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Platforms & Emerging Tech

AI agents that do real work, with oversight

We build AI agents that read your documents, use your tools and complete multi-step tasks, with the evaluations, permissions and human checkpoints a real business needs.

AI Agent Development

What you receive

  • Use-case brief and success metrics
  • Evaluation suite and quality benchmarks
  • Production agent and tool integrations
  • Guardrails and approval workflows
  • Monitoring for quality, latency and cost
  • Operating guide and improvement plan

Our approach to AI Agents

A demo agent takes an afternoon. A production agent has to behave predictably when inputs are odd, tools fail or someone tries to manipulate it. Most of our effort goes into that second part: evaluations, permissions, observability and graceful handoff to a person.

We start with one narrow, measurable workflow and expand from there. Agents get the minimum access they need, high-impact actions require approval, and every decision is logged so you can see exactly what happened and why.

This is likely a fit if…

  • Teams spend hours on repeatable work that still needs judgment
  • Your support queue is full of questions your documentation already answers
  • A chatbot pilot impressed people but never reached production
  • You need AI on private data with clear access control and audit trails
Capabilities

What’s included

Workflow and task agents

Agents that plan and carry out multi-step work across your business systems.

Support and service agents

Grounded answers, real account actions and clean escalation to your human team.

Knowledge and RAG

Permission-aware retrieval over your documents with citations users can check.

Tool and MCP integration

Secure connections to CRMs, databases and internal APIs through scoped tools.

Evaluation and testing

Regression suites using real examples, run before every prompt or model change.

Guardrails and oversight

Prompt-injection defences, approval gates, usage limits and complete audit logs.

The engagement

How we deliver it

  1. 01

    Use-case selection

    We pick one workflow with clear value and define what success means numerically.

  2. 02

    Evaluation first

    A test set from real examples is built before the agent, setting the quality bar.

  3. 03

    Build with guardrails

    Tools, permissions and human checkpoints are designed in, not added at the end.

  4. 04

    Supervised rollout

    Shadow mode, then limited release, with monitoring of cost, quality and escalations.

Tools and platforms

  • Claude
  • OpenAI
  • Model Context Protocol
  • LangGraph
  • pgvector
  • Python
  • FastAPI
  • Langfuse
  • Temporal
FAQ

AI Agents questions

The things founders and leadership teams actually ask us, with honest answers. Anything else, we’re one message away.

Ask your own question
  • No agent is perfect, so we design for that. Agents get limited permissions, irreversible actions need human approval, evaluations catch regressions, and low-confidence cases are escalated to a person.

Ready to talk about AI Agents?

Book a free consultation. We’ll scope your project, pressure-test the plan and give you an honest estimate, with no commitment required.