Services / AI agent development
AI agents that do the work, not the demo.
Every team has seen the impressive agent demo that falls apart on Tuesday’s real inbox. We build the other kind: agents wired to your actual systems through permission-scoped tools, checked by guardrails and evals, with a human approving anything irreversible. Narrow mandate first, widened as trust is earned.
Proof: SchoolWare — ours — lets staff run a school day from Claude or ChatGPT.
What we build
Agents for the work your team repeats, plus the guardrails, evals, and wiring that make them safe to trust with it.
Operations agents
Tickets triaged, invoices processed, schedules maintained — the repetitive decisions your team makes a hundred times a day, made consistently.
Intake & research agents
Inbound leads qualified and routed with a drafted reply; markets and documents summarized into briefs your team actually reads.
Tool wiring via MCP
Agents connect to your CRM, database, and internal APIs through MCP servers we build — the same standard our own products run on.
Guardrails & approvals
Validation on every action, permission scopes the agent cannot exceed, and human sign-off exactly where the cost of a mistake is real.
Evals & monitoring
Scripted test scenarios run before every change, plus production monitoring — so quality is measured, not vibes-based.
Copilots for your product
Agentic features inside your own SaaS — the “do it for me” button your customers are starting to expect.
Is an agent right for your problem?
When an agent is the right call
The work is high-volume, follows rules with judgment at the edges, and has a clear definition of done — triage, intake, drafting, reconciliation. Those are agent-shaped problems with measurable payback.
When it isn’t
If the process itself is undefined — five people doing it five ways — an agent just automates the confusion. We’ll say so, help you pin the process down, and only then build. Sometimes the honest answer is a form and a cron job.
An engagement like this
A services firm points an intake agent at its shared inbox. Every inquiry gets read, qualified against the firm’s criteria, logged in the CRM, and answered with a drafted reply a human approves in one click. The team’s morning email hour becomes ten minutes of reviewing instead of typing.
Questions we hear a lot
What’s the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent does work: it reads inputs, makes decisions against your rules, and takes actions in your systems — creating records, drafting replies, moving tickets — with a human approving where it matters. If the output of the work is an action rather than an answer, you want an agent.
How do you stop an agent from doing something wrong?
Three layers: tools are permission-scoped so the agent physically cannot exceed its mandate; guardrails and validation check every action against your rules before it executes; and human-in-the-loop approval gates the irreversible steps. We also run evals — scripted test scenarios — before and after every change.
Which AI models do you use?
Whichever fits the job and your constraints — Claude, GPT, or open-weight models where data must stay in-house. We design agents so the model is swappable; the value lives in the tools, guardrails, and workflow, not in one vendor’s API.
Is our data safe?
Your data stays in your systems; the agent reaches it through scoped, audited tool calls, and we configure model providers with training-on-your-data disabled. Where requirements are stricter, we deploy on infrastructure you control.
How long until an agent is doing real work?
A single-workflow agent — say, lead triage or invoice intake — typically reaches supervised production in 3–6 weeks. We start narrow, measure, then widen its mandate as trust is earned.
Which workflow should an agent take first?
Bring us the annoying one. We’ll scope the agent, the guardrails, and the payback — free, in plain English.