Tool calls that fail silently, orchestration state that drifts under load, costs that spike with no warning — none of it shows up in the demo, all of it shows up in production. We run a fixed-scope production-readiness assessment on your existing agent system, then harden what the findings justify.
Whatever the agent does, it runs on the same production frame: guardrails and runtime checks on every request, a human gate where you want one, and an improvement loop that makes week four better than week one. This is the frame the assessment scores your system against.
Read the full build guide: how to build a production-scale AI agent →
A chatbot answers. An agent finishes. We build agents that orchestrate across CRMs, ERPs, document stores and ticketing — taking work from request to resolution.
Same engineering rigour: every step is observable, replayable, and gated by the same governance controls as the rest of the platform.
And when there's a brittle RPA bot in the way, we replace it with an intelligent agent that adapts instead of breaking.
A coordinator and specialised agents that work across all your platforms — CRM, ERP, knowledge, search, files.
Multi-step processes completed end-to-end with deterministic checkpoints and AI judgement where it helps.
Replace brittle screen-scraper bots with agents that adapt to UI change and unfamiliar layouts.
Briefs, decks and summaries assembled from live data — written, checked, ready to send.
Four domains where multi-step automation pays back fast.
Onboarding, dispute resolution, periodic compliance reviews.
Use case: Account-opening agent D02Cross-department routing, document handling, status follow-ups.
Use case: Grievance triage agent D03Plan changes, refund flows, retention offers — completed in the chat.
Use case: Self-serve plan-change agent D04Inspection capture → triage → work-order — without a hand-off.
Use case: Inspection workflow agentDiscovery to scale, engineered for production from day one.
See how we deliver →The assessment instruments and pressure-tests your existing agent system across the failure surfaces that stall most pilots: tool and MCP integrations, orchestration state under load, cost and latency observability, and evaluation coverage against real traffic.
You get a findings report — where the system will break, what it will cost to run, and a prioritized hardening plan covering evaluation harness, observability and failure containment. Implementation follows only if the findings justify it.
A short, no-pitch teardown: the failure modes that stall most agent rollouts, and how a fixed-scope assessment finds them in your system. Then, if it's worth a conversation, a 20-minute look at your stack.
