Answers with a source
Each response cites the document and section behind it, so a reviewer verifies in one click rather than trusting a confident tone.
AI Core — Where we lead
Custom LLM & RAG Systems
An assistant that answers from your own documents, cites the source, and admits when the answer is not there.
- Vector search and hybrid retrieval
- Fine-tuning and model distillation
- On-prem and VPC deployments
- Hallucination testing and citation tracing
- Engagement shape
- Corpus assessment, then a fixed-scope build with an evaluation gate before rollout.
- Typical duration
- Four to ten weeks, depending on the corpus and the deployment target.
- Starting point
- A document set and twenty questions your team wishes it could answer instantly.
Overview
What the work involves.
Private assistants fail at retrieval far more often than at generation. If the right passage never reaches the model, no amount of prompting rescues the answer. So the effort goes into parsing, chunking, hybrid search and reranking, and the model is treated as the last step rather than the product.
Every answer carries citations back to a document and a section. Where the retrieved context does not support an answer, the system says so instead of filling the gap. That behaviour is tested rather than hoped for — the evaluation set deliberately includes questions the corpus cannot answer.
Deployment follows your policy, not ours. Managed APIs with zero retention, your own cloud account, an isolated VPC, or open-weight models on hardware you control. The retrieval layer behaves identically in each case.
Outcomes
What you get.
A measured hallucination rate
A held-out question set scored on every change, including questions whose correct answer is that the corpus does not cover it.
Deployment inside your boundary
Self-hosted, VPC or zero-retention API, chosen to match your policy, with the same retrieval behaviour in each.
A corpus that stays current
Ingestion runs on a schedule with change detection, so a retired policy document leaves the index instead of haunting it for two years.
Deliverables
What lands in your hands.
- Ingestion pipeline with parsing, chunking and change detection
- Hybrid vector and keyword retrieval with reranking
- Citation and source-tracing layer
- Evaluation set covering answerable and unanswerable questions
- Access control mapped onto your existing permissions
- Fine-tuned or distilled model where the case justifies it
- Deployment into your cloud, VPC or on-premise hardware
Approach
How we run it.
Read the corpus
We inspect what you actually hold: formats, duplication, versions, scanned pages, and the documents that contradict each other. Retrieval quality is decided here, before any code.
Build retrieval, then test retrieval
Chunking and search strategies are compared on recall against a labelled question set before a model is asked to write a single answer.
Add generation and citations
Prompting, answer shape and refusal behaviour, scored against the evaluation set for accuracy, grounding and how often it correctly declines.
Deploy where policy allows
Access control mapped to your existing groups, so a user can only retrieve what they could already open, and the audit log shows who asked what.
Each phase ends with something you can read and act on. If the evidence says stop, stopping there is a supported outcome rather than an awkward conversation.
Stack
What we build it with.
- Anthropic Claude
- Llama
- pgvector
- Qdrant
- LlamaIndex
- Cohere Rerank
- vLLM
- Ragas
Tooling is a decision we make per project, against your constraints and whatever your team already runs. Nothing on this list is a requirement, and we will work inside your existing stack where it holds up.
Questions
What clients ask about Custom LLM & RAG Systems.
Yes. Open-weight models served with vLLM on your GPUs, an embedding model alongside them, and the vector index inside your own database. No request leaves your network.
Retrieval scores gate generation, the prompt requires citations, and the evaluation set includes questions the corpus cannot answer. Abstention rate is reported next to accuracy, not hidden behind it.
Rarely at the start. Better retrieval fixes most accuracy problems more cheaply. Fine-tuning earns its place for tone, structured output or cutting inference cost once behaviour has settled.
Ready to scope Custom LLM & RAG Systems?
Tell us where the work sits today and what is holding it up. We will come back with the shape of a first phase, what it would prove, and what running it takes.
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