AI Development
Assistants, agents and LLMs, deployed responsibly.
Build LLM-powered tools that connect to your knowledge, execute workflows, and reduce repeat work across teams.
- 01 · Scope
Find the question worth answering. We start from the work being repeated, not from the technology.
- 02 · Prove
Build the thin slice. One use case, end to end, on your real documents.
- 03 · Harden
Add what production needs. Permissions, evaluation, guardrails, logging, monitoring.
- 04 · Hand over
Leave it owned. Documentation and a working session with the team that runs it.
How it works
From documents to a cited answer.
The model is one step of five. The other four decide whether the answer is correct.
- 01
Documents
It starts with what you already have: contracts, reports, tickets, wherever they live.
- 02
Passages
Each document is cut into short passages, so an answer can point at a paragraph rather than a file.
- 03
Question
A question comes in, and the system finds the few passages that actually address it.
- 04
Model
Only those passages go to the model. It answers from them, not from what it happens to remember.
- 05
Answer
The answer comes back with a link to the passage it came from, so anyone can check it.
What we deliver
Systems built for daily use, not prototypes for a demo.
Assistants and agents
Document AI
LLM automations
Fine-tuning and LLMOps
MCP server development
Enterprise-grade by default
What comes with every system we hand over, by default.
Governance
Who owns the system, who may change it, and how changes are approved.
Auditability
Every answer traces back to its sources, every action to a log entry.
Security
Data stays where it should; keys and access follow your policy.
Monitoring
Quality, cost and errors watched in production, with alerts.
Production standards
Tests, CI/CD and documentation, as for any software you run.
Evaluation sets
Real questions, fixed, to show whether each change makes answers better or worse.
Permission-aware retrieval
The assistant only finds what the person asking is allowed to see.
Next service · ML Engineering
Predictive models built for decision-making.
Build forecasting and optimization systems that support operations, pricing, and planning, with measurable performance.
Open the serviceContact
Have a pile of documents that should answer questions?
Tell us what your team keeps looking up, and we will tell you what it would take.
Get in touch