Internal knowledge assistant
Ask questions across policies, manuals and documents — with sources.
AI Applications & Intelligent Agents
Retrieval-augmented search over your private documents and multi-step agent pipelines that research, verify and summarise — with citations, guardrails and cost controls built in.
Typical stack
- Python
- LangChain
- LlamaIndex
- FastAPI
- Qdrant Vector DB
- OpenAI
- Docker
Overview
Practical AI built on your own data.
AI is most useful when it is grounded in your own documents and data, and when its answers can be checked. We build AI systems that cite their sources, respect access permissions and fit into existing workflows.
Every project starts with a narrowly defined use case and an evaluation set, so quality is measured rather than assumed — and costs are monitored from the first day.
Capabilities
What we deliver.
- 01 Multi-agent research and data-extraction pipelines
- 02 Private RAG search over your documents
- 03 Source citations and cross-checking guardrails
- 04 Prompt pipelines and model evaluation
- 05 Latency and token-cost optimisation
Use cases
Where this makes the biggest difference.
Document extraction
Pull structured data from invoices, contracts, reports and forms.
Research & briefing
Agents that gather, verify and summarise information on a schedule.
How we deliver
From discovery to production.
A transparent, milestone-based process with a working demo every two weeks.
- 01
Discovery & workflow audit
We sit with the people doing the work, map where time and data get lost, and turn that into a written scope you can hold us to.
- 02
Architecture blueprint
Data model, integrations, access control and stack choices — decided up front and explained in plain language.
- 03
Interface design
Clickable prototypes tested with your actual users, so the software fits the floor, the field and the front office.
- 04
Build in two-week sprints
Small, testable releases with a working demo at the end of every sprint — real progress you can see and try.
- 05
Testing & hardening
Automated end-to-end and unit tests, security review and role-based access checks before anything touches production.
- 06
Launch & ongoing care
Zero-downtime deployment, automated backups, a 30-day warranty, and optional monthly maintenance after that.
FAQ
Frequently asked questions.
Is our data used to train public AI models?
No. We use enterprise API terms that exclude training on your data, and can deploy open-source models on your own infrastructure when data must not leave it.
How do you prevent wrong answers?
Answers are grounded in retrieved sources and cited, low-confidence responses are flagged, and we test against an evaluation set built with your team before launch.