A Multinational Energy Manufacturer

AI-Assisted Reports Review Reduced Manual Effort by 90%

A multinational energy manufacturer needed a faster way to review materials test reports (MTRs), which verify supplied materials meet safety and quality requirements. Allata designed an AI-assisted review workflow inside the client’s secure ChatGPT Enterprise environment, helping the team compare purchase orders, test documents, and inspection requirements while reducing manual inspection by approximately 90 percent.

Services

Industry

Energy & Utilities

OVERVIEW

The engagement created a working AI-assisted review process that helped the client validate documentation more efficiently and with greater consistency. It also kept the solution inside the client’s existing secure environment, avoiding the need for additional platforms or custom interfaces.

The Allata team delivered practical adoption assets alongside the workflow so the client could continue using and validating the solution internally.

Working AI-assisted review process
Excel evaluation tracker for review output
Reviewer playbook to support adoption and future use

THE CHALLENGE

For this organization, MTRs are critical documents that confirm supplied parts meet required standards. They often include dense, variable-format information that must be checked against specific inspection and compliance requirements.

The client’s existing process depended on manual review by subject matter experts (SMEs), which made the work slow, repetitive, and difficult to scale. Report reviewers had to compare extracted information across multiple documents, including reports written in multiple languages, and confirm alignment with standardized checklist requirements.

The challenge was further complicated by inconsistency across manufacturers, since report formats were not standardized. That made it harder to locate, compare, and validate the same fields from one document set to the next.

Because the work supported quality and compliance decisions, any delay or error could affect downstream approvals and confidence in material acceptance. The volume, complexity, language variation, and formatting inconsistency all increased the risk of missed discrepancies and inconsistent results.

Manual review of complex documents
Reports in multiple languages
Inconsistent formats across manufacturers
Heavy dependence on subject matter expertise
Risk of missed discrepancies or inconsistent results

OUR SOLUTION

Allata designed and built a two-part platform: an Intelligent Document Processing solution. Rather than introducing external platforms, application programming interfaces (APIs), or a custom user interface, Allata built the solution entirely within the client’s existing ChatGPT Enterprise environment. This intentionally lightweight approach reduced technical complexity and kept the engagement focused on delivering business value.

The workflow used the purchase order, testing documents, and the inspection requirement document as inputs. It compared what was supposed to be inspected against what had been tested, then surfaced confirmed matches and flagged items that could not be verified with certainty.

The approach was fail-safe. If the system could not absolutely confirm a document link, it did not guess; instead, it directed reviewers to the specific item for human follow-up. The team also completed iterative validation with sample documents, formal use acceptance testing (UAT) with client SMEs, and knowledge transfer through a Reviewer Playbook.

ChatGPT Enterprise-based workflow
Document comparison across purchase orders, test results, and inspection requirements
Fail-safe logic that flagged uncertain matches for human review
Iterative validation, formal UAT, and SME review
Reviewer Playbook and knowledge transfer for adoption

THE RESULT

The solution significantly reduced the manual burden of documentation review and improved the client’s ability to process MTRs more consistently. The team estimated that manual inspection effort was reduced by approximately 90%, leaving reviewers to focus on the items the AI could not verify.

The engagement delivered a working process, supporting tools, and a repeatable approach the client could continue evaluating internally. It also improved consistency in identifying discrepancies and reduced time spent on repetitive review work.