From Secure Foundation to Scaled GenAI: Redapt's Growth Path with Entergy

A nuclear energy operator running a fleet of reactor units needed a cloud partner it could trust with increasingly sensitive, regulated workloads — starting small before going anywhere near its highest-stakes systems. The operator first proved that trust with a secure AWS GovCloud foundation, then handed Redapt its highest-stakes AI use case: a production generative AI system built on Amazon Bedrock that now processes regulatory documentation across 45 programs and six reactor units. The relationship has since grown into standing AI operations capacity.

Turning Challenges Into Opportunities

From Earning Trust to Expanding Scope

The operator could not hand a compliance-critical, nuclear-regulated AI system to an unproven partner. Its regulatory renewal program alone generates dense engineering and compliance documentation across dozens of programs — the kind of workload where a mistake carries regulatory consequences, not just a missed deadline

In regulated industries, the biggest AI engagements are rarely won on the first call. They're earned — one bounded, well-executed scope at a time — until the buyer trusts the partner with something that actually matters.

The Problem

The operator's nuclear fleet generated enormous volumes of dense engineering and compliance documentation for a major regulatory renewal program — thousands of pages that engineers searched manually, in an environment where auditability and GovCloud compliance were non-negotiable. Before any AI system could touch that workload, the operator needed proof that a delivery partner could execute cleanly on a bounded, lower-stakes scope first. Nothing about the relationship could start at the top.

The Solution

Redapt started with a fixed, milestone-priced AWS GovCloud landing zone — proving execution discipline before being trusted with higher stakes. That earned Redapt the operator's highest-stakes AI use case: a GovCloud-compliant, Bedrock-based retrieval pipeline with Titan V2 embeddings, Step Functions orchestration, and Textract OCR, so engineers could query compliance documentation in plain language with every answer grounded in the source material. Redapt then converted the relationship into standing AI operations capacity rather than a one-time project.

The Outcome

The operator now runs a production generative AI system across 45 programs and six reactor units — with zero delivery escalations across the broader AWS Energy vertical relationship. What started as a single landing-zone engagement became three separate work orders and a standing capacity arrangement, extended twice without a new vendor search. AWS's own partner review has since named the pattern replicable across other nuclear and Energy accounts.

SOLUTION DEEP DIVE

A Phased Path to Production AI

Redapt delivered three sequenced engagements — a secure cloud foundation, a production GenAI system, and standing AI operations capacity — each expanding scope only after the last one proved out.

Secure Cloud Foundation

Built and hardened an AWS GovCloud landing zone with account structure, network segmentation, and compliance controls
Delivered the engagement on a fixed, milestone-priced scope with named deliverables at each stage
Established the governance model the operator would use to evaluate every engagement that followed

Production GenAI for Regulatory Documentation

Designed a retrieval-augmented pipeline on Amazon Bedrock using Titan V2 embeddings and Knowledge Bases
Orchestrated document ingestion at scale through AWS Step Functions, supporting up to 45 concurrent processing streams
Applied Amazon Textract OCR, automated metadata enrichment, and human-in-the-loop failure handling for auditability
Deployed the system into GovCloud production, serving 45 programs and six reactor units

Standing AI Operations Capacity

Converted the relationship from project-based delivery to an open work order for ongoing ML operations support
Extended that work order twice within five months, each time adding hours rather than opening a new vendor search
Began a new Bedrock-based data automation proof of concept building on the same production foundation

Partnership Page

THE OUTCOMES

Shifting From Single Projects To Standing Capacity

The operator now treats the relationship as standing capacity, not a single delivered project, with AWS citing the pattern as repeatable across its Energy vertical.

Production AI at Scale

A generative AI system processes regulatory documentation across 45 programs and six reactor units in production.

Standing Delivery Capacity

A single work order became three separate engagements, extended twice without a new vendor search.

Zero Delivery Escalations

Part of a broader AWS Energy vertical track record with zero escalations across every engagement.

A Replicable Pattern

AWS's own partner review named this GenAI document-management approach reusable across other nuclear and Energy accounts.

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CONCLUSION

A Foundation Built to Keep Growing

If your pilots keep stalling before reaching anything with real regulatory weight, that's a trust problem, not a technology one. This operator's AI program didn't start with AI — it started with a foundation Redapt had to earn first. Connect with Our AI Production Deployment & MLOps Team about earning that same path for your own regulated environment.

PRIVACY / CONFIDENTIALITY STATEMENT

Your trust and confidentiality are our top priorities. Redapt prioritizes and protects our customers' privacy, so we don't publicly disclose account names or other identifiable information. We are eager to share our successes and are happy to provide specific referrals or case studies upon request.

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