AI Automation
Some workflow steps need judgement, not just routing — reading an invoice, triaging an inbox, drafting the reply. That is where a model earns its keep, carefully.
Ordinary automation routes data; AI automation understands it. The invoice PDF becomes structured fields, the inbound email becomes a classified, routed ticket with a drafted reply, the messy form submission becomes a clean CRM record. These judgement steps used to require a human in the loop; a well-engineered model step now handles the volume while humans keep the exceptions.
The engineering that separates useful from embarrassing: prompts treated like code — versioned, tested, evaluated against real examples; cost ceilings per run so enthusiasm cannot outrun the budget; confidence thresholds that route uncertain cases to humans instead of guessing; and full logging of what the model saw, decided and produced. Every AI step sits inside the same error-handled, monitored workflow discipline as everything else.
This practice builds AI automation where it genuinely pays — triage, extraction, classification, drafting, summarisation — and refuses it where it does not. If your step is deterministic, you get a cheaper deterministic answer; if it needs a model, you get one with guardrails. The scoping conversation sorts that honestly before any build.
What the engagement covers
- Candidate mapping: which workflow steps genuinely need judgement versus deterministic routing
- Model-step engineering: versioned prompts, structured output validation, retry and fallback paths
- Evaluation set from your real cases — quality measured before the step goes live
- Cost ceilings per run with usage tracking and alerts on drift
- Confidence thresholds routing uncertain cases to a human review queue
- Full logging — inputs, outputs and decisions inspectable per run
- Human-in-the-loop checkpoints on anything customer-facing or irreversible
Honest limits
What this is deliberately not.
Not this: Deterministic steps wearing a model — if a rule does it, the rule wins on cost and reliability
Not this: Unreviewed AI output sent to customers — drafts and classifications yes, final voice no
Not this: Decisions with legal or financial finality — prepared by the system, signed by a person
Not this: Model steps without evaluation — "it looked fine in the demo" is not a deployment standard
Questions · AI automation
Asked before building.
Judgement. Workflow automation moves and shapes data along fixed rules; AI automation adds steps that read and interpret — extracting, classifying, drafting. Most real systems mix both: deterministic rails with model steps where the content is messy. The scoping decides which steps earn the model.
Measured on your cases, not quoted from a brochure: the evaluation set scores accuracy before anything goes live, and the number is reported. Messy inputs lower it — which is why confidence thresholds route the uncertain tail to humans. You get the real number per step, and the improvement path with it.
Usually cents per call at small-business volumes — pennies for an email classification, a bit more for document extraction. The build sets per-run ceilings and tracks actuals against projections; the surprise-invoice failure mode is a design flaw this practice does not ship.
Draft, yes; send unreviewed, rarely. The standard pattern: the model drafts, a human approves with one glance, the system sends. For low-risk, template-shaped replies, auto-send can be earned per category over time as accuracy proves out — the trust is granted gradually, on evidence.
Related: all automation services · AI services · the contract guide.
Also in this section
Workflow Automation · Business Process Automation · CRM Automation · Ecommerce Automation · Zapier to n8n Migration
Scoping something in this space?
Written scope within two business days — deliverables, milestones, timeline, terms, price at the bottom. Compare it against anyone.