Group 01
Submission pipeline
Field re-keying twenty-two times faster, with a 94% extraction match against reviewer truth. Per-field confidence scoring with a reasoning trace.
Read the submission pipeline case studyCase Studies
Thirteen production AI workflows.
One operations team.
Each case is a discrete workflow we shipped — file extraction, role-scoped assistants, autonomous queue sweeps, browser automation. Real vendors, real metrics, anonymized customer.
Customer is "Meridian Operations." If a flow looks like one of yours, it probably is.
New here? Start with the 7-minute walkthrough of the full system
Group 01
Form-extraction pipelines
Documents come in as attachments. They go out as structured rows in a system of record. File-type detection, reference enrichment, and self-improving via reviewer corrections.
Group 02
Role-scoped AI assistant
One chat surface, five specialists. Each role has its own tool access and its own scope, designed so a specialist can't act outside its domain.
Group 03
Autonomous & infrastructure patterns
The queue checks itself. The mirror prevents rate limits. The browser handles systems without an API. Patterns that run without a person in the loop.
Have a workflow shaped like one of these?
If you have a queue where a person reads a document and types it into another system, or a chat surface that needs role-scoped tool access, the same patterns fit. We'll scope it on a 30-minute call.