AI & Automation
AI Automation
Automation that removes actual hours from actual weeks. We start from the task, not the technology, and we are happy to tell you when a rule and a cron job would beat a language model.
Start with the task, not the model
The AI projects that work begin with a specific, repetitive, well-bounded task that someone currently does by hand — reading invoices into a system, triaging inbound enquiries, summarising calls into a CRM, checking documents against a checklist. The ones that fail begin with a decision to use AI and a search for somewhere to put it.
Evaluation is the difference
A demo works on the examples you chose. A production system needs to be measured against cases you did not choose, including the awkward ones. Every automation we ship has an evaluation set, a measured accuracy figure, a defined confidence threshold and a human review path for anything below it.
We also set cost budgets and monitor them. Token spend that nobody is watching is how a useful automation becomes an unexplained line item.
What the engagement includes
Opportunity assessment
Which tasks are worth automating, and which are not.
Pipeline build
Retrieval, prompting, structured output and validation.
Evaluation harness
A measured accuracy figure you can hold us to.
Human-in-the-loop
Escalation paths and review queues for low-confidence cases.
Cost controls
Budgets, caching, model routing and monitoring.
Integration
Into the CRM, inbox, ERP or tool where the work already lives.
Technologies we use
Frequently asked questions
What can AI realistically automate in our business?
Tasks that are repetitive, language-heavy and tolerant of a review step: reading and extracting from documents, drafting routine replies, classifying and routing enquiries, summarising long material, and first-pass quality checks. Tasks needing exact arithmetic, hard guarantees or genuine judgement are a poor fit — those want ordinary software.
How accurate is it?
That is exactly the right question, and the honest answer is that it depends on the task and has to be measured. We build an evaluation set from your real data and report a number before you commit to a rollout. If the number is not good enough, we say so.
Will our data be used to train models?
Not on the enterprise API tiers we build on — Anthropic, OpenAI and Google all contractually exclude API data from training. For genuinely sensitive material we can design around self-hosted open models instead.
Is this just Zapier with extra steps?
Sometimes it should be. If a no-code automation solves your problem, we will recommend it and help you set it up. Custom work earns its cost when the volume is high, the logic is complex, or the task needs judgement a rule cannot express.