AI automation that takes the busywork off your team, not the judgement.
Agents that read the invoice, sort the inbox, update the CRM and prepare the report, then hand the decisions that matter to a person. Built with approvals, audit logs and a way back when the model is unsure.
- People approve what matters
- Every action logged
- Measured against the manual way
Work that's ready to automate.
The best candidates are high-volume, repetitive and rule-of-thumb heavy, with a clear right answer most of the time. If these sound familiar, there's probably a win here.
- 01
People copy data between tools
The same details move from email to spreadsheet to accounting software, by hand, every day.
- 02
The inbox decides the day
Requests, orders and complaints arrive unsorted, and someone spends hours just routing them.
- 03
Documents pile up
Invoices, purchase orders and forms wait for someone to read, check and key them in.
- 04
Reports take a day to assemble
Every week someone pulls numbers from five places to build the same report.
- 05
Follow-ups slip
Leads, renewals and reminders depend on someone remembering, and some never happen.
- 06
Hiring is the only way to scale
Volume grows, and the only answer so far has been more people doing the same steps.
Rule-based automation or AI agents? Often both.
Fixed rules are cheaper and perfectly predictable when the input is tidy. AI earns its place when the input is messy and needs judgement. Good automation uses each where it fits.
| Consideration | Rule-based automation | AI agents |
|---|---|---|
| Handles | Rule-based automationStructured, predictable input | AI agentsMessy emails, PDFs and free text |
| Decisions | Rule-based automationOnly what was written as a rule | AI agentsJudgement calls, with a confidence level |
| When unsure | Rule-based automationFails or does the wrong thing | AI agentsHands over to a person |
| Upkeep | Rule-based automationA new rule for every new case | AI agentsImproves with examples and evaluations |
| Cost per task | Rule-based automationClose to zero | AI agentsSmall, measured per run |
| Best for | Rule-based automationMoving and syncing clean data | AI agentsReading, classifying and drafting |
We'll use plain rules wherever they'll do, and only bring in a model where judgement is genuinely needed. It keeps costs down and behaviour predictable.
Agents for the work your team repeats every day.
Each one is scoped to a single workflow, measured against how long it takes by hand, and built so a person can step in at any point.
- 01
Workflow agents
Multi-step processes where a model handles the judgement and code handles everything that must be exact.
- Order processing
- Onboarding
- Approvals
- 02
Inbox and ticket triage
Emails, WhatsApp messages and tickets read, tagged by intent and urgency, and routed to the right person with a suggested reply.
- Helpdesk
- 03
Document processing
Invoices, purchase orders, IDs and forms read, checked against your records and posted where they belong.
- Invoices
- Purchase orders
- KYC
- 04
CRM and back-office updates
Records kept current across your tools: new leads logged, deals updated, follow-ups scheduled, without anyone re-typing.
- CRM
- Accounting
- Sheets
- Slack
- 05
Reporting automation
The weekly report assembled from every source and written up in plain language, ready before the meeting.
- Weekly reports
- Summaries
- Alerts
- 06
Human-in-the-loop review
A simple queue where people approve, correct or reject what the agent proposes, and every correction makes it better.
- Approval queue
- Corrections
- Audit log
From a manual process to an agent you trust with it.
We measure the manual way first, so the agent has a baseline to beat and you can see exactly what it saves.
- 01
Map the process
We sit with the people who do the work and write down every step, exception and decision.
You get- Process map
- Time per task
- 02
Pick what to automate
We split the work into what rules can do, what needs judgement, and what should stay with a person.
You get- Automation plan
- Fixed quote
- 03
Build a test set
Real past cases with known-right outcomes, so the agent is judged on your work, not a demo.
You get- Evaluation set
- 04
Build with a person in the loop
The agent runs alongside your team and proposes actions that people approve, until the numbers say it's ready.
You get- Shadow mode
- Approval queue
- 05
Go live in stages
Low-risk cases first, then more as accuracy holds, with everything logged and reversible.
You get- Staged rollout
- Audit log
- 06
Measure and improve
Time saved, error rates and cost per run tracked monthly, and corrections fed back into the system.
