Five types of agent we ship.
Each one is purpose-built, eval-covered, and handed over with tracing so you know what it's doing in production.
The agentic loop.
Every agent we build follows the same four-phase cycle. It's what separates an agent from a chatbot.
Perceive
Receives a trigger — an inbound email, a form submission, a cron schedule, or a manual API call.
Plan
Breaks the goal into a sequence of sub-tasks and decides which tools to call, and in what order.
Act
Executes — calling APIs, querying databases, writing outputs — and self-corrects when a tool returns an error.
Report
Logs the full run, returns the result, and escalates to a human if confidence falls below the threshold.
Agents built for your industry.
We adapt the same agentic architecture to whichever industry you operate in.
What we build with.
Provider-agnostic from day one — swapping a model or vector DB doesn't mean rewriting your agent.
Agents we've shipped.
Common questions.
Still have questions? Send us a message.
Comparing providers? See our roundup of the top AI agent development companies in 2026.
Work in a specific sector? Browse AI agents by industry.
A custom AI agent is an autonomous system that perceives a trigger, plans a sequence of actions, calls external tools and APIs, and returns a result — without a human in the loop for each step. Unlike a chatbot that answers questions, an agent completes multi-step tasks end-to-end.
A chatbot responds to messages. An agent acts on them — it can search a database, book a calendar slot, update a CRM, send an email, and summarise the outcome, all in one run. Agents are goal-directed; chatbots are response-directed.
Any system with an API: CRMs (HubSpot, Salesforce), calendars (Google, Outlook), databases, Slack, email providers, Twilio for voice, internal REST APIs, and third-party SaaS tools. We build the tool layer as part of every engagement.
A focused single-purpose agent — lead qualification, support triage, voice receptionist — typically takes 6–10 weeks from kick-off to production. More complex multi-agent systems take longer. We scope every project before committing to a timeline.
Every agent we ship has an eval suite, confidence thresholds, a human escalation path, and full tracing via LangSmith or Langfuse. Agents that aren't confident enough to act escalate instead of guessing. We also run regression evals before each deployment.
We work on a fixed-price proposal basis. After a 20-minute discovery call we scope the agent, define success criteria, and send a proposal. There's no cost to the discovery call.
Who leads this work
AI Agent & ML Engineer
Builds AI agents that reason, call tools through MCP, and remember context — and the ML work behind them, from model selection to evaluation and fine-tuning.