AI Agents That Run Your Operations — With Boundaries a Senior Engineer Designed

Case Studies

Case Studies

“Our accounts team was spending three hours every day processing invoices manually — downloading PDFs from emails, keying data into our accounting system, matching purchase orders, chasing approvals. The automation they built handles the entire workflow end to end. Invoice processing that used to take three hours now takes fifteen minutes of oversight. Our month-end close is two days faster and our accounts team can finally focus on analysis rather than data entry.”

“We were drowning in support tickets — over 400 per day — and our response times were getting worse every month. The AI-powered ticket routing system they built categorises every incoming ticket instantly, assigns it to the right specialist team, and handles the straightforward ones automatically. Our average response time dropped by 60% and customer satisfaction scores are the highest they have ever been.”

“Dispatch coordination was our biggest bottleneck. Manually assigning drivers to jobs, optimising routes, handling last-minute changes — it was chaos every morning. The automated dispatch system they built considers driver location, vehicle capacity, delivery windows, and traffic conditions to create optimal assignments in seconds. Our on-time delivery rate went from 82% to 96% and our drivers are covering 20% more drops per day.”

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A workflow moves data along a path someone predefined: when X happens, do Y. An AI agent owns an outcome. It reads the input — an email, a PDF, a support ticket — works out what the situation is, chooses the next action, and handles the exception itself, escalating only when it crosses a boundary we designed together. If your process can be fully described in if-then rules, you do not need an agent, and we will tell you so. Agents earn their keep on the work that today needs a person to read something and judge.

A production system, not a demo. A typical engagement delivers: an agent with a precisely defined scope of work, integrations into the tools you already use, an audit log of every decision the agent makes, a supervision view so your team can see and reverse its work, documentation, and a plan for widening its autonomy as trust builds. The deliverable that matters most is the least technical one: a written boundary, agreed with you before we build, of what the agent may decide alone and what it must hand to a human.

Boundaries are engineered, not hoped for. Every action an agent can take is permission-scoped — it physically cannot call an API it was not granted. Outputs pass structured validation before they touch your systems. Confidence thresholds force escalation to a human when the agent is unsure. And before launch we red-team it: senior engineers actively try to push the agent out of scope with adversarial inputs. We will also tell you plainly when a process should not get an autonomous agent yet — that conversation is far cheaper before launch than after.

Four categories are proven in production: reading and routing (inbound enquiries, support tickets, applications — classified and sent to the right person with context), document work (invoices, contracts, forms — extracted, validated, filed), first-line customer conversations that end in real actions (a meeting booked, a record updated, a ticket raised), and per-contact follow-up that adapts to what each person actually did. What agents should not own yet: decisions with regulatory exposure, irreversible one-shot actions, and judgement calls your own experts still disagree about. Our <a href="/services/process-consulting">agent readiness assessment</a> scores your processes against exactly this line.

A focused first agent starts from £1,000. Multi-agent programmes that take over a whole operational area typically run £15,000–£50,000. Cost tracks the risk profile more than the technology: an agent drafting internal summaries is cheap to bound safely; an agent that talks to your customers and writes to your CRM demands more validation, red-teaming, and supervision infrastructure. Every quote is scoped against the success definition we agree up front, and most clients see payback within three to six months.

Two to six weeks from kickoff for a first agent, launched in supervised mode — every decision logged and reviewable. What moves the timeline is not the AI: it is the integration surface (how many systems the agent acts on) and the risk of its actions (customer-facing agents get longer red-team cycles than internal ones). There is no ramp-up period after launch; the agent either does the work or it does not, and you can see which in the logs from day one.

No. We carry the engineering; what we need from you is process knowledge — the person who knows how the work is actually done today. After launch you get full documentation and decision logs, and you choose the operating model: we train your team to own the agent, or we run it under a maintenance retainer. Either way, you are never dependent on a black box only we understand.

It occasionally will — anyone who tells you otherwise is selling. The engineering question is making wrong cheap: actions are reversible wherever possible, low-confidence decisions queue for human review instead of executing, and every decision is logged so a mistake is traceable to its cause and becomes a boundary or prompt fix, not a mystery. In production our classification and extraction agents typically run above 95% accuracy — but the architecture is designed on the assumption of fallibility, not the hope of perfection.

Yes — that is the point. Agents act through the systems you already run: HubSpot, Salesforce, Pipedrive, Zoho, Slack, Teams, Google Workspace, Notion, Xero, QuickBooks, Zendesk, and anything with a REST API or structured export. No platform migration, no rip-and-replace. The agent becomes a very fast, very consistent operator of your existing stack.

They overlap, but the centre of gravity differs. An AI automation agency connects your apps and streamlines rule-based flows — valuable when your bottleneck is data movement. An AI agents agency builds systems for the work that needs reading, judgement, and action — valuable when your bottleneck is a person who has to look at something before anything can happen. If you are not sure which describes your problem, a <a href="/free-consultation">free scoping call</a> will tell you honestly, including if the answer is the simpler, cheaper option.

UK GDPR is a design constraint, not an afterthought. We minimise what reaches the model — agents receive the fields they need, not whole records. We sign DPAs, configure UK/EU data residency where the model provider offers it, ensure your data is not used for model training, encrypt in transit, and scope every agent permission to the minimum required. We are happy to walk your DPO through the architecture before anything is built.

Agents take the grind, not the judgement: the reading, routing, keying, chasing, and formatting that fills your team's week without using their expertise. In practice our clients redeploy those hours into fee-earning and customer-facing work. Where a process genuinely turns on your people's judgement, we design the agent to prepare the decision, not make it — and we will say so when a process should simply stay human.

We are model- and platform-independent. At the reasoning layer we work with GPT-4o, Claude, and other frontier models; at the orchestration layer with n8n, Make, and custom Node.js or Python — chosen per engagement based on volume, latency, data residency, and running cost, not on reseller commissions we do not take. Our <a href="/blog/complete-guide-n8n-automation-switching-from-zapier">n8n guide</a> shows how we think about the platform layer in practice.

That is expected, and the system is built for it. Agent boundaries and prompts are version-controlled, so changes are deliberate and reviewable rather than ad hoc. Decision logs surface drift early — you see the agent meeting more exceptions before anything breaks. Retainer clients get boundary and behaviour updates as standard; everyone gets documentation that lets any competent engineer evolve the agent safely. An agent should evolve with your business, not fossilise it.