AI · Deep dive 03
AI-Powered Automation
Where rules can't reach, we wire AI in with the guardrails production deserves. Routing, extraction, summarisation, decision support — leverage that compounds, not demos that rot.
Scope
AI-augmented operational automation: classify, extract, summarise, route, decide. Similar to AI-assisted ops on the automation track, with extra emphasis on agentic patterns and multi-step workflows.
Does this sound familiar?
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Support triage takes someone four hours a day and still misclassifies.
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Contracts arrive as PDFs and someone copy-pastes fields into the CRM.
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Every compliance-flag case gets a manual review because the rules engine can't handle nuance.
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Sales reps spend an hour a day turning calls into notes.
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You've seen vendors demo 'agents' and don't know which tasks are actually agent-shaped.
The customer payoff
What changes
What you feel once it’s running.
Hours reclaimed on the tasks AI is actually good at.
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Error-rate reductions on tasks where rules-engines misclassified.
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Cost + quality visible — you know what you're paying for what.
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Fallbacks in place so low-confidence outputs escalate, not slip through.
Phases
⏱ 4–10 weeks typicalHow AI-Powered Automation actually runs.
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01
Map the tasks
Which tasks are classification, which are extraction, which are generation, which are agentic? Different patterns want different tools."
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02
Prototype
Days. Test against real production examples. Reject what doesn't hit the quality bar."
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03
Guardrail
PII filters, content checks, confidence thresholds, human-in-the-loop escalation. Guardrails are the difference between demo and production."
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04
Deploy + watch
Shadow-mode first. Cut over when metrics hold. First 30 days of production monitored closely."
The hand-off
You'll have
What lands in your hands — every artefact, nothing hidden.
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2–4 production-grade AI workflows
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Evaluation suites for each
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Guardrails (PII, content, confidence) documented + tested
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Cost + quality dashboards
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Human-escalation paths for low-confidence outputs
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Maintenance runbook
Common questions
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Q·01 When is agent appropriate vs simple LLM call?
Simple call when the task is one-shot (classify this, summarise that). Agentic when the task requires multiple steps with intermediate tool use (look up customer, check inventory, draft email). We'll tell you which applies."
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Q·02 Do you fine-tune models?
Rarely. Modern base models + good prompts + RAG cover 95% of use cases. Fine-tuning is 3x the cost and rarely warranted. We'll say so if it is."
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Q·03 What about PII?
Scrubbed before it reaches the model by default. We can route through your gateway or self-hosted model if the data class requires it."
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Q·04 Can we test these before they go live?
Yes — we build eval suites that run against real historical data. You see accuracy numbers before cutover."
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Q·05 How do we stay in control of spend?
Token budgets per workflow with alerts. We cap before anything breaks the bank."
Ready to start
AI where the work actually is.
Shortlist of the right workflows, prototypes in days, production in weeks. Start with the task that hurts most.
Start an AI automationThe 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 →AI Integration
Service overview →- 01 AI Opportunity Mapping
- 02 AI-Driven Product Features
- 03 AI-Powered Automation — you’re here
- 04 Evaluations, Guardrails & Observability
- 05 Vendor-Neutral Integration