RAG Knowledge Assistant
Staff spent significant time searching policy documents, contracts and historical records spread across several systems — and answers varied depending on who was asked.
- Project type
- Retrieval-augmented AI
- Industry
- Knowledge-intensive services
- Domain
- AI
- Delivery
- Ingestion and retrieval, then evaluation tooling
Technologies
- Python
- FastAPI
- Vector database
- LLM APIs
- PostgreSQL
- React
- Retrieval
- Hybrid
- Answers
- Cited
- Access control
- Role-scoped
These describe scope and architecture. We do not publish performance figures we cannot substantiate.
Business problem
Staff spent significant time searching policy documents, contracts and historical records spread across several systems — and answers varied depending on who was asked.
Our approach
We built an ingestion pipeline that chunks documents along semantic boundaries and a retrieval layer that combines keyword and vector search, filtered by the requesting user's permissions. Every answer carries citations so it can be verified in seconds.
Result
Staff get cited answers in plain language, scoped to what they are allowed to see. Because retrieval quality is scored against a maintained evaluation set, changes can be shipped without guessing whether they helped.
Key features
The capabilities that mattered most to the outcome.
- Hybrid semantic and keyword retrieval
- Citations linking every claim to a source passage
- Permission-aware retrieval scoped to user role
- Document ingestion pipeline with change detection
- Evaluation harness scoring answers against a gold set
Technologies used
Chosen for the requirement rather than by habit — we explain the trade-offs before building.
Your project would not necessarily use the same stack. We choose per requirement, and we will explain the reasoning before anything is committed.
Need something like this?
Tell us how your situation compares. We will outline what would carry over, what would differ, and what it would take to deliver.
Related work
Other systems in the same domain.
AI
Conversational AI
AI Chatbot
Customer support
- Business problem
- A large share of support volume consisted of the same handful of questions, but a generic chatbot had previously damaged trust by confidently inventing answers that were wrong.
- Result
- Routine questions are resolved without an agent while genuinely novel ones reach a person quickly with the conversation attached. Gaps in documentation surface as data instead of as complaints.
Key features
- Answers grounded in approved company content
- Clean escalation to human agents with full context
- Web, in-app and WhatsApp channels from one core
- Python
- FastAPI
- LLM APIs
- PostgreSQL
- Next.js
- +1 more
AI
Agentic AI
AI Agent Platform
Operations automation
- Business problem
- Multi-step operational processes required a person to move information between systems and apply judgement at each hop. Simple scripted automation broke on the exceptions, which were common.
- Result
- Routine paths complete without human involvement while exceptions surface with a full trace of what the system tried. Because every run is observable, failures are diagnosable rather than mysterious.
Key features
- Typed tool definitions with schema validation
- Planner, worker and reviewer agent separation
- Step limits, timeouts and per-run cost ceilings
- Python
- FastAPI
- LLM APIs
- Redis
- PostgreSQL
- +1 more
Have a Project in Mind? Let’s Build It.
Tell us what your business needs. We scope the solution, recommend the right architecture and give you a clear plan — design, build, deploy, maintain and grow.