AI agents that actually do work — not AI demos that almost do.
Practical AI agents wired into the tools your team already uses — inbox triage, lead qualification, internal Q&A, customer support drafts, recurring reporting. Implementation, not demos.
Teams with repetitive decisioning hidden inside an inbox.
- Repetitive decisioningYour team makes the same judgement calls dozens of times a day — routing, qualifying, drafting, summarising.
- Inbox-heavy operatorsLead and client conversations live in email. Follow-up quality depends on one person being online.
- The same twelve questionsFounders and ops leads answering the same prospect or team questions on a loop. An agent holds that context.
A few cases where an agent will not earn its keep yet.
- Pre-revenueWithout real traffic or workflow volume there is nothing meaningful for an agent to improve.
- You only want a chatbot widgetWidgets are easy. Production agents that actually do work require scope, evals and iteration.
- General-purpose creativity toolsWe build narrow, accountable agents. If you want a blank-canvas creative assistant, a commercial product already does that.
Discovery
We map where the team currently spends judgement time — and whether an agent is the right fix. Not every task is.
Scope one agent
We pick the agent with the clearest payback and define the inputs, tools, guardrails and success criteria.
Build + evals
We ship the agent with a dataset of real prior cases as the evaluation bar. No green dashboards without it.
Handover + monitor
Your team runs it. We watch for drift, failure modes and new use cases. Scope the next agent only when this one is healthy.
Investment guidance
Circle Wellbeing
Website, booking and local-service architecture for a three-clinic wellness brand.
See work ↗Revitalise WCS
A publishing system the organisers can actually run — without waiting on a dev cycle.
See work ↗CrownX operating model
Forms, payments, CRM steps and automations wired into one working flow.
See work ↗Questions we get before people book.
How reliable are these in practice?
As reliable as their evals. We set the bar with real prior cases, measure against it, and only ship when the agent clears it. Reliability without evals is marketing copy.
What about hallucinations?
Grounding, retrieval, structured tool use, and refusal behaviours. Most production hallucinations come from asking the model to make up what it should be retrieving. We engineer around that.
We are a clinic — what about patient data?
We do not touch clinical data. Agents are scoped to operations: bookings, reminders, admin queues, intake triage, follow-up. De-identified examples only, and your vendor agreements apply.
What tools do you build on?
Model-portable where it matters — OpenAI, Anthropic, Google, open source. Orchestration via TypeScript, n8n, Make or native platform tooling depending on the workload. We pick for fit, not fashion.
What if a better model drops mid-project?
Good. Our builds are model-portable by default. The prompts, evals, tools and guardrails outlast any one model — you swap the engine and re-run the evals.
Who owns the work?
You do. Prompts, configurations, evals and integration code are handed over at the end of the engagement with documentation your team can run without us.