RAG development that gives grounded, cited answers
Retrieval-augmented generation lets a language model answer from your knowledge — contracts, manuals, tickets, BIM models, clinical guidelines — instead of from memory. The hard part is not the demo; it is retrieval quality, permissions and knowing when the answer is wrong. That is the part we engineer.
What we deliver
Ingestion pipelines
Connectors for file shares, wikis, databases and APIs, with parsing for PDFs, tables and scans, sensible chunking and incremental re-indexing.
Hybrid search & reranking
Vector and keyword search combined, then reranked, so exact terms like part numbers and names are found as reliably as concepts.
Citations & guardrails
Every answer links to the passages it used. When the sources do not support an answer, the system says so instead of guessing.
Access control
Document permissions enforced at retrieval time, so people only get answers built from content they are allowed to read.
Evaluation
A test set of real questions scored on retrieval and answer quality, run on every change to prompts, models or chunking.
Chat, search & API surfaces
Delivered as an assistant, a search experience, a Slack or Teams bot, or an API your other systems call.
Typical use cases
- Internal knowledge assistants for policies, processes and wikis
- Customer support answers grounded in product documentation
- Contract, tender and compliance document review
- Natural-language questions over BIM and engineering data
- Clinical guideline and protocol lookup for care teams
Technology we use
OpenAI
Ollama
Python
PostgreSQL
Elasticsearch- Weaviate
How we work
- 01
Discover
We learn the business, the users and the constraints, and agree what success looks like.
- 02
Prove
We test the riskiest assumption first — a prototype or AI proof of concept — before scaling the team.
- 03
Build
Short iterations, working software every two weeks and demos you can click through.
- 04
Run
Launch, monitoring and continued development — or a clean handover to your team.
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Frequently asked questions
What is RAG development?
RAG development is building systems where a language model first retrieves relevant passages from your own data and then answers using them. It keeps answers current and traceable without retraining a model.
Which vector database do you use?
It depends on scale and your stack. PostgreSQL with pgvector covers many projects; Elasticsearch, OpenSearch or Weaviate suit larger or search-heavy ones. We pick what you can operate.
How do you stop hallucinations?
You reduce them rather than eliminate them: better retrieval, answers constrained to cited sources, refusal when evidence is missing, and an evaluation set that catches regressions before users do.
Can RAG respect our document permissions?
Yes. We carry permissions from the source systems into the index and filter at query time, so the model never sees content the user cannot access.
Further reading
RAG Development: Building Retrieval That Holds Up in Production
Retrieval-augmented generation is easy to demo and hard to run. Here is how we design RAG systems that stay accurate, secure and affordable once real users arrive.
5 min read
LLM Agents in Production: Tools, Guardrails and Evaluation
Agents that call tools and take actions are powerful and fragile. The engineering practices that keep them safe, measurable and affordable in production.
5 min read
AI in BIM: Practical Uses for Building Information Models
BIM models hold enormous amounts of structured data that teams rarely have time to use. Where AI genuinely helps, from model checking to asking a model questions in plain language.
5 min read
Let's talk about your project
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