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
- Document processing: invoices, receipts, purchase orders and messy PDFs turned into structured, validated data your accounting or ERP system can ingest
- 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.
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Workflow Automation · Business Process Automation · CRM Automation · Ecommerce Automation · Zapier to n8n Migration
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