Custom LLM and RAG Integrations
Every business has knowledge locked in folders, PDFs, ticketing systems, and one person's head. RAG (Retrieval-Augmented Generation) lets an AI agent search and answer from that knowledge with citation. We build, secure, and tune those systems for you.
The problem you are facing
Generic AI does not know your contracts, your pricing exceptions, your past tickets, or your training manuals. So your team still answers the same questions, looks up the same documents, and reinvents the same answers, every week.
What we build for you
- Document ingestion pipeline: PDFs, Docs, Drive, SharePoint, Notion, Confluence, tickets
- Vector database: pgvector, Pinecone, or Weaviate depending on your stack
- Retrieval strategy: hybrid search (semantic plus keyword), re-ranking, citation
- Answer agent: answers with quotes and source links, never hallucinates the source
- Access control: who can query what, redact sensitive fields on the fly
- Eval harness: measurable accuracy, not vibes
- Private or on-prem option: self-hosted models for data that can never leave your network
How it works
- 1
Source inventory
What knowledge exists, where, and who owns it. We catalog before we ingest.
- 2
Pipeline and chunking
Document parsing, semantic chunking, metadata tagging. Bad chunks make bad answers.
- 3
Retrieval and re-rank
We tune retrieval for your data, and verify with an eval set of real questions.
- 4
Agent and guardrails
The answer agent always cites sources, refuses to answer outside scope, and escalates to humans when confidence is low.
- 5
Deploy and monitor
Production rollout with logging, evals, and a feedback loop.
Outcomes you can expect
- Your team finds answers in seconds, with sources
- New hires ramp in days because the knowledge is searchable
- Customer-facing chatbots stop hallucinating
- Sensitive data stays where it should, under your control
- Knowledge stops being a single point of failure
If your business runs on knowledge, RAG is usually the highest-leverage first AI engagement. It pays back fast and unlocks every later capability.
Frequently asked questions
Can you keep all our data on-prem?
Yes. We deploy open-weight models (Llama, Mistral, Qwen) on hardware you control, with no outbound network access. We have done this for legal, healthcare, and finance clients.
How big can the knowledge base be?
We have shipped systems on hundreds of thousands of documents. Above that, we shard and use hierarchical retrieval.
How accurate is RAG?
With a tuned pipeline and good source content, 92 to 98 percent on well-formed questions. We measure and report continuously.
What about updates?
Incremental. When source documents change, only the affected chunks re-index. No retraining required.
Is this different from fine-tuning?
Yes. RAG is better for facts (your data, ground truth, citations). Fine-tuning is better for style and voice. We sometimes combine them.
Ready to get started?
Order this service through our contact form and our team will be in touch within one business day. Prefer a quick call first? Book one for free.