From 3–5 Day Regulatory Responses to Under One Day with Agentic AI
A multi-agent regulatory filing platform helped a North American utility reduce evidence search and response effort while preserving legal review, auditability and source traceability.
A Utility Preparing Rate-Case Filings Against Fixed Deadlines
A North American utility with a Rates & Regulatory Affairs function responsible for preparing large rate-case filings against fixed statutory deadlines. The work depended on prior testimony, evidence, workpapers and regulator data requests distributed across several enterprise repositories.
Evidence Was Scattered, and Every Answer Carried Risk
Evidence, prior testimony, data requests and workpapers were distributed across SharePoint, network shares and individual mailboxes. Analysts were spending a significant share of each filing cycle locating precedent, reconciling versions and assembling evidence instead of building the regulatory case.
Fragmented Evidence & Precedent
Evidence, prior testimony, data requests and workpapers were distributed across SharePoint, network shares and individual mailboxes, with no single point of retrieval.
Slow Regulatory Turnaround
A regulator data request typically took 3–5 working days to answer, with analysts spending most of that time assembling material rather than building the case.
Inconsistency & Audit Risk
An answer could be inconsistent with prior testimony, creating avoidable regulatory scrutiny and additional legal review.
Objectives: Reduce data-request turnaround and filing preparation effort without changing the legal review model · Trace every generated answer to a source document so auditability is built into the workflow · Detect potential precedent inconsistency before internal legal review rather than after it.
A Five-Agent Platform on a Hybrid Footprint
The solution was a multi-agent platform on a hybrid footprint. Retrieval, orchestration and inference ran on the client's standardised cloud platform, while SharePoint, identity and business intelligence remained in their existing environments. Five specialised agents separated the workflow into clear responsibilities.
Intake & Classification
Receives the request and classifies the work to be performed.
Precedent Retrieval
Searches a chunked, metadata-tagged corpus of filings and testimony.
Drafting
Produces a draft response grounded in retrieved evidence.
Consistency Check
Compares the proposed response with prior positions and testimony.
Review Packaging
Prepares the output and evidence for the named human reviewer.
Hybrid Retrieval, Full Traceability, Confidence-Based Routing
Retrieval used a hybrid approach — vector search plus keyword search — because regulatory citations and docket numbers do not perform reliably on pure semantic matching. Every generated paragraph carried a source reference. If the evidence or confidence level fell below the defined threshold, the case was routed to a named reviewer instead of being drafted automatically.
The result was not a replacement for legal review. It was a more disciplined evidence and drafting workflow that gave reviewers traceability, surfaced potential inconsistency earlier, and reduced the amount of time analysts spent assembling material manually.
Measured Outcomes After Two Filing Cycles
Speed & Effort
- Data-request turnaround for clear precedent (~70% of volume) fell from 3–5 days to under 1 day — roughly 80% faster.
- Analyst search and assembly effort fell 60%, returning 900+ hours per filing cycle.
Quality & Trust
- Draft-to-approved edit rate stabilised at 12% by cycle two, down from 34% in UAT.
- 2 precedent inconsistencies were flagged and caught pre-review by the agent, versus a routine occurrence before.
- 100% of generated content carried citations, supporting audit sign-off.
"Agentic AI creates value in regulated knowledge work when speed is designed together with traceability and human accountability. Hybrid retrieval, source citations, confidence-based routing and high-quality metadata made it possible to reduce response effort without removing the existing legal review model."
Bring Speed and Traceability to Regulated Knowledge Work.
SYNAPLAB helps organizations assess AI workflow readiness, decompose knowledge-intensive processes, design governed Agentic AI architectures and define measurable value before scaling autonomy.