Somebody is the integration
A person reads one screen and types into another. It works, it is slow, and it stops entirely when they are on holiday.
01 AI agents & automation
AI that does the task, not just describes it.
A chatbot answers a question. An agent finishes the job — reads the record, checks the calendar, updates the CRM, sends the confirmation, and hands to a person when it should not decide alone.
- Every action logged and reviewable
- Human approval on anything irreversible
- Runs beside your process before it replaces it
The problem
The work between the systems.
Most businesses have already bought good software. What they have not bought is the person who moves information between it — so a human does that, all day.
Decisions wait on availability
A routine judgement — is this lead worth calling, does this booking fit — sits in a queue until someone gets to it.
Rules live in people's heads
The criteria for escalating, discounting or prioritising are real but undocumented, so they are applied inconsistently.
Volume means headcount
Doubling enquiries means doubling the admin, because none of the routine handling is automated.
How it works
AI Agents & Automation, in shape.
Request
A task arrives — an enquiry, a record, a trigger.
Reason
The agent works out what the objective needs and which step comes next.
Tools & APIs
It calls only the functions it has been given access to.
Your data
Reads the records it needs, and nothing outside its scope.
Action
Updates, drafts or books — with approval first where it matters.
Result
Logged end to end, so every decision is reviewable.
Anything irreversible — spending, contacting a customer, deleting a record — waits for a person unless you decide otherwise.
Our approach
Bounded autonomy: real tools, hard limits.
An agent is a language model given a specific job, a defined set of tools it may use, and clear boundaries on what it must not decide alone. The engineering is mostly in the boundaries, not the model.
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One job, clearly scoped
Agents that do one thing well beat one that vaguely does everything. We scope narrow and expand once it is proven.
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A defined toolset
The agent can call only the functions we give it — read a record, check availability, create a draft. Nothing else is reachable.
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Approval gates on consequences
Anything that spends money, contacts a customer or cannot be undone waits for a person unless you explicitly decide otherwise.
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Full audit trail
Every step, tool call and decision is logged, so you can see exactly why it did what it did.
What's included
What we build into an agent.
Usually one agent for one workflow first, measured, then extended.
Workflow analysis
We map the real task — inputs, decisions, exceptions — including the parts nobody has written down.
Tool and API integration
Connecting the agent to your CRM, calendar, booking system, database or accounting package.
Reasoning and task planning
Structuring how the agent breaks a request into steps and decides which tool to use next.
Guardrails and constraints
Explicit limits on scope, spend, recipients and actions, enforced in code rather than in the prompt.
Human-in-the-loop approval
Review queues for the actions you want a person to sign off, with context attached.
Escalation and handover
Clear paths to a human when confidence is low or the situation is outside scope.
Memory and context
Giving the agent access to the records and history it needs, without exposing everything else.
Logging and observability
A readable trace of every run, so failures are diagnosable rather than mysterious.
Evaluation against real cases
Testing on your actual historical data before it touches anything live.
Monitoring and tuning
Tracking success rate, escalation rate and cost per run after launch.
Use cases
What businesses use agents for.
Realistic examples. What fits your business depends on your data and process — that is what the first call establishes.
Lead triage
Read an incoming enquiry, check it against your criteria, enrich it from your records, route it to the right person and draft a first reply.
Booking coordination
Check genuine availability across staff and equipment, propose slots, confirm, and update every system that needs to know.
Quote preparation
Pull the relevant history and pricing, assemble a draft quote, and put it in front of a person to approve and send.
Job status chasing
Monitor jobs against expected timelines, flag the ones slipping, and notify the right person with the context attached.
Document handling
Read incoming invoices or forms, extract the fields, validate against records, and file or escalate.
Internal question answering
Answer staff questions from your own documentation, with a link to the source rather than an unsourced assertion.
Technology
What we build with.
Tools we actually use. If your stack needs something not listed, ask — we will tell you honestly whether we can integrate with it.
Workflow & orchestration
n8nCustom orchestrationScheduled jobsWebhooks
Models & reasoning
LLM integrationFunction callingStructured outputRetrieval augmentation
Business systems
CRM APIsCalendarsBooking systemsAccounting platformsEmail & SMS
Data & hosting
MySQLPostgreSQLREST APIsAzureAWS
Outcomes
What changes once an agent is running.
The in-between work disappears
Moving data between systems stops being a person's afternoon.
Consistent response times
Routine handling happens at 2am the same way it happens at 2pm.
Rules applied the same way every time
Criteria encoded once and applied consistently, instead of varying by who is on shift.
Volume without headcount
More enquiries handled by the same team, because the routine part is automated.
Visibility into the process
Logging reveals where the process actually breaks, which is often a surprise.
People on the judgement calls
Your team spends the day on the decisions that genuinely need a human.
How it runs
From one workflow to a working agent.
Seven stages. Nothing touches live systems until it has been tested against real cases.
01
Discover
Map the workflow, the decision rules and the exceptions.
02
Plan
Define scope, tools, guardrails and the success measure in writing.
03
Build
Integrations, reasoning, guardrails and approval paths developed and unit tested.
04
Integrate
Connected to your live systems in read-only mode first.
05
Test
Evaluated against real historical cases, running beside the manual process.
06
Launch
Enabled for real work once accuracy is demonstrated, not assumed.
07
Improve
Monitor success and escalation rates, tune, then widen scope.
Questions
About ai agents & automation.
If yours is not here, ask us directly — you will get a straight answer rather than a sales call.
A chatbot converses — it takes a message and returns a message. An agent takes an objective and works towards it, deciding which tools to call and in what order, then taking actions in your systems. A chatbot can tell someone your availability. An agent can check the calendar, book the slot, update the CRM and send the confirmation.
Three things. It can only call the specific functions we give it, so there is no path to anything outside scope. Anything irreversible — spending money, contacting a customer, deleting a record — sits behind human approval unless you decide otherwise. And every run is logged, so mistakes are diagnosable rather than mysterious.
No, and usually you should not. Agents work through the APIs of the tools you already run. The point is to remove the manual work between them, not to become another system of record.
We agree the measure before building — time saved, response time, escalation rate, error rate — and it runs alongside the manual process until it is demonstrably meeting it. If it does not, we say so.
Model usage is charged per token, so cost scales with volume. We estimate it during scoping using your real volumes and design the workflow to keep calls efficient. It is rarely the largest line item.
Not without your explicit agreement. We will tell you which services process your data and what their retention terms are before anything is connected.
Related AI solutions
These are usually built together.
Most projects combine two or three — an agent that runs on n8n workflows, or a chatbot that hands to a custom system.
Tell us which task keeps landing on someone's desk.
We will tell you whether an agent can take it, what it would need access to, and roughly what it saves.