Custom LLM & RAG Chatbots

Retrieval-augmented systems that answer from your knowledge base, your products, your code — with citations, evals, and the engineering rigor to keep them accurate over time.

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Custom LLM & RAG

Chatbots grounded in your data — not the public internet.

A retrieval pipeline tuned to your data, an LLM that cites its sources, and an eval harness that keeps everyone honest.

Common signs your team is overdue for custom llm & rag:

  • Generic chatbots that confidently invent answers
  • No way to tell which document an answer came from
  • Knowledge bases that go stale within weeks of launch
  • No metric to tell whether a change made things better or worse

What we build for custom llm & rag:

  • Document ingestion with chunking strategies tuned per content type
  • Hybrid retrieval (semantic + keyword) with re-ranking
  • Citations and source links on every answer
  • Eval harness: golden Q&A set, regression scoring, leaderboard
  • Hallucination guards: refuse-to-answer when retrieval is weak
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Capabilities

Where RAG shines

Grounded retrieval, measured quality — outcomes our clients keep coming back for.

Internal copilots

Ask your codebase, runbooks, or wiki in natural language — with sources.

Support assistants

Customer-facing chat grounded in your help center, refunds policy, and product docs.

Research assistants

Browse a corpus of papers, patents, or specs and produce evidence-backed briefs.

Product assistants

Answer questions about your catalog, configurations, and compatibility.

How we deliver

RAG done right

01

Audit your data

Sample, profile, and clean the corpus. Decide what’s in scope vs. out.

02

Build evals

A golden Q&A set + scoring rubric. Every change is measured against this.

03

Tune retrieval

Chunking, embeddings, re-ranking — tuned to your content shape.

04

Operate

Refresh the index, watch the dashboards, evolve the eval set.

Tools & platforms we use:

OpenAI Anthropic LlamaIndex LangChain Pinecone pgvector Qdrant Cohere Postgres S3

FAQ

Questions teams ask us about Custom LLM & RAG

Can it work with our private/on-prem data?
Yes. We can deploy fully on your infrastructure with open-weight models (Llama, Mistral, Qwen) and self-hosted vector stores.
How do you stop hallucinations?
Strong retrieval + grounded prompting + refusal-when-uncertain + citation requirements + an eval harness scoring faithfulness.
How long does it take to get to production?
Most projects ship a real, usable system in 3–6 weeks. Discovery is 1–2 weeks; build sprints are weekly with demos.
Will my data be used to train models?
No. We default to enterprise tiers (OpenAI, Anthropic, Bedrock, Vertex) that don’t train on your data. For sensitive use cases, we deploy open-weight models on your infrastructure.
How do you control costs?
We design cost-aware from day one — model routing (cheap model first, escalate when needed), caching, batch processing, and per-user budgets with alerts.
Can you work with our existing engineering team?
Yes. We embed alongside your team, transfer ownership progressively, and document everything we build.