Voice Booking Agent, Dental Appointment Agent
A voice agent that answers calls, checks live availability, books the appointment and writes the patient record back — memory and tools wired to one reasoning loop.
Portfolio
AI Agents Portfolio
Agents that finish the job rather than describe it — scoped to one task, evaluated against real cases, and given only the permissions the work requires.
- Tool & Function Calling
- RAG & Knowledge
- Guardrails & Approvals
- Evaluation Harnesses
- Tracing & Observability
Selected work
Our Work
A selection of the agent patterns we put into production across industries.
Personal Assistant Agent, Voice & Text Executive Assistant
An assistant that takes voice or text over Telegram, transcribes it, then creates tasks, drafts email and manages the calendar through a shared tool set.
Database Chat Agent, Natural Language to SQL
A conversational agent that answers business questions by querying Postgres directly, holding session memory so follow-up questions keep their context.
Gmail Email Agent, Inbox Triage & Reply Agent
An agent that reads incoming mail, decides whether to file, escalate or answer it, and drafts the reply through a structured output parser.
WooCommerce Ops Agent, Store Operations Agent
A store agent reachable over Telegram that reads orders, updates products and reports on stock, with its own operating brief held in memory.
AI Coaching Agent, Client Coaching Workflow
A multi-stage coaching agent that intakes client context, plans a programme and follows up on progress across a long-running conversation.
Production agent builds. Each preview is the live workflow canvas — hover any card to watch it run left to right.
Methodology
Our Approach
How an agent gets from a scoped task to a widened mandate, without the pilot-that-never-ships problem.
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01
Scope The Job
One well-defined task with a measurable outcome, and an explicit list of what the agent may and may not decide on its own.
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02
Build The Tools
Typed, permission-scoped integrations with your systems, plus retrieval over the knowledge the agent needs in order to be right.
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03
Evaluate
A test set built from your real historical cases, scored on every iteration, with a target agreed before anything goes live.
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04
Pilot With Oversight
The agent runs on live work with a human reviewing its actions, so accuracy is proven in production conditions before authority widens.
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05
Harden
Guardrails, rate and value limits, tracing, replay and cost and latency monitoring — the parts that separate a demo from a dependency.
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06
Widen The Mandate
Approval gates relax as the numbers justify it, and the next task joins the agent’s scope on the same evidence-led terms.
Common challenges
What Should Your Agent Take Off Your Desk?
The questions every serious agent conversation runs into.
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How is this different from a chatbot?
A chatbot converses; an agent acts. It calls your systems, completes a multi-step task and produces a result. Both have a place, but they are different engineering problems with different risks.
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How do you stop it doing something bad?
Permission scoping per tool, hard value and rate limits, blocked actions, approval gates on anything irreversible, and deterministic validation around every consequential step.
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What happens when it is unsure?
It escalates rather than guesses, handing a person the full picture of what it checked and why it stopped. Every failure feeds the evaluation set so it is caught automatically next time.
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Which model are we tied to?
None. The architecture keeps model choice swappable and routes per task, so a pricing or capability change from a provider is a config decision rather than a rebuild.
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Is our data used for training?
No. Enterprise API tiers with training disabled, and open-weight models running inside your own infrastructure where policy or regulation requires it.
Ready To Put An Agent On Real Work?
Name the task that eats your team’s day. We will scope an agent for it, define the guardrails and show you the numbers from a pilot.