How to cut manual review of engineering documents by up to 70% without losing accuracy?
Azati built an AI-powered pipeline that extracts data from engineering documents and AutoCAD drawings for a large Middle Eastern oil and gas operator, cross-checks it against internal systems, flags discrepancies, and feeds verified results into the client's existing Knowledge Hub platform, processing roughly 100,000 documents with an estimated 50-70% cut in manual review effort.
technical documents processed by the AI pipeline to date
estimated reduction in manual review effort for standard document packages
estimated reduction in time spent preparing data for reporting
In oil and gas operations, technical documentation never stops arriving. Contractors submit engineering drawings, inspection reports, and compliance documents continuously, each one needing to be checked against what the operator's internal systems already know. Manually reading, cross-referencing, and reporting on all of it scales linearly with headcount and degrades in accuracy as volume grows.
The client, a large Middle Eastern oil and gas operator, was carrying exactly that burden largely by hand, made slower and riskier by the variety of formats contractors submitted documentation in.
Azati built an AI-powered pipeline that takes over the extraction, validation, and discrepancy-detection work, then feeds the results directly into the client's existing Knowledge Hub platform, the system engineers already use, rather than introducing a separate tool that competes for their attention.
Engineers were manually pulling data out of technical documents and engineering drawings, then checking it by hand against internal software systems. At scale, this approach consumed significant time and introduced a real risk of human error in work where accuracy directly affects engineering and operational decisions:
Standard OCR and document parsing tools are not built for AutoCAD drawings, P&ID diagrams, and dense technical schematics, where the meaningful information is embedded in layout, geometry, and notation as much as in text:
The client already relied on a Knowledge Hub platform for viewing and using processed results. A new, disconnected tool would have meant another system for engineers to learn and another silo of data to reconcile:
As the project progressed, new categories of documents and edge cases surfaced that hadn't been part of the original scope, requiring the team to clarify requirements iteratively rather than work from a fixed specification:
Generic document AI doesn't handle AutoCAD drawings and engineering schematics well. Azati combined computer vision and large language models with real engineering-document expertise, building extraction logic that understands geometry, layout, and technical notation, not just text on a page.
Azati built the solution as an AI integration into the client's existing platform rather than a competing tool. That decision meant engineers kept working in the system they already knew, with AI-extracted, verified data simply appearing where they expected it.
This isn't a proof of concept. The pipeline has processed approximately 100,000 technical documents in production, the kind of volume that surfaces edge cases a pilot never would.
New document types and use cases kept surfacing as the engagement progressed. Azati's team translated evolving, sometimes informal business requests into clear technical specifications quickly enough to keep delivery moving, including a rapid two-week turnaround when client security requirements needed urgent rework.
If your team is manually extracting data from engineering drawings and chasing discrepancies by hand, Azati can show you what an AI-powered pipeline looks like for your document volume.
Cut my document review timeAzati combined computer vision, OCR, and large language models with deep engineering-document expertise to build a pipeline that extracts, validates, and surfaces discrepancies in technical documentation at scale, then feeds the results directly into the client's existing platform.
The pipeline extracts relevant data directly from technical documents and AutoCAD engineering drawings, using computer vision and geometry-aware processing to interpret layout, tags, and notation, not just plain text. This is the layer that turns unstructured documents into structured, analyzable data, ready for downstream use.
Extracted data is checked against existing records in the client's internal systems to identify discrepancies, missing information, or inconsistencies, replacing manual cross-referencing and validation work that previously consumed significant engineering time.
Large language models help interpret the extracted content in context, supporting more nuanced understanding of technical language and documentation patterns than rule-based extraction alone could achieve.
Verified, structured results are delivered directly into the client's existing Knowledge Hub platform, where engineers already review documents, examine extracted objects and attributes, and compare results against the original source.
Beyond document-level review, the platform supports working with hierarchies of tags, documents, purchase orders, and functional locations, including bulk updates via Excel for teams managing large volumes of related records.
A dedicated reporting module turns processed data into exportable, filterable reports, with direct links back to the original document and the specific object in question, closing the loop from raw drawing to actionable report.
| Area | Azati contribution |
|---|---|
| Document extraction | Built computer vision and OCR pipelines for technical documents and AutoCAD drawings |
| Data validation | Implemented automated cross-checking against internal system records |
| AI/LLM integration | Applied large language models to interpret technical document content |
| Platform integration | Connected processing results into the existing Knowledge Hub platform