OpsIQ
A production AI operations layer, not just an assistant.
OpsIQ handles the repetitive, time-sensitive work that fills an operations day — customer conversations, inspections, contractor coordination, staff check-ins — and routes the exceptions to a named person. It is designed for control, not chaos: policy-backed answers, explicit routing, audit trails and human escalation built in. We built it in production for a UK operations business and tailor it to yours.
Who it's for
- Property and facilities management
- Field services, maintenance and inspection
- Multi-site hospitality
- Home services and trades
- Logistics and distribution
- Franchise and multi-branch operations
Operations work is fragmented, time-sensitive and hard to see.
Customers ask questions, report faults and chase updates around the clock. Teams have to separate a genuine maintenance issue from a service query, log the job correctly, gather photo evidence, coordinate a contractor and escalate anything urgent. Inspections generate volumes of photos and notes that turn into consistent action slowly, if at all. Managers need a real read on workload, blockers and risk, but most check-ins happen informally and disappear into chat.
The instinct is to bolt a chatbot onto the front of it. That solves the wrong problem. A chatbot answers questions; an operations business needs something that follows its rules, triggers real work, records what it did, and steps back when a human should take over. OpsIQ is built as that operating layer, not as a front-desk demo.
What it does
What OpsIQ does
Five working layers, each attached to a real operational outcome rather than a conversation.
Customer-facing messaging agent
Works over the messaging channels your customers already use. It classifies each conversation before replying, answers only from approved policy, asks for photo evidence, and creates jobs, complaints or escalations. In sales mode it qualifies enquiries against live availability and hands to a human with the full context, rather than dropping a cold lead into a shared inbox.
Inspection intelligence
AI vision checks whether a field photo is usable, interprets the evidence conservatively, and turns it into structured jobs. It is deliberately not reckless: the prompts are tuned to avoid mistaking shadows for damp or untidiness for damage, and to never make accusations about a resident or customer.
Contractor coordination
Handles outreach, offers slots, confirms bookings and chases completion — then escalates to a person when a job stalls rather than letting it drift.
Voice check-ins with staff
Runs structured voice check-ins with team members, transcribes and analyses them, and produces tiered reports from individual summaries up to team and director roll-ups, with action items and risk flags surfaced rather than lost.
Leadership Q&A over your data
Lets leaders ask questions of operational data through a constrained, read-only query layer — forced read-only access and statement timeouts — so the experience is conversational while the access stays bounded and auditable.
How it's governed
Why it is safe to put in front of customers
The governance is in the code, not the brochure. This is the part most AI operations tools skip.
Policy-driven answers
The customer-facing agent pulls approved policy content instead of answering from memory, which matters where deposits, repairs, approvals and complaints are concerned.
Fail-closed authorisation
Actions pass through authorisation gates that deny by default. An action lease and stale-action aborts stop the system acting on a message that has already moved on.
Feature-flagged rollout
Every capability can run observe-only before it is armed, so you watch it work on your real traffic before it sends anything. Working-hours windows and escalation thresholds are configurable, not hard-coded.
Human approval and takeover
Campaign messages need human approval before they go out. An admin console gives dense tables, first-class recovery screens and a clean route for a person to take over a conversation.
Structured audit logs
The platform records messages, workflow stages, action leases, timeline events and structured results rather than hiding behaviour inside opaque prompt logs — so you can show what happened and why.
The evidence
Evidence from production
Figures from live operation of the system at an anonymised UK operations business, over representative windows. Yours will differ; these show the system works at real volume.
- 24,676
- customer messages handled across 940 conversations
- 5,164
- logged AI actions, 99.23% outbound send success
- ~20,175
- inspection photos analysed, 1,313 jobs created
- 848
- action items extracted from 142 voice check-ins
Measured over representative seven-week windows in production at an anonymised UK operations business, self-reported from the system's own logs (spring 2026). Illustrative of throughput and control, not a performance guarantee for your deployment.
See OpsIQ running against your operation.
Fifteen minutes with the people who built it — not an account manager — about where a governed AI operations layer fits, and where it should not.