Assessment
AI workflow fit assessment.
Use one painful deal workflow to decide whether automation is worth pursuing, what should stay human-reviewed, and what a production sprint must prove before go-live.
Start with the workflow, not the tool.
The assessment looks at the source material, manual review path, security boundary, and output format before recommending a build.Source Material
Which files, data rooms, evidence sets, exports, policies, contracts, or ERP tables does the workflow depend on?
Manual Review Path
Who prepares the first pass, who checks it, what errors matter, and where do senior reviewers lose time?
Security Governance
What data sensitivity, access rules, deployment boundaries, retention requirements, and audit expectations apply?
Output Fit
What should the system produce: issue list, workpaper draft, evidence queue, SQL-backed answer, memo, or dashboard?
What comes next
A practical recommendation before you fund the build.
We map the baseline, source boundary, reviewer path, acceptance criteria, and implementation risk, then recommend a production sprint, mapping first, or no build.
Workflow intake
Understand the bottleneck in detail
Sprint shape
Define baseline, sources, reviewers, and output
Next step
Production sprint, mapping first, or a no-fit answer
OpenAI partner network
Build with an OpenAI Select Partner.
Recognition supporting our work helping organizations build, deploy, and scale production AI systems with OpenAI.Ready for a production decision