Your Team Should Not Spend Hours Doing Work AI Can Handle.
AI agents that receive work, decide what to do with it, act across your tools and escalate to a person when they should.
- No obligation, no sales script
- You own the code
- Fixed scope after discovery
100+
Projects delivered
98%
Client satisfaction
30+
AI and automation experts
24/7
Support and maintenance
Trusted by forward thinking brands worldwide. Every build ships with documentation, training and full source code ownership.
What the status quo is costing you
Same data, five times
The same record gets keyed into several tools
Nobody planned it that way. It accumulated, and now it costs a few hours a week from several people at once.
Errors compound
Manual steps fail quietly
A wrong figure copied once becomes a wrong invoice, a wrong report and an awkward call three weeks later.
It does not scale
The process breaks as volume grows
What runs fine at ten orders a day starts cracking at fifty. The usual answer is hiring, and it is the expensive one.
The same work, two ways
Manual today
With an agent
Someone watches an inbox and forwards each message to the right person
Every message is read, classified and routed the moment it arrives
Invoice details are typed from a PDF into the accounting system
Fields are extracted, validated against the purchase order and posted
A person checks two systems and fixes whatever disagrees
Records are reconciled continuously, and only real conflicts surface
Reports are assembled by hand every Monday morning
The report is built, checked and delivered before anyone logs in
Follow ups happen when somebody remembers
Every thread is tracked, and nothing is dropped because a person was busy
Systems that finish the job, not chatbots that describe it
A chatbot answers. An agent acts. It receives work, decides what to do with it, uses your actual tools to do it, and comes back with the job finished or a clear question for a person.
We build the second kind, because answering a question about an invoice is worth far less than processing it.
1 to 3 months
Typical payback on one workflow
60%
Manual work removed, on average
100%
Actions logged and auditable
AI agent architecture
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Trigger
What starts the run. A new email, a form, a webhook, a row in a database or a schedule.
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Reasoning
The model reads the context and decides what needs to happen, within limits you set.
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Tools
The functions it may call. Your CRM, your database, an API, a document parser, a calendar.
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Actions
The work itself. Records written, messages sent, files produced, tickets closed.
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Memory
What it learned this run, stored so the next one starts better informed.
What the agent learns feeds back into the next decision.
What AI agents can do
Read and classify
Take unstructured input such as email, PDFs and forms, and turn it into structured records.
Decide and route
Apply your rules to work out who or what should handle something, and send it there with context.
Act across systems
Create, update and reconcile records in the tools you already run, not in a separate silo.
Draft for approval
Produce replies, quotes and reports that a person signs off in seconds instead of writing.
Watch and alert
Monitor for conditions that matter and raise the right alert to the right person, once.
Escalate cleanly
Recognise its own limits and hand over with the full history attached.
Business workflows we automate
Invoice and document processing
Extract, validate and post data from PDFs and scans into your finance system.
Inbox triage and routing
Classify inbound mail, attach context and route it without a person reading first.
CRM hygiene and enrichment
Deduplicate, enrich and keep records current across every tool that holds them.
Order and ticket handling
Move work through its stages, chase what is stuck and close what is finished.
Report generation
Build recurring reports from live data and deliver them on a schedule.
Onboarding and offboarding
Run the checklist end to end, from account creation to access removal.
Integrations we connect to
Model choice follows the task, not a vendor relationship. Where data cannot leave your network, we self host.
- Models
- Claude GPT open models self hosted
- Orchestration
- n8n LangGraph custom Python services
- Business tools
- HubSpot Salesforce Pipedrive Zoho Odoo
- Finance
- QuickBooks Xero Sage Stripe
- Comms and storage
- Gmail Outlook Slack WhatsApp S3 Google Drive
The agent never gets the last word on anything that matters
Approval gates
Anything financial, contractual or irreversible waits for a person. You choose where the line sits.
Confidence thresholds
Below a set confidence the agent stops and asks rather than guessing.
Full audit trail
Every decision and action is logged with its inputs, so any outcome can be traced back.
One click override
A person can reverse or redo any step, and the agent learns from the correction.
One workflow, start to finish
A real run, step by step. The highlight follows the system as it works.
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1
A supplier invoice arrives by email
The trigger fires the moment the message lands, no polling and no queue.
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2
The agent reads the PDF
Supplier, line items, totals, dates and currency are extracted as structured fields.
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3
It checks the purchase order
Your ERP is queried and the totals are compared. A mismatch stops the run here.
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4
It posts the invoice
The record is created in your accounting system with the document attached.
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5
A person approves payment
Anything over your threshold lands in an approval queue with the reasoning shown.
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6
The run is logged
Inputs, decisions and outputs are stored, and the outcome informs the next one.
How it works
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1
Talk
A 30 minute call. You describe the process, we tell you if it is worth doing.
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2
Scope
Fixed price, fixed timeline, written down before anyone commits.
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3
Build
Working software every week, not a status report at the end.
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4
Hand over
Migration, training, documentation and the keys.
Questions people ask
How is an agent different from a chatbot?
A chatbot returns text. An agent calls tools and changes real records, then reports what it did. The chatbot tells you the invoice total; the agent posts the invoice.
Will an agent make mistakes?
Sometimes, which is why every one ships with confidence thresholds, approval gates on risky actions and a complete log. The goal is removing the routine ninety percent so your team handles the exceptions.
Does our data go to an AI vendor?
Only if you want it to. We can run open models on your own servers so nothing leaves your infrastructure.
Do we need to replace our current tools?
No. Agents sit on top of what you already run and talk to it through APIs.
How soon do we see a return?
A single well chosen workflow usually pays for itself within one to three months. We agree the baseline up front so the number is verifiable.
What happens when something breaks?
Retries, error branches and alerting are built into every workflow, so failures surface immediately rather than being discovered weeks later.
