Automation · Deep dive 02
AI-Assisted Operations
Where rules can't quite reach, AI earns its keep. LLM-powered routing, summarisation, decision support, triage — embedded where they actually boost throughput, with guardrails.
The scope
An engagement to identify ops tasks where AI beats rules, then ship production-grade automation with evaluation harnesses, cost controls, and graceful fallbacks. Not a demo; leverage that ships.
Does this sound familiar?
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Inbox triage is someone's full-time half-job, and the rules engine can't capture the nuance.
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Support tickets get routed wrong 20% of the time and the team re-routes manually.
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Sales or customer success reps spend an hour a day summarising calls into the CRM.
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You've tried 'just ChatGPT' for ops tasks and it works 70% of the time, which isn't enough.
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The team has opinions about what AI could help with but no shared map of where it actually pays off.
The customer payoff
The payoff
What you feel once it’s running.
A short list of AI-augmented workflows with measurable hours-saved and quality floors you can defend.
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Guardrails + human-in-the-loop patterns so low-confidence outputs get escalated, not yeeted.
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Cost dashboards — token spend per workflow, capped and alerted.
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An evaluation harness so you know when a prompt degrades before your users do.
Phases
⏱ 4–8 weeks typicalHow AI-Assisted Operations actually runs.
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01
Map
Shadow the ops team. Score tasks on AI-suitability (pattern vs rule, ambiguous vs deterministic) and cost (time spent). Top 3–5 surface fast."
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02
Prototype
Build minimal versions in days, not weeks. Test against real examples. Reject the ones that don't hit the quality bar; keep the survivors."
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03
Harden
Evaluation suites, guardrails (PII filters, content checks), fallbacks, logging. Production rigour for the survivors."
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04
Deploy
Roll out behind feature flags, shadow-mode first, full cutover once the metrics hold."
The hand-off
You'll have
What lands in your hands — every artefact, nothing hidden.
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2–4 AI-augmented workflows in production
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Evaluation suite for each (prompt regression testing)
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Cost + quality dashboard per workflow
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Fallback paths — human escalation or deterministic
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Playbook for adding the next AI-augmented workflow
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Security + data-residency review documented
Straight questions
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Q·01 Which models do you use?
Depends on task. Claude (Anthropic) for nuanced judgement and long context. GPT-4o-class for speed + structured output. Open-weights (Llama, Mistral) when data residency or cost demands it."
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Q·02 What about our data leaving the company?
Reviewed per workflow. We use zero-retention APIs where available, route through your own gateway if you prefer, and choose self-hosted models when the data class demands it."
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Q·03 How accurate is 'production-grade'?
It depends on the task — but we set a target quality floor before building and don't ship below it. Anything that doesn't clear the floor gets rejected or gets a human escalation path."
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Q·04 What happens when the model gets worse after an update?
Evaluation suite catches it. We run prompt regression tests on every model change and cut over only if the metrics hold."
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Q·05 Will our team be able to maintain these?
We structure for that. Prompts live in versioned files, evaluations run in CI, and we document the 'when to escalate to Mashed' boundary."
Ready to start
AI where it actually pays.
Two-day shadow of ops work, honest shortlist of the AI-augmented wins, clear path to production. Let's see what's worth building.
Start an AI ops engagementThe wider map
Every service page at a glance.
Each link below opens a dedicated page on that specific piece of one of our four service pillars. Jump sideways — different service, same way of working.
Digital Product Strategy
Service overview →Web & Mobile Development
Service overview →Business Automation
Service overview →- 01 Workflow Automation
- 02 AI-Assisted Operations — you’re here
- 03 Process Digitisation
- 04 Custom Internal Tools
- 05 System Integration & APIs
- 06 Data Pipelines & ETL