AI workflow automation

What it is

Automations that take a trigger — an email, a form, a new record — and carry it through classification, drafting, system updates and notification.

Why businesses need it

The expensive part of operations is handoff: copying information between systems and re-reading it. Automation removes the copy step and keeps the judgement.

What MIWEBX delivers

• Document intake: extraction, classification and routing
• Lead and support triage with enrichment before it reaches a person
• Report and proposal drafting from live data
• Cross-system sync where no native integration exists
• Scheduled back-office jobs with failure alerting

Core features

• Deterministic steps first; AI only where it adds value
• Confidence thresholds that decide what needs a human
• Retry, replay and idempotency on every external call
• Full audit trail of what was read, decided and written
• Kill switch and dry-run mode for new automations
• Cost per run tracked next to time saved

Technical capabilities

• Pipelines built as explicit stages — trigger, extract, decide, act, notify — each independently testable
• Rules and schema validation first; a model call only where a rule cannot decide
• Model sits behind the stage interface, so the classifier can change without touching the flow
• Idempotency keys, retries with backoff and replay for every external write
• Confidence mapped to a routing rule: complete, queue for review, or fail to a named owner
• Structured audit record of inputs read, decision taken and fields written
• Dry-run and kill switch per automation, with failures alerted into existing ops channels

How we build it

• Trace the process as it actually runs, including its exceptions
• Agree the trigger, the data each stage may read and the systems it may write
• Automate the deterministic stages first and leave judgement manual
• Add model stages only where they clearly beat a rule, each with a threshold
• Dry-run against live traffic and compare with what staff did by hand
• Go live with a review queue, audit log and a monitored failure path

Technology stack

• Python
• Node.js
• TypeScript
• OpenAI APIs
• Webhooks
• PostgreSQL
• Docker

What you receive

• Live automations with their trigger and destination systems mapped
• Review queue and escalation rules for low-confidence runs
• Audit log and monitoring dashboard
• Documentation of every decision rule in plain language
• A list of what was deliberately left manual, and why

Business benefits

• Staff stop re-typing the same information twice
• Turnaround measured in minutes rather than days
• Exceptions surface immediately instead of at month end
• Process survives someone leaving