Voice Booking Agent, Dental Appointments by Phone
A voice agent that answers the practice phone, understands natural speech, checks live availability and books the appointment before hanging up.
Agents that complete work rather than describe it — each one scoped to a single job, bounded by explicit guardrails, and logged end to end.
Selected work
Agents that hold context, use real tools and take action — not prompts wrapped in a chat window.
A voice agent that answers the practice phone, understands natural speech, checks live availability and books the appointment before hanging up.
An inbox agent that reads, categorises and labels incoming mail, drafts contextual replies and surfaces only what genuinely needs a human decision.
A personal agent reachable by voice or text on Telegram, managing calendar, tasks, notes and email across connected Google services.
An operations agent running store admin from a chat thread — order lookups, stock changes and customer queries without opening the dashboard.
An agent that translates plain-English questions into safe, schema-aware queries and returns explained results to non-technical staff.
A structured coaching agent running a multi-session programme, tracking each participant’s progress and following up by email between sessions.
A selection of ai agents work. Detailed case studies, metrics and references are shared on request.
Methodology
The methodology behind every engagement, from first workshop to the retainer that follows launch.
One job, defined precisely: what counts as done, what must never happen, and which cases always belong to a person.
The narrow function set the agent may call, the value and volume limits around it, and the approval gates on anything consequential.
Built with structured tracing from the first commit, so every plan and tool call is inspectable while it is still being developed.
Scored against real historical cases with known correct outcomes, including the adversarial ones you would not want it to get wrong.
It proposes, a person approves, and we measure the agreement rate until the boundary is proven against your own data.
Gates relax only where evidence supports it, with alerting, spend limits and a kill switch left permanently in place.
Common challenges
The problems clients usually arrive with — and how each one gets handled.
Narrow toolsets rather than open access, approval gates on destructive operations, value and volume caps per run and per day, and actions built to be reversible.
It is scored against historical cases where the right outcome is already known, then runs supervised until the agreement rate justifies relaxing a gate.
Often not. Deterministic processes are cheaper and easier to reason about as plain automation, and we will say so rather than putting a model where an if-statement belongs.
Smaller models where they suffice, caching on repeated context and hard spend caps per agent — with cost per completed task reported alongside accuracy.
Every run stores its plan, tool calls, inputs and outputs, so any outcome can be replayed and explained to a customer, an auditor or a post-mortem.
Name the process that eats your team’s week. We will scope it, define the boundary and show you what an agent can safely own.