Document Ingestion
Parsing and chunking pipelines for PDFs, docs, tickets, wikis, and websites — the foundation retrieval quality depends on.
RAG Development & Business Knowledge Search
Make business information easier to find. Retrieval-augmented generation, or RAG, combines document search with AI-generated answers. We build these systems around an approved knowledge collection, source references, and access requirements.
Discuss this serviceWhat the project can include
Embeddings & Vector Search
Embedding pipelines and vector search infrastructure that find the right passages, not just similar words.
Grounded Answers with Citations
Generate answers using retrieved passages, with source references where available and a fallback when the information is insufficient.
Knowledge-Base Chatbots
Support and internal assistants that answer from your documentation, policies, and FAQs.
Hybrid Retrieval & Reranking
Keyword + semantic retrieval and reranking where answer quality demands more than a basic pipeline.
Evaluation & Tuning
Retrieval and answer quality measured against real questions, so improvements are grounded in data.
Possible applications
Examples of where this service could fit. The final scope depends on your process, information and existing tools.
Customer support assistant answering from your help center
Internal Q&A over policies, contracts, and documentation
Searchable knowledge across years of documents and archives
Product-docs chat for technical users and developers
Onboarding assistants that know your processes
Delivery approach, process and technologies
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Our approach
Retrieval quality first
Review document quality, parsing, chunking and retrieval alongside prompts and answer evaluation.
Citations by default
Source references can help users check an answer against the retrieved passages. Missing or unsuitable sources need a fallback.
Your data stays yours
Hosting, data access, retention and any use by third-party providers are agreed during scoping. Review provider settings before using confidential information.
Project stages
Knowledge Audit
We map your sources, formats, and the questions the system must answer.
Ingestion & Index
Documents are parsed, chunked, embedded, and indexed for retrieval.
Pipeline & Evals
Retrieval and generation are built and measured against real questions.
Ship & Iterate
Production deployment with feedback loops that keep quality improving.
Technology options
Selected around your requirements and existing systems.
- OpenAI
- Anthropic Claude
- LangChain
- Vector search
- PostgreSQL
- Supabase
- Python
- FastAPI
- AWS
Common questions
Practical details to consider when planning this service for your business.
RAG retrieves relevant passages from an approved collection before generating an answer. Source references can help readers check the response, but retrieval and generation can still make mistakes.
RAG retrieves information at question time; fine-tuning changes model behaviour through training. The appropriate approach depends on the task, source material, evaluation and operating costs.
PDFs, Word documents, spreadsheets, web pages, help-center articles, tickets, and database records. Scanned documents go through OCR first — that's part of our document intelligence work.
We measure it rather than promise it: retrieval and answer quality are evaluated against a test set of real questions, answers carry citations, and low-confidence cases can fall back to a human. Accuracy depends on source quality and scope, and we'll tell you honestly what to expect.
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View all servicesWant answers grounded in your own knowledge?
Tell us about your requirements, existing tools, and priorities. We can then discuss the scope, timing, and cost.
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