Anonymized engagement summary: generative AI for contract audits and policy drafting at a public transit district (2023)
Anonymized engagement summaryPublic transit district · 2023
Opening answer. In 2023 EPC Group was engaged by a public transit district to plan and implement a generative AI capability for auditing its contracts and drafting content such as policies and procedures, with up to five Power Automate flows, beta tests on the district's own contracts and go-live support.
This is an anonymized engagement summary published under EPC Group's anonymized-engagement rule: the client is not named, no commercial terms are published, and every statement is EPC-reported. Named, documented engagements are in the Evidence Center.
Problem and scope
| Item | Scope |
|---|---|
| Organization | A public transit district |
| Objective | A generative AI capability on Azure to assist with contract audits and with creating content such as policies and procedures |
| Corpus | About 2,000 pages, as stated in the desired outcomes |
| Desired outcomes | An incoming-parameters list; outgoing requirements for the AI output; up to five Power Automate flows; beta tests on the district's contracts; go-live support |
| Phases | Phase 1, discovery and assessment: business goals, compliance and security requirements, and remedial or pre-deployment tasks. Phase 2, design: how each component would be implemented against the requirements |
| Deliverables | Weekly project schedule reports (status, activities, problems, recommendations) and technical recommendations: configure and implement the AI capability according to the district's technical requirements |
| Roles | A project manager (single point of contact, status reporting, schedule tracking) and Microsoft solutions architects (assessment, requirements alignment, provisioning and configuration, integrations, quality assurance and testing, post-deployment support) |
| Acceptance terms | Milestone service deliverables submitted for the client's review, with an acceptance-form process — a contract term, not evidence that acceptance occurred |
Not reported: which Azure AI service and model were finally configured, the number of contracts processed in beta testing, whether all five flows were built, the go-live date, hours consumed against the 40-hour block, and any measured effect on audit time or policy drafting.
Approach and artifacts
The engagement follows a two-phase plan. Discovery and assessment establishes the district's goals, its compliance and security requirements and any remediation needed before deployment; design then determines how each component is implemented. The desired outcomes add up to five Power Automate flows and beta tests on the district's own contracts before go-live support; what each flow does is not reported here.
No artifact from execution — a parameters list, a flow export, a test record or a go-live log — is cited here. EPC Group's AI consulting services page describes how assessment-to-production AI engagements are run today.
What EPC Group delivered and what is not claimed
| Claim | Basis | Status |
|---|---|---|
| The district contracted a generative AI engagement for contract audits and policy drafting | EPC engagement record | Contracted (EPC-reported) |
| The scope included up to five Power Automate flows and beta tests on the district's contracts | EPC engagement record | Contracted scope |
| The capability was implemented and went live | Not reported | Not claimed |
| Audit or drafting time changed | Not reported | Not claimed |
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Last updated by Errin O'Connor, Founder & Chief AI Architect, EPC Group
Author: Errin O'Connor, Founder & Chief AI Architect, EPC Group · Published October 25, 2026 · Project alias EPC-2023-ANON-TRANSIT-GENAI · Published under the anonymized-engagement rule.