AI agents that finish the work — not just suggest it.

Goal-driven agents that plan, call your tools, browse, write, and report back — with proper guardrails, audit trails and human approval where it matters.

Overview

From “chat” to “done”.

A chatbot answers a question. An agent takes the next steps: opens the ticket, checks the inventory, drafts the reply, gets it approved and sends it — while leaving a clean trace of what it did.

We design agents around a specific job-to-be-done. We define what they can and can’t do, give them sharp tools, and put humans in the loop for the moments that matter. The result feels like a fast, careful junior teammate — not magic.

Scoped, not god-likeEach agent owns a specific workflow with explicit boundaries.
Auditable by designEvery step, tool call and decision is logged and reviewable.
Human-in-the-loopApproval gates for any action that touches money, customers or data.
RecoverableStuck? The agent escalates rather than guesses.
AGENT TYPES
  • Workflow agents (multi-step business tasks)
  • Research agents (gather, synthesise, cite)
  • Support agents (resolve tickets end-to-end)
  • Sales & outbound agents (qualified outreach)
  • Operations agents (back-office automation)
  • Coding & review agents (internal dev tooling)
Capabilities

What goes into a real agent.

Planning & reasoning

Decompose a goal into steps, choose tools, recover from errors and explain what it’s doing.

Tool use

Curated tools wrapping your internal APIs, databases, knowledge bases and external SaaS.

Memory & context

Short-term and long-term memory so the agent learns what’s important across sessions.

Guardrails

Allow / deny rules, rate limits, spending caps, content filters and explicit approval steps.

Observability

Per-step traces, costs, latencies and outcome metrics — so you can debug and improve.

Multi-agent design

Specialist agents coordinated by a planner, with clear hand-offs and shared workspace.

Process

From use case to a working agent.

Map the workflow

We document how the task is done today, by whom, with what tools — and where it commonly breaks.

Design the agent

Roles, tools, prompts, memory, guardrails and approval points. We pick the smallest possible scope first.

Pilot & evaluate

Run on shadow mode against real cases. Compare outcomes to humans. Iterate the prompt and tooling.

Roll out & expand

Move from shadow → assisted → autonomous on the cases where the agent has earned trust.

Use cases

Where agents already pay back fast.

Tier-1 support resolution

Agents that read the ticket, check the systems, draft the response and complete simple actions — with human approval.

Research & due diligence

Targeted research with structured outputs and citations — from market scans to vendor assessments.

Sales operations

Lead enrichment, CRM hygiene, follow-up drafting and pipeline summaries the team actually uses.

Back-office automation

Invoice processing, document checks, exception handling — with rules and judgement combined.

Internal IT & HR

Self-service agents for access requests, onboarding, FAQ-driven support and policy lookups.

Engineering helpers

Triaging issues, drafting tests, summarising PRs and watching deploys — with the team in the loop.

Get started

Got a workflow that’s a great fit for an agent?

Tell us about it. We’ll come back with whether it’s a fit, what the smallest viable agent looks like, and how to measure it.