Pillar 02
AI agents built for real work, not just chat
I design and build agents that do a defined job inside your existing tools: read incoming documents, look things up, update records and hand off to a person when a decision is sensitive. Each agent gets narrow permissions, a test suite and logging, so you can see exactly what it did and why.
Sounds familiar?
- Your team retypes the same information from emails, PDFs and forms every day
- Leads and support requests wait hours for a first response
- You tried a generic chatbot, but it could only talk, not act
- You worry an AI system will send the wrong price or email the wrong customer
What I deliver
Tool-connected agents
Agents that call your APIs, CRM, booking system, ticketing tool or database to complete a task end to end.
Document processing and extraction
Invoices, orders, intake forms and unstructured emails parsed into structured records and validated before they are written.
Guardrails and human approval
Narrow permissions per agent and approval checkpoints for sensitive actions such as refunds, quotes or contract changes.
Evaluation and monitoring
Test sets built from your real cases to measure accuracy, catch regressions and reduce hallucinations before release.
Typical projects
- Lead qualification and intake agent that updates your CRM
- Customer support and booking agent connected to your calendar
- Supplier invoice and order processing into your database or ERP
- Internal operations agent that prepares reports and drafts replies for approval
Tools I use
Questions
How do you stop an agent from making costly mistakes?
Each agent can only use the tools and data it needs for its job. Sensitive actions wait for human approval, every action is logged, and the agent is tested against your real cases before it goes live.
Are we locked into a platform or monthly license?
No. The agents run in your own cloud account, the code is yours, and you pay the model provider directly at cost.
Can the agent work with our existing software?
Usually yes. If a system has an API, a database or even a structured export, an agent can work with it. The readiness audit confirms this up front.
Next step
Private AI Knowledge Base →
A private assistant that answers questions from your own documents with sources, hosted in your cloud in the EU, so company data never trains public models.