You get- Monthly report
- Improvements
What we build is yours. In writing.
Software you'll run for years shouldn't tie you to the people who wrote it. Once the work is paid for it's yours outright, handed over in accounts you control.
- 01
Code and IP
Full ownership of the code and design files passes to you on payment. No licence fees, ever.
- 02
Your own accounts
Repositories, hosting, stores and domains are handed over in your name, not kept in ours.
- 03
Documentation
Setup guides, architecture notes and a record of key decisions, so any capable team can pick it up.
- 04
Confidentiality
We sign an NDA before you share anything sensitive, and treat your data that way throughout.
Automation you can switch on without losing sleep.
An agent that acts on your systems needs more care than one that only answers questions. These come standard.
- Control
Approvals where it matters
Payments, refunds and anything irreversible wait for a person until you decide otherwise.
- Trust
A full audit log
Every input, decision and action recorded, so you can always see why something happened.
- Safety
Narrow permissions
Each agent can only touch the tools and records its job needs, nothing more.
- Fallbacks
A way back
When confidence is low or a tool is down, the task goes to a person instead of failing quietly.
- Cost
Cost per run
Every run is metered, with budgets and alerts, so savings stay ahead of spend.
- Privacy
Your data stays yours
Enterprise model tiers that don't train on your data, and personal data redacted where possible.
Plugged into the tools you already use.
We connect to your existing systems rather than asking you to move to a new platform.
Models
- Claude
- OpenAI
- Gemini
- Open-weight models
Orchestration
- Tool use
- MCP
- Queues
- Scheduled jobs
Connects to
- Gmail
- WhatsApp Business
- Slack
- Google Sheets
Business tools
- Tally
- Zoho
- HubSpot
- Salesforce
Reliability
- Audit logs
- Retries
- Alerts
- Langfuse
Start where you are, with a fixed quote first.
Every engagement starts with an itemised proposal. Pick the model that fits what you already know.
- 01Discovery sprintThe problem is clear, the solution isn't yet
- 02Fixed-scope buildRequirements are understood and agreed
- 03Dedicated product teamThe product is live and keeps growing
Recent work from the studio.
A look at what we've designed, built and launched recently.
Automation, or something bigger?
If the job is a customer-facing feature or a whole new system, these are better places to start.
- AI SolutionsYou want AI features inside your product for your customers to use.
- Custom Software DevelopmentThe process needs a proper internal system, with automation as one part.
- Product ModernisationOld systems without APIs are the real blocker to automating anything.
- Ongoing Product EngineeringYour automations are live and need monitoring and steady improvement.
Questions
The things people
ask us first.
If yours isn’t here, send it over. We reply within 1 hour, guaranteed, and you’ll get a straight answer rather than a brochure.
What's the difference between an AI agent and normal automation?
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Normal automation follows fixed rules, which works when the input is tidy and predictable. An AI agent can read messy input like emails and PDFs, make a judgement call and decide which tool to use next. We combine the two: rules wherever they'll do, a model only where judgement is needed.
Will it make mistakes?
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Sometimes, which is why we design for it. Agents are measured against a set of real past cases before going live, anything high-stakes waits for a person's approval, and low-confidence cases are handed over rather than guessed. You see the accuracy numbers before you decide how much to trust it.
Can it work with our existing tools?
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Usually, yes. We connect through each tool's API, and where there's no API we use exports, email or scheduled syncs. Accounting, CRM, email, WhatsApp and spreadsheets are the most common.
How do we know it's worth it?
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We time the manual process before we start and track time saved, error rates and cost per run afterwards. If a workflow isn't worth automating, we'll say so in the mapping stage, before you spend on a build.
Is our data safe?
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We use enterprise model tiers that don't train on your data, give each agent only the access its job needs, redact personal data where we can and keep a full audit log of every action.
Will this replace our team?
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It replaces the repetitive part of their work, not the people. Most teams use the time to handle more volume or focus on the cases that need real judgement.
Where ambitious
ideas become reality.
We design, develop, and support digital experiences that turn ambitious ideas into meaningful products.