How is an AI agent different from a chatbot?

A chatbot answers questions in a conversation. An agent plans a sequence of actions, calls tools or APIs, checks the results, and keeps going until the task is actually complete.

Can an agent take actions in our live systems?

Yes, with scoped permissions and approval checkpoints you define. We start in a sandbox and graduate to production access only once you're confident in its behavior.

What happens when the agent isn't sure what to do?

We build explicit escalation paths — the agent pauses and routes the task to a person rather than guessing on anything consequential.

Which platforms do you build on?

We work with leading LLM providers and agent frameworks, choosing the stack that fits your latency, cost and data residency needs.

How long does a first agent take to build?

A focused, single-workflow agent typically takes a few weeks from scoping to a production pilot.

AI Solutions

AI Agents That Get Work Done

We build autonomous agents that plan, reason and act inside your existing tools — completing real tasks, not just answering questions.

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Beyond chat: agents that act

Most teams already have a chatbot. Few have an agent that can actually finish a job. We design AI agents that break a goal into steps, call the right tools and APIs, check their own work, and escalate to a human only when judgment is genuinely required — so your team spends time on outcomes, not babysitting software.

What we build

Multi-step task agents

Agents that plan and execute workflows spanning several tools and decisions.

Tool & API orchestration

Safe, scoped access to your CRM, ERP, helpdesk and internal APIs.

Human-in-the-loop controls

Approval checkpoints and audit trails for sensitive actions.

Memory & context

Agents that retain relevant history across sessions and tasks.

Observability

Full logs of every decision and action an agent takes.

How we build your agent

Step 1

Map the workflow

We identify the tasks worth automating and the decisions that need human review.

Step 2

Design the agent

We define tools, guardrails and success criteria before writing a line of code.

Step 3

Build & integrate

We connect the agent to your real systems in a sandboxed environment first.

Step 4

Test under load

We run the agent against real scenarios and edge cases before go-live.

Step 5

Deploy & monitor

We ship with logging and alerting, then tune based on real usage.

Frequently asked questions

Ready to put AI to work?

Book a free consultation and we'll map the highest-impact AI opportunity for your business.

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