AI & Automation
AI Automation
Workflow automation that removes repetitive manual work and connects the systems your teams already use, with AI where it helps.
Talk to us about AI AutomationAutomation takes the repetitive steps out of a process and connects the systems your team already uses, so information moves without someone copying it. AI is added where a step needs reading, classifying or drafting, and left out where a plain rule does the job better.
Automating the wrong step is worse than automating nothing. Before building we look at each step and ask two things: how often does it happen, and what does a mistake cost. Frequent, low-risk steps are automated. Rare or costly ones keep a person in the loop.
Problems this solves
- People re-type data between a CRM, a spreadsheet, an inbox and an accounting or operations system.
- Documents such as invoices, forms and emails are read and sorted by hand.
- Follow-ups and status updates depend on someone remembering.
- Existing automations break silently and nobody notices for days.
What you get
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Process map and automation plan
Each step listed with its volume and risk, and a clear decision for each: automate, assist with AI, or keep manual.
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System integrations
Connections between your tools through their APIs, webhooks or files, built so a change in one system does not break the rest.
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AI steps where they help
Reading documents, extracting fields, classifying requests and drafting replies, each with a confidence check.
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Background jobs with retries
Work runs in queues, failed steps are retried safely, and nothing is processed twice.
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Human checkpoints
Review queues for the cases a rule or a model should not decide alone.
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Alerts and run history
You can see what ran, what failed and why, and you are told when something stops.
Is this the right fit?
A good fit when
- The process is repeated often and the steps are known.
- The systems involved can be reached by API, export or email.
- Someone owns the process and can say what correct looks like.
Probably not when
- The process changes every week. Stabilise it first.
- It happens a few times a year. The build will cost more than it saves.
- The real problem is an unclear process, not a slow one.
How the work runs
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01
Understand
A short call to understand the actual problem, not just the requested feature. We push back if the scope doesn't match the goal.
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02
Architect
A concrete technical plan and estimate before any code is written, covering stack, integrations, and where the real risk is.
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03
Build
Iterative delivery with visibility into progress, not a black box until launch day.
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04
Validate
Tested against real-world requirements and failure modes, not just the happy path, before anything reaches production.
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05
Operate
Deployed, monitored, and supported after launch, with infrastructure and observability included from day one. Never a forced retainer.
Questions about AI Automation
Do we need AI for automation?
Often not. Many valuable automations are plain rules and integrations. We add AI only for steps that involve unstructured input such as free text, documents or images.
Can you automate work across tools we already pay for?
Yes. Most of this work is connecting existing systems. We check what each tool's API allows before promising anything.
What happens when an automated step fails?
It is retried where that is safe, and otherwise stopped and reported with enough detail to fix it. Silent failure is the thing we design against first.
Should we use a no-code automation tool instead?
For simple flows between popular tools, a no-code product can be the right answer and we will tell you. Custom automation fits when volume, reliability, cost or data rules go beyond what those tools handle well.
From the blog
No-Code Automation or Custom Build: How to Decide
No-code tools are the right choice more often than developers admit, and the wrong one more often than their marketing suggests.
Document Processing with AI: Invoices, Forms and Email
AI can read documents that older tools could not. What makes it dependable is the checking around the model, not the model alone.
Why Automations Fail Silently and How to Catch It
The dangerous failure is the one nobody sees. Five causes of silent failure, and the monitoring that makes each of them visible.