POC. See How We Work →
Both sometimes work together. A readiness review on what exists, then an architecture sprint for the parts being rebuilt. We'll tell you which one fits on the call.