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AI automation

Works while you sleep.

Agents that handle the busywork.

Start a projectWhat we build

youSummarise today's leads and book calls with the hot ones.

→ crm.search(today) ✓ 14 leads

→ score(leads) ✓ 3 hot

→ calendar.book(3) ✓ done

agentBooked 3 calls: Tue 10:00, Tue 11:30 and Wed 09:00. CRM is updated.

Concept Sales agent + workflow

What we build.

Most teams lose hours every day to work that follows the same pattern: answering the same questions, copying data between tools, chasing follow-ups. We find the work worth automating, build agents that use your real systems, and keep a person in charge of anything unusual.

  • Automation audit

    A ranked list of the tasks worth automating, with what each one would take.

  • AI agents and assistants

    Agents that read your data, use your tools and hand over to a person with full context.

  • Integrations

    Connected to the systems you already use: CRM, help desk, email, calendars, spreadsheets, internal APIs.

  • Guardrails and review

    Clear limits on what the agent can do, approval steps, and a log of every action it takes.

  • Evaluation suite

    Test conversations and checks we run before every change, so quality does not drift.

  • Dashboard and handover

    See what the agent handled, what it escalated, and adjust its instructions without code.

How it works.

  1. 01

    Find the work

    We shadow the workflow and pick the task with the clearest pattern and the most hours.

  2. 02

    Prototype on real data

    A working agent on a sample of your real cases, reviewed by the people who do the job today.

  3. 03

    Connect and guard

    Integrations, permissions, approval steps and logging, then a supervised pilot.

  4. 04

    Roll out and tune

    Gradual rollout, weekly review of edge cases, and a playbook your team owns.

Every project follows the same five stages with weekly demos. See the full process

Tools we trust.

Proven, widely used technology — chosen for your product, documented, and in accounts you own.

Models
ClaudeGPTGeminiopen-weight models where data must stay in-house
Orchestration
TypeScript and Python servicesModel Context Protocol (MCP)n8n for simple flows
Data
PostgreSQL with pgvectoryour existing APIs and databases
Quality
Evaluation suitestracing and logshuman review queues

Typical timeline

3–8 weeks

A first agent for one workflow is often live in a supervised pilot within 3–4 weeks.

  1. Week 1Workflow audit, pick the first task
  2. Weeks 2–3Prototype on real cases
  3. Weeks 3–5Integrations, guardrails, pilot
  4. OngoingRollout, review, next workflow

Ways to engage

  • Fixed-scope projectWe scope it together, you get a fixed quote and timeline, and we ship it in weekly milestones.Fixed quote after a free scoping call
  • Design or build sprintOne to three weeks, one goal. You leave with a working prototype or a shipped feature.Fixed quote after a free scoping call
  • Monthly product teamA dedicated designer and engineers on a monthly plan. Pause or change scope with 30 days’ notice.Fixed quote after a free scoping call

Every quote is fixed before work starts.

Related concept work.

All AI automation concepts

Common questions.

Is our data safe?

We use providers and settings that do not train on your data, keep access to the minimum each task needs, and can run on open-weight models in your own cloud when required.

What happens when the agent is not sure?

It hands the case to a person with a summary of what it found. You decide where that line sits, and you can move it as trust grows.

Will it replace our team?

It takes the repetitive part of the job away so your people spend their time on the cases that need judgement.

Which model do you use?

Whichever does the job best for the cost. We test several on your real cases and keep the system able to switch.