Where are our people re-keying data that a machine should be moving?
We engineer automations for hours-heavy work such as re-keying, reporting, payroll and document routing, put them into production, and keep them running.
How it works: five steps, one Principal Engineer
One Principal Engineer leads it from the first call until your team has adopted the work and trusts it. Each step shows what it means for this engagement.
- Step 01
Discovery Call
30 minutes with the Principal Engineer who would lead this engagement. Free, and no sales handoff.
- Step 02
Direct Assessment
Find the hoursMeasure where the time goes.
- Step 03
Shared Roadmap
DesignPick the automation and the human checkpoints.
- Step 04
PE-Led Delivery
Build and run in parallelCompare against the manual process.
- Step 05
Outcome Assurance
OperateMonitor, maintain and extend.
What changed for a client
Vizius streamlined timekeeping entry and analysis for an industrial services company consolidating acquired shops onto one timekeeping process.
Ready when you are
Thirty minutes with the Principal Engineer who would lead the work. Pick a time on the spot, or email us.
Prefer email? Send us a note instead.
What you get
- Automations for re-keying, reporting, payroll consolidation and document routing
- Plain-English questions over ERP and financial data (formerly Chat With Your Data)
- Production deployment with monitoring and human review where it matters
- Ongoing engineering as processes change
A Principal Engineer, not a salesperson
Likely lead: David Hyde-Volpe or Jared Sutton. You talk to the engineer who does the work on Back-Office AI Automation.
Questions buyers ask
Is this AI or ordinary automation?
Both. We use language models where they help, such as reading documents, and plain code where it is cheaper and more reliable.
How do you keep errors out of payroll or finance?
Parallel runs against the manual process and human review steps before anything goes live.
What happened to Chat With Your Data?
It is now part of this offering: plain-English access to your data, built and run in your environment.
More detail
Most AI value sits in the hours your team spends moving data between systems. We engineer automations for that work, put them into production, and keep them running as your processes change. That includes plain-English access to your own data.
Related offerings
- Relevant
AI Proof of Concept
"Will this work on our data before we bet a budget on it?"
- Relevant
AI Readiness Assessment & Roadmap
"Which AI ideas will actually pay back here, and in what order?"
- Relevant
AI Governance & Guardrails
"What are our people already putting into AI tools, and what stops sensitive data going with it?"
Not sure this is the right fit?
Take the blind spot check. Four questions to start, then go deeper on one dimension.
Take the check
