Services · Intelligence
Agentic AI products that do real work
An agent reads the situation, makes a plan, uses your tools and finishes the job, with a human approving anything that matters. We design and build these products for production.
Why it matters
Chatbots answer questions but leave the work to your team. Most AI pilots never reach production because reliability, cost and safety were not designed in.
Multi-step work completed end to end, not just suggested
Human approval on high-risk actions, by design
Evaluations and guardrails before launch
Model-agnostic: swap providers without a rewrite
What's included
End to end, one team.
- Agent architecture
- Planning, memory, tool use and multi-agent orchestration that fits the task.
- Tool & MCP integration
- Connect agents to your CRM, email, docs, databases and internal APIs through secure tools and MCP servers.
- Human-in-the-loop
- Review queues, approvals and clear audit trails for every agent action.
- Evaluation
- Test suites and scorecards that measure accuracy, cost and latency on your real data.
- Guardrails & security
- Permission scopes, prompt-injection defences, PII handling and rate limits.
- Agent UX
- Interfaces that show progress, reasoning and results your team can trust.
How it works
Four steps to launch.
- 01
Find the workflow
Pick one repetitive, high-value process and measure it today.
- 02
Pilot in 4–6 weeks
Build a working agent on real data with humans reviewing every output.
- 03
Prove it
Compare accuracy, time and cost against the baseline.
- 04
Scale
Harden, monitor and roll out to more teams and workflows.
FAQ
Good questions
What is the difference between a chatbot and an agent?
A chatbot replies to messages. An agent takes actions: it reads data, calls tools, makes decisions within limits you set and completes a task.
Which AI models do you use?
We are model-agnostic. We choose between Anthropic Claude, OpenAI and open-weight models based on accuracy, cost, latency and data requirements, and design so you can switch later.
Is our data used to train public models?
No. We use enterprise API terms or self-hosted models so your data is not used for training, and we can keep data in your own cloud.
How do you stop agents making mistakes?
We scope permissions tightly, add human approval for risky actions, run evaluations before launch and monitor every run in production.
Let's build
Have a project in mind?
Tell us what you're building. We reply within one business day.