AI Automation
Automation that removes the retyping.
The valuable AI work in most businesses is not a chatbot. It is the invoice that gets rekeyed into three systems, the quote that waits two days for a summary, the inbox someone triages by hand every morning. We find those, automate them, and keep a person in the loop where a wrong answer would cost something.
What you end up with
- Each automation scoped against measured hours, not estimated ones
- Human review retained wherever an error carries real cost
- Every automated decision logged and auditable
- Runs inside your existing tools rather than adding another portal
Scope
What the engagement covers.
Platforms & tooling
- Claude API
- Power Automate
- Azure AI
- Python
- n8n
- Zapier
- Microsoft Graph
- 01
Process discovery
A short engagement watching how work actually flows, scoring each candidate by time spent, error rate and how tolerable a mistake would be.
- 02
Document and email processing
Extraction from invoices, purchase orders, forms and contracts into your existing systems, with confidence thresholds that route uncertain cases to a human.
- 03
Internal knowledge assistants
Retrieval-based assistants over your own documentation and ticket history, scoped to existing permissions so nobody sees what they should not.
- 04
Workflow integration
Connecting the automation to the tools you already run — Microsoft 365, your CRM, your accounting package — rather than adding another place to check.
- 05
Evaluation and guardrails
A measured accuracy baseline, logged decisions, an audit trail, and a documented fallback for when the model is unsure.
How it runs
Four phases, agreed before we start.
- 01
Assess
Output: environment map, risk list, fixed-price proposal.
- 02
Plan
Output: runbook, comms templates, rollback criteria.
- 03
Execute
Output: live status channel, step-by-step verification log.
- 04
Support
Output: reconciliation report, documentation, handover session.
Questions
About ai automation.
Does our data get used to train someone else’s model?
No. We build on enterprise API tiers where inputs are not used for training, and we document the data path so you can show it to your own auditors.
What if the model gets something wrong?
That is a design question, answered before we build. Low-stakes steps run automatically; anything with financial or legal consequence routes to a person, with the model’s confidence attached.
How do you know it is actually saving time?
We measure the manual process first. Without a baseline there is no way to prove a return, and a lot of automation projects quietly skip this step.
Also from Queue Bytes
Other things we do.
Email Migration
Move mail, calendars and contacts without losing a message.
ExploreManaged IT Support
A help desk that answers, plus the maintenance behind it.
ExploreMicrosoft 365 Deployment & Audits
Deploy it properly, then prove the tenant is actually secure.
ExploreGoogle Workspace
Deployment, hardening, and moves in either direction.
ExploreTalk to someone who has done ai automation before.
A short call to understand the environment, then discovery if it looks like a fit. You will get a written proposal either way.