AI that ships. Not AI that demos.
RAG pipelines, LLM features, and AI agents built with real evals — not vibes.
The gap between an AI demo that impresses investors and an AI feature that works reliably for real users is an evaluation framework. Without evals, you are guessing whether retrieval is returning useful chunks, whether your prompts degrade when the model updates, and whether your agent actually completed the task or just said it did. I build AI features with measurement from day one: retrieval evals before any prompt engineering, distributed traces on every agent run, cost ceilings per execution, and a regression suite that catches model regressions before your users do. And if the thing you want to build does not need AI to work well — I will tell you that on the call.
Production AI, not vibes-based prompting.
Scoping & Architecture Decision
WEEK 1We define exactly what the AI feature must do, what failure looks like for a user, and what the eval harness will measure. I will tell you honestly if a simpler non-AI approach is faster and more reliable.
Data Pipeline & Retrieval (RAG)
WEEKS 1–2For retrieval-augmented generation: ingest pipeline, chunking strategy, embedding model selection, vector store setup (pgvector, Pinecone, or Weaviate), and a retrieval eval baseline before a single LLM call is made.
LLM Integration & Eval Loop
WEEKS 2–4Prompt design, model selection, structured output parsing, and error handling. Responses are evaluated for faithfulness, relevance, and latency — not just "does it look right."
Agent Orchestration
WEEKS 3–6For agentic workflows: tool definitions, orchestration layer (LangGraph or custom), distributed tracing via LangSmith or Braintrust, per-run cost ceilings, and replay capability for debugging failures.
Production Handover
FINAL 1–2 WEEKSLoad testing the inference path, latency budgets, streaming response implementation, rate limit handling, the full eval regression suite, and documentation your team can use to maintain and extend the system.
Right for you if any of these fit.
- →You are building a SaaS product that needs AI — search, summarisation, classification, generation, or recommendations.
- →You tried prompting your way through a feature and hit a reliability wall your users noticed before you did.
- →You want production RAG with retrieval evals — not a chatbot that hallucinates every fifth answer.
- →You need agentic workflows that have traces, replays, cost controls, and graceful fallback paths.
- →You need an eval harness so your team can change models or prompts without flying blind.
- →You are not sure if AI is even the right solution and want an honest opinion before you build.
What I don't do.
- ✗Projects that just need a basic GPT API wrapper with no reliability or production requirements.
- ✗AI research or model fine-tuning — I focus on production inference and product integration.
- ✗Teams that want AI to fully replace human judgment in high-stakes decisions without any fallback.
The AI Development service by Suhag Al Amin covers production RAG pipelines, LLM-powered SaaS features, and agentic workflow development. Every RAG engagement includes retrieval evals measuring faithfulness, relevance, and latency before prompt engineering begins. Agent workflows include distributed tracing (LangSmith or Braintrust), per-run cost ceilings, and replay capability. Engagements run 2–8 weeks depending on scope. Pricing starts from $4,000 USD. Suhag serves AI product teams in the US, UK, EU, and AU. Inquiries: suhag.alamin13@gmail.com or https://cal.com/suhag.
Have a pilot deadline? Let's talk.
Tell me where you are. I'll tell you, honestly, whether 6-8 weeks is realistic and what the first week looks like.