Agents that ship work, not just answers.
For teams buried in repetitive, multi-step tasks. We build AI helpers that actually do the work — using your tools, checking with a human at the right moments, and logging everything. Not demos that break the first time something unexpected happens.
An AI agent isn't a chatbot with extra steps. It's a worker — so plan for its bad days first.
Most AI agent demos look impressive until something unexpected happens — and something unexpected always happens. We start with the what-ifs: what can go wrong, when should a human approve first, and how do you check afterwards exactly what the AI did.
Every agent we build has clear limits: which tools it may use and when, a spending cap per task, sensible retries when something fails, and a full log you can replay. These aren't extras — they're the difference between a reliable helper and a liability.
We build with several proven AI frameworks and pick the one that's easiest to inspect and fix when something goes wrong — not the one with the best marketing.
What we build.
Research assistants
AI that browses, gathers, and summarizes — turning hours of reading into tidy reports for competitor research, deal checks, and market research.
Support ticket sorting
Reads incoming tickets, works out what each one is about, drafts a reply from your knowledge base, and passes the unclear ones to your team.
Paperwork processing
Pulls the important details out of invoices, contracts, forms, and records — checks them, files them in the right place, and flags anything it's unsure about for a human.
Back-office automation
Multi-step jobs that move between your customer system, accounting tools, data, and chat apps — kicked off by an event, a schedule, or a click.
Coding helpers
AI that reads your codebase, writes tests, fixes small issues, reviews changes against your standards, and plans bigger pieces of work.
Sales assistants
Researches prospects, personalizes outreach, follows up on schedule, logs everything in your customer system, and tells your sales team who's warming up.
Where agents deliver the most leverage.
Financial services
Watching transactions, sorting alerts, building reports, and drafting client messages.
How we build production agents.
Task audit
We map the exact job the AI will own — every input, every step, every decision. We agree what success looks like, in numbers, before writing code.
Rules & permissions
We decide what the AI may do on its own and what needs a human's OK first — and write it all down before we build.
Build & test
We build the agent and run it through a big test set: normal cases, weird cases, and deliberate attempts to trip it up. We measure what each run costs, too.
Practice run
The agent works alongside your team, but a human reviews every action before it counts. Two weeks of this catches the surprises safely, before going live.
Go live & improve
We switch it on with spending caps and an off switch. Then we improve it weekly based on what the logs and your reviewers catch.
Tools we use.
Engagement models.
Proof of Value
from $22k
One agent, one job. Two weeks practicing with human review, then two weeks running for real.
- One workflow automated
- Up to 8 tool connections
- A full record of every action
- 30-day post-launch support
Production Suite
from $55k
A team of agents covering 3–5 jobs, sharing what they know, with spending caps and a live dashboard.
- 3–5 automated workflows
- Agents share context and memory
- Cost and speed dashboards
- 90-day post-launch tuning
Enterprise Platform
custom
The full setup for companies running AI agents at scale, across whole departments.
- Unlimited workflow agents
- Access controls and audit records
- Runs privately on your own systems
- Ongoing retainer
Frequently asked.
5 questions answered. Still have one? Reach out.
For clearly defined, repetitive jobs with good testing: very reliable. For vague, open-ended tasks: less so. We only take on work where the reliability bar can actually be met — and we add human check-ins for everything else.