AI Integration and Process Automation
Putting AI where the work actually happens — inside your processes, your platforms and your inboxes. Nine engagements, each measured on how many cases complete without a person rather than on how many steps were automated.
The benefit lives in the exceptions.
Rule-based automation already handles the cases that were easy, which is why the remaining work is the hard part: the invoice in an unfamiliar layout, the email that could be three different things, the record that almost matches. Those cases are where the cost sits, and they are the only reason to add AI to a process at all.
So every engagement here starts by measuring the process as it runs rather than as it was documented, designs the exception path before the automated one, and reports straight-through rate rather than steps automated. Where measurement shows a step exists because of a system limitation removed years ago, we say to delete it — removing a step is cheaper and more reliable than automating it.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Intelligent Process Automation
Automation for processes that need judgement, measured on straight-through rate with exceptions designed in from the start.
Read more →AI Integration into CRM, ERP and HRMS
AI inside the systems people already use, respecting the permission model and surviving vendor upgrades.
Read more →Business Workflow Automation
Cross-system orchestration with retries, idempotency and visibility, so nothing stalls unnoticed.
Read more →Document Workflow Automation
Intake, classification, extraction, validation and filing as one flow, measured on straight-through rate.
Read more →RPA Modernisation with AI
An honest robot-by-robot audit: retire, re-platform onto APIs, or extend with AI where judgement is needed.
Read more →AI API and Middleware Development
A versioned, authenticated, rate-limited service layer so many applications can consume AI safely and cheaply.
Read more →Legacy System Modernisation with AI
AI-assisted comprehension, rule extraction and test generation, with every finding verified before it is trusted.
Read more →Internal Knowledge Management AI
Answers across your internal systems with source, date and permissions respected — and stale content actively removed.
Read more →AI Email and Inbox Automation
Classification, routing, extraction and drafted replies for shared inboxes, with a person approving what matters.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Automate a process end to end
Intelligent process automation where judgement is the blocker, workflow automation where the process spans systems and runs on email, and document workflow automation from intake to filing.
Put AI where people already work
Integration into CRM, ERP and HRMS using supported extension points, internal knowledge AI with permissions enforced at retrieval, and inbox automation that drafts rather than sends.
Modernise what you already have
RPA modernisation starting from an audit rather than an assumption, and legacy system modernisation that uses AI for comprehension, never for automated rewriting.
Make AI a platform capability
AI middleware and API development, so the second and third teams consume a governed service rather than each integrating with a provider on their own terms.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
What is the difference between RPA and intelligent process automation?
RPA follows fixed rules, usually by driving system interfaces as a person would; it breaks on variation and hands every judgement case back to people. Intelligent process automation adds models that read unstructured input and make uncertain decisions, with confidence thresholds routing the doubtful cases to a reviewer with the reason attached.
How do you measure whether an automation worked?
Straight-through rate — the share of cases that complete without a person — reported by case type, alongside cycle time and cost per case. Steps automated is a vanity metric: a process can have twenty automated steps and still send eighty per cent of cases to a human, which is what the count conceals.
Should we use agents for this instead?
Sometimes, and the distinction matters. The engagements here automate defined processes where the steps are known and the value is reliability at volume. AI agents decide their own sequence of actions, which suits open-ended tasks and costs more in control and oversight. A known process does not need an agent, and giving it one adds unpredictability for nothing.
What happens to the people currently doing this work?
Their work changes shape rather than disappearing — fewer routine cases, more exceptions and judgement. That is a real organisational change and it goes badly when it is sprung on people, so exception handlers are involved in the design from the start. They know the edge cases better than the process documentation does.
Do we own what you build?
You do, in full, on final payment: workflows, integrations, models, prompts, validation rules, tests, runbooks and documentation. Everything is built on supported interfaces so it survives your vendors' upgrades, and handed over so your own team can change it without us.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.