Prototype to production AI sprint

When you need this

• The demo works. Real users keep finding the failure modes, and each fix is a prompt tweak nobody can verify.
• Token spend grows faster than usage, and there is no accounting that says which feature, user or model is responsible.
• There is no evaluation, so every prompt change is a gamble and the team ships on gut feeling.
• Nothing is observable: when a request fails, the only artifact is a screenshot from a customer.
• The agent loop was written in a notebook. State lives in memory, retries are ad hoc, and one crash loses the conversation.
• You have a launch date and a prototype, and the gap between them is engineering, not research.

What you get

• Architecture: the target design for your system, covering request flow, state, retrieval, tools, caches and failure handling, agreed in writing before implementation starts.
• Agent orchestration: the agent loop rebuilt on LangGraph with explicit state, checkpoints, retries, timeouts and resumable tool turns, so a crash or a provider error does not lose the user's work.
• RAG / GraphRAG: the retrieval layer your use case actually needs, vector RAG or Neo4j GraphRAG, with reranking and source-cited generation where faithfulness matters.
• Evaluation: RAGAS, LLM-as-a-Judge and grounding checks wired into a test suite that acts as the acceptance criterion for the sprint and for every later change.
• Observability: per-request tracing across model calls, retrieval and tools, with latency, token and cost attribution, so incidents are diagnosed from data.
• Semantic caching: a cache in front of the model that serves repeated or near-repeated requests without a new call, with hit accounting so you can see what it saves.
• Model routing: complexity-based routing that sends each request to the cheapest model that meets the quality bar, with the routing decision recorded and explainable.
• Security review: prompt-injection surfaces, tool permissions and consent for sensitive actions, secrets handling, input validation and per-user quotas, reviewed and fixed where they are in scope.
• Deployment: Docker images, configuration and the deployment procedure for your infrastructure, run end to end at least once with your team watching.
• Runbook: how to operate the system, what to check when something degrades, how to roll back, and how to change prompts, models and retrieval without breaking the tests.

Outcomes

• A production architecture with explicit state, retries and tool contracts, written down and reviewed with your team before it is built.
• Evaluation as code: a test suite that serves as the acceptance criterion for the sprint and for every change after it.
• Observability that traces each request across model calls, retrieval and tools, so the next incident has a root cause instead of a guess.
• A cost layer, per-token accounting, semantic caching and complexity-based model routing, so spend follows a policy instead of arriving as a surprise.
• A deployed system with a security review, a runbook and documentation, handed over so your team runs it without me.

How it works

1. 1

   Technical call

   A 20-minute technical call. You show me the prototype, tell me what production means for it, and describe the failures you already know about. I ask about traffic, models, data, the team who will operate it, and any launch date.

2. 2

   Written diagnosis

   I read the prototype and write down what it does, what breaks, what production requires that it does not have, and which of the ten areas above are the real gap for your system. Some prototypes need all ten; some need four done properly.

3. 3

   Fixed-scope proposal

   Scope, timeline and price in writing, starting at $2,500 for 3–4 weeks. The proposal states what will be true at the end of the sprint, and wherever practical states it as tests, so acceptance is a passing suite rather than an opinion.

4. 4

   Sprint and handover

   Architecture first, then orchestration and retrieval, then the evaluation, observability and cost layers, then the security review and deployment. You see working increments through the sprint, not a reveal at the end. Handover is code, documentation, the runbook, and a session with the engineers who will operate it.

Proof: what I built

• OmidGPT is a multi-provider agentic AI platform I built and operate: 209 API endpoints, 50 database models, about 94K lines of Python and TypeScript, 391 commits. The cost and reliability layers this sprint installs are the ones running there: per-token cost accounting, semantic caching, complexity-based model routing, an agentic tool-calling runtime with resumable tool turns and a tool-call ledger, and production billing and quota systems.

  Read the OmidGPT case study

• Jozveh-AI is an eight-agent LangGraph pipeline over Neo4j GraphRAG that turns source material into cited study documents. Its evaluation layer, RAGAS, a custom claim-level LLM judge and post-generation grounding filters, runs as 673 tests across 77 files. That is the shape of the evaluation deliverable in this sprint: tests that fail when faithfulness drops.

  Read the Jozveh-AI case study

• AiMatin runs FastAPI and PostgreSQL behind a Next.js front end, with a custom Node.js MCP server through which an AI operator runs admin workflows in production. It is the smallest of the three systems and the one that shows the deployment and runbook side: a solo-operated platform that keeps running because the operations were engineered, not improvised.

  Read the AiMatin case study