Moat capability

Custom LLM Development

Build domain-aware language model systems with retrieval, tools, evaluation, and guardrails tailored to your business context.

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Problems addressed

Generic AI gives generic answers and misses your process, terminology, and risk tolerance.
Your knowledge lives across documents, systems, and teams that are hard to search.
You need a model workflow with quality controls before it touches customers.

Deliverables

LLM application architecture, retrieval design, prompts, tools, and model routing.
Fine-tuning or adaptation guidance when it is actually justified.
Evaluation suites, human review workflows, cost controls, and production monitoring.

Development approach

Product clarity before technical noise.

We avoid training for its own sake and first test whether retrieval, tools, or workflow design solve the problem.

We build measurable behavior into the product so quality improves instead of drifting.

We design fallbacks for uncertain outputs and high-risk actions.

Relevant technologies

OpenAI, function calling, RAG, embeddings, Postgres, pgvector, LangGraph-style orchestration, evals.

Questions businesses ask.

Clear answers before scope, architecture, and budget decisions.

Do I need to fine-tune a model?

Sometimes, but not always. Many business systems perform better with strong retrieval, careful prompting, tool use, and evaluations before fine-tuning is considered.

Can you connect private company knowledge?

Yes. We design retrieval systems around permission boundaries, source traceability, freshness, and the real structure of your internal content.

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