Real use cases
Automation with a number attached.
Every agent we ship has a measurable job. These are the patterns that keep proving themselves.
Customer support agent
Answers common questions instantly, drafts replies for the rest, and escalates what actually needs a human.
Invoice & data entry
Reads invoices, receipts, and forms — then files the data into your accounting system without retyping.
Lead qualification
Scores and enriches inbound leads, routes hot ones to sales in minutes instead of days.
Weekly reporting
Pulls numbers from your tools, writes the summary, and posts it before Monday standup.
Email & ticket triage
Sorts, tags, and prioritises every incoming message so your team opens the inbox to a plan, not a pile.
Document processing
Extracts structured data from contracts, CVs, and PDFs — searchable, validated, ready to use.
How we build
Boring process,
magic result.
AI projects fail on process, not models. Ours is deliberately unexciting: understand first, automate second, measure always.
Map the workflow
We sit with the people doing the work and chart every step, exception, and handoff. No automation before understanding.
Pick the right moments for AI
Not everything needs a model. We identify where AI genuinely earns its keep — and keep deterministic code for the rest.
Prototype in days
A working agent on real (sample) data within the first week. You judge results, not slide decks.
Pilot with a human in the loop
The agent drafts, a person approves. We tune prompts and edge cases until approval becomes a rubber stamp.
Deploy, monitor, improve
Logged, measured, and reviewed. You see exactly what the agent did, and the numbers it moved.
AI stack
The plumbing behind
the “magic”.
Hover to see each tool's job. Models get the headlines; retrieval, orchestration, and monitoring do the work.
Got a workflow that eats hours?
Describe it in two sentences. We'll tell you if AI can take it — and what the payback looks like.