Document AI & Knowledge Systems
Retrieval-Augmented Generation (RAG) Applications
AI applications that answer questions using your own documents as the source, citing where each answer came from, so responses are grounded in your content rather than general internet knowledge.
Overview
Why it matters
for your business.
General-purpose AI models know nothing about your products, contracts or internal procedures, and they may guess confidently. Retrieval-augmented generation fixes this by searching your documents first and giving the model only relevant passages to answer from. We build RAG applications such as policy assistants, technical manual helpers, regulatory lookup tools and contract Q&A systems.
Quality in RAG comes mostly from retrieval. We experiment with chunking, embeddings, hybrid keyword and vector search, and re-ranking on your actual content, then measure answer faithfulness and citation accuracy against a test set. Answers show their sources so users can verify. When the documents do not contain an answer, the system is designed to say so rather than improvise.
What's included
- Document ingestion and chunking pipeline
- Vector database with hybrid search and re-ranking
- Question-answering interface with source citations
- Evaluation set measuring retrieval and answer quality
- Access controls and document-level permissions
- Re-indexing process for updated content
Outcomes
What changes
for your business.
Ideal for
- Manufacturers with large technical manuals
- Compliance, legal and policy teams
- Support teams answering detailed product questions
Answers you can verify
Each response links to the exact passages used, so users check the source before acting.
Expertise on tap
Specialist knowledge in manuals and policies becomes accessible to anyone, not just a few experienced staff.
Current information
Updating documents updates the answers, with no model retraining required when content changes.
How we deliver
A clear, accountable
process.
Every engagement follows the same disciplined approach, adapted to the size and urgency of your project.
- 01
Identify
We find the use cases and processes where AI or automation clearly pays off, and estimate the impact.
- 02
Prototype
A proof of concept on your own data shows what works before you commit to a full build.
- 03
Deploy
We integrate with your systems, add guardrails and human review, and train your team.
- 04
Monitor & improve
Accuracy, cost and usage are tracked, and the solution is tuned as your data and needs change.
Ways to work with us
- Fixed-price projectsDefined scope, milestones and deliverables.
- Implementation & integrationOne-time technology deployment and integration.
- Time & materialFlexible delivery based on actual effort and resources.
- Monthly managed servicesRecurring application, IT, marketing or infrastructure support.
FAQ
Common
questions.
Something else on your mind? Ask us directly.
Can a RAG application still give wrong answers?
Yes. Retrieval may miss the right passage, or the model may misread it. We reduce this with careful retrieval tuning, instructions to answer only from sources, citations and evaluation. For high-stakes uses we recommend human review of answers before they are acted on.
What document formats can be used as sources?
PDFs, Word files, slides, spreadsheets, web pages, wiki pages and databases are all workable. Scanned documents need OCR first. Tables and diagrams need extra handling, so we review a sample of your content to plan ingestion properly.
Let's talk about Retrieval-Augmented Generation (RAG) Applications.
Tell us what you need and get a clear plan and proposal, free and with no obligation.