Custom AI agents that use your tools to do the work, and stop for a person before anything that can't be undone.
The nine kinds we build most often, and any other agent your work needs.
Routine tickets resolved end to end — the order looked up, the policy applied, the reply sent — and the rest handed to your team with what the agent found.
Every inbound lead qualified against your criteria, the company researched, the first email drafted and the meeting booked.
Orders, scheduling and data entry across your systems, done step by step and logged.
Invoices matched to orders, accounts reconciled and expenses checked, with anything over your limit sent for approval.
Contracts, claims and forms read, checked and turned into records, with the fields they pulled shown for review.
Password resets, access requests and policy questions handled, with access granted only after a person approves it.
Answers from your own documents and data with the sources shown, and research across them written up as a brief.
A coordinator that splits a job between specialist agents — research, drafting, review — and checks their work before it is done.
Agent features built into your own app or SaaS product, working on your users' data within their permissions.
Six steps, from the workflow it takes on to an agent your team can trust to work on its own.
One workflow picked and mapped, with the number the agent has to beat.
The agent's tools, permissions and guardrails agreed, with a prototype on your systems and a dated estimate.
Built and connected to your APIs, and scored against an evaluation set as it grows.
Run in shadow on real cases, with every failure reviewed before it acts on its own.
Live on real work, with monitoring and a human handoff from day one.
Re-tested when a model or tool changes and improved from the logs, or handed over to your team.
One workflow picked and mapped, with the number the agent has to beat.
The agent's tools, permissions and guardrails agreed, with a prototype on your systems and a dated estimate.
Built and connected to your APIs, and scored against an evaluation set as it grows.
Run in shadow on real cases, with every failure reviewed before it acts on its own.
Live on real work, with monitoring and a human handoff from day one.
Re-tested when a model or tool changes and improved from the logs, or handed over to your team.
Controls we build into an agent, chosen for what it may do.
Each tool gets its own access, set in the tool rather than in the prompt.
Actions you choose, such as refunds or deletions, wait for a person to approve them.
When it's unsure or missing a fact, the case goes to your team with what it found.
Changes to prompts, models or tools are tested on your real cases before release.
What it read, what it called and what came back, recorded for every case.
Quality, errors and cost watched in production, with an alert when they drift.
Text in emails and documents is read as data, never as instructions, and personal details can be masked.
A ceiling on steps, time and cost for each case, so a loop can't run up a bill.
The model picked for quality, cost and speed on each task, with a fallback.
Starting something new with AI, or adding it to software you already have.
An AI MVP with the features that matter most, to launch and learn.
An AI product designed, built and launched in full, by one team.
AI engineers join your team, or a dedicated team runs the work.
AI built into the systems you already run, working with your data and permissions.
A demo or proof of concept made reliable, secure and measured, ready for real users.
Hosted and monitored, with models evaluated and updated as your data changes, agreed with you.
Custom software development across industries: web, mobile and AI applications, built and security-audited by one team.
We also build software for insurance (insurtech), government (govtech), energy and media. If your industry is not listed, we can still build for it.
Three products of our own, designed, built and kept running by the team that would build your agent.
Retail · SaaS
A point-of-sale system that keeps sales, stock and receipts together on till, tablet and phone.
Travel
A travel booking app whose AI assistant finds flights, hotels and trip ideas, through to a confirmed booking.
AI
An AI assistant for customer support and sales that answers questions and guides buyers.
What teams ask before they hand work to an agent, answered plainly.
A chatbot answers, and a workflow follows the same fixed steps every time. An agent is given a goal and a set of tools, decides which steps to take and takes them — looking up an order, updating a record, sending a reply — within limits you set. Where the steps never change, a workflow is cheaper, and we'll say so.
You decide, action by action. Reading and drafting usually run on their own; anything that moves money, deletes data, changes access or contacts many people waits for a person's approval, with the agent's reasoning beside it. The limits live in the tools themselves, not only in the prompt.
Through your APIs, as tools the agent may call, each with its own permissions — or through MCP servers we build, so every agent you run can use the same tools. Where a system has no API, an agent can work its web interface, but we use that last.
Before we build, we collect real cases from your history with the right outcome for each, and agree the score the agent has to reach. Every change is scored against them, and the agent then works alongside your team in shadow, on live cases, before it acts on its own.
It's designed for it. When it's unsure, it hands the case to a person with what it found; every step is traced, so you can see why it did what it did; and the case joins the evaluation set, so the same mistake fails the next release instead of reaching a customer.
An agent gets only the tools and data its job needs, and what it reads — emails, web pages, documents — is treated as data, never as instructions. Personal and payment details are masked where the model doesn't need them, and every release is tested with injection and leak attempts.
Claude, GPT, Gemini or open models such as Llama and Mistral, chosen per task by testing them on your cases; LangGraph, the OpenAI Agents SDK or the Claude Agent SDK, or n8n where your team already runs it. We're not tied to any of them, and you can change models later without a rebuild.
Choosing the workflow and writing the evaluation set takes one to two weeks, a working agent on your data two to six, and a shadow run a few weeks more before it acts on its own. The estimate sets out the timeline before any work starts.
Building depends on the workflow and the systems it touches; after a first call we send a written estimate. Running cost is mostly model usage per case, which we estimate before we build and cap in production with limits on steps and spend.
When one agent isn't enough — a job that needs research, drafting and review, say — we build a coordinator that hands each part to a specialist agent and checks the result. We start with one agent, though: most workflows don't need more, and fewer moving parts are easier to trust.
