AI That Passes Inspections: Document-First RAG for Highly Regulated Industries (Adlib x SSON x West Point Technologies)
If your RAG outputs can’t be traced back to controlled sources—your AI won’t be trusted (or adopted). Learn how “document-first RAG” improves accuracy, auditability, and compliance readiness in regulated environments.
Make AI outputs defensible, starting with the documents.
RAG can unlock huge value in regulated operations, but only if your inputs are consistent, validated, and traceable to controlled sources. This session breaks down a practical approach to document-first RAG so your AI initiatives can move from pilots to trusted production.
The challenge: regulated AI fails when documents aren’t AI-ready
Most organizations underestimate the variability of “real” enterprise documents (scans, templates, mixed formats, inconsistent metadata) especially at high volume. That variability drives rework, exceptions, and friction during inspection/validation.
Even worse: if you can’t show provenance/lineage and citations for what your AI used, trust breaks down fast.
What “AI-ready by design” looks like
This webinar walks through an upstream readiness layer that:
- Captures and normalizes documents into consistent formats
- Extracts meaning (text/data), then verifies via rules and exception workflows (including human-in-the-loop when needed)
- Publishes verified content into AI/RAG so outputs are more consistent and compliant
- Adds auditability: traceable provenance + audit trails from source → output
Practical accuracy + trust controls (beyond prompt tweaks)
You’ll also see how teams pressure-test AI output quality using:
- Multiple prompts → multiple answers → voting algorithm to detect inconsistency
- Escalation paths for low-consensus outputs (automation + human review)
- Multiple LLMs to evaluate/compare results for higher confidence in critical workflows
Because in regulated operations, adoption comes down to trust: if outputs aren’t accurate, they won’t be used.
What you’ll learn (key takeaways)
- How document-first RAG improves RAG quality by extracting + standardizing content before it hits the AI layer
- What “AI-ready by design” means: capture → normalize → extract → verify → publish → validate outputs
- How to build audit trails + provenance so outputs are trackable and defensible
- Accuracy guardrails: multiple prompts + voting algorithm + multi-LLM evaluation + human-in-the-loop for exceptions
- Why adoption = trust: if accuracy isn’t there, speed doesn’t matter
Who should watch
Best for leaders responsible for:
- Information governance, records, quality, regulatory operations
- AI / analytics leaders deploying RAG in regulated workflows
- Ops and transformation owners in insurance, life sciences, manufacturing, energy, and public sector environments
FAQs (short)
Is this about RAG only?
It’s about making RAG reliable in regulated settings, by fixing inputs upstream and adding validation + auditability.
Do you store documents?
No, Adlib’s role is typically transactional processing: preparing documents (and where relevant, embeddings/encodings) to feed your existing systems and vector stores.
Our Speakers
Dale Overholts III
Dale Overholts III is an experienced technology leader specializing in digital transformation for the insurance sector. As CTO, Dale favors innovation strategies that focus on applying practical, targeted changes that shrink cost centers and drive outsized returns for mission-critical workflows. Known for bridging technical depth with business outcomes, Dale is passionate about helping highly regulated organizations unlock the full potential of advanced technologies while maintaining trust and auditability.
Anthony Vigliotti
Anthony Vigliotti is Chief Product Officer at Adlib Software with 20+ years in business workflow and intelligent document processing. He started in manufacturing at Xerox, designing components for toner-cartridge remanufacture and earning a patent in the process. Seeing how outdated document processes slowed operations, he pushed for new automation approaches—momentum that led Xerox to sponsor his return to university for a master’s in Information Technology. Since then, Anthony has led product management, alliance/partner strategy, and product development across Xerox, Notable Solutions (NSi), Nuance, and Kofax. His work centers on turning unstructured content into reliable, compliant, AI-ready data that accelerates real-world outcomes in regulated industries. Anthony holds a B.S. in Mechanical Engineering and an M.S. in Information Technology, both from the Rochester Institute of Technology.
Mickey Garcia
Mickey Garcia has 25+ years of experience in enterprise document and quality management software for Life Sciences, working with companies such as Abbott, AbbVie, Medtronic, Johnson & Johnson, GSK, P&G, Smith & Nephew, and Teva. He has held roles in strategy, product management, presales, and services for Adlib OEM partners like Dassault Systèmes (ENOVIA) and MasterControl, as well as leadership positions at M-Files and Epicor ECM (DocStar). Earlier, he spent seven years at Johnson & Johnson in operations, quality, and IT, is a certified Lean Six Sigma Black Belt, and holds an engineering degree from the University of Florida and an MBA from Stanford University.
