Overview: GenAI Engineering Services in Saint-Michel

Beyond the demo, for Saint-Michel teams

I build the evaluation harness, guardrails and monitoring alongside the feature itself, so quality doesn't silently regress after launch. Anyone can wire up an API call — shipping something reliable, cost-aware and safe under real user load from Saint-Michel is a different job.

What's included

Prompt and pipeline architecture

Structured prompt design, function calling and multi-step orchestration built to hold up under real Saint-Michel user traffic, not just a clean demo.

Architecture components

• Function calling and tool-use design
• Multi-step agent orchestration where genuinely needed
• Structured output validation

RAG systems

Retrieval-augmented generation built on vector databases, grounding responses in your Saint-Michel business's actual data instead of relying on model memory alone.

RAG components

• Document ingestion and chunking strategy
• Vector database selection and indexing
• Retrieval quality evaluation

Evaluation suites and guardrails

Automated evals and safety guardrails so quality is measured continuously for your Saint-Michel deployment, not just checked once before launch.

Cost-aware model routing

Model routing and caching that keeps inference costs sane as usage scales for Saint-Michel businesses.

How the engagement runs

Process

Scoping & architecture

We define the exact use case, failure modes and success metrics before writing a single prompt for your Saint-Michel product.

Build & evaluation

The feature and its evaluation harness are built together, not bolted on afterward.

Launch & monitoring

Production monitoring catches quality regressions in your Saint-Michel deployment before users do.

Who this is for

Product teams shipping AI features

Saint-Michel teams that need an AI feature to actually hold up in production, not just impress in a demo.

Engineering teams needing a specialist

Saint-Michel businesses that want a GenAI specialist to plug into an existing team rather than own the whole build.

Tools & technology

Model providers

LLM platforms

OpenAI, Anthropic (Claude), and open-source models via Together or self-hosted

Chosen per use case for your Saint-Michel deployment — cost, latency and quality all factor in.

Note

Vector databases and orchestration tooling are selected to fit your Saint-Michel team's existing stack wherever possible.

Quick answer

GenAI engineering here means building the full production system around a language model — prompt architecture, RAG retrieval, evaluation harness and guardrails — not just wiring up an API call. For a Saint-Michel business, that's the difference between an AI feature that looks good in a demo and one that stays reliable, cost-aware and safe once real users in Saint-Michel are hitting it every day.

How this compares to a no-code AI wrapper tool

• No-code AI wrappers give your Saint-Michel business a generic prompt box; this builds structured pipelines with function calling, multi-step orchestration and validated outputs tuned to your actual use case.
• Wrapper tools rarely ground answers in your own data; RAG systems here index your Saint-Michel business's actual documents in a vector database so responses aren't relying on model memory alone.
• No-code tools have no evaluation layer; this ships automated evals and safety guardrails so quality for your Saint-Michel deployment is measured continuously, not just eyeballed once.
• Wrapper tools bill per generic call with no cost controls; model routing and caching here keeps inference costs sane as usage scales for Saint-Michel businesses.

Why Saint-Michel teams choose Deepak Suhag

Production LLM pipelines, RAG systems and evals that hold up past the demo — built by someone who ships and monitors, not just prototypes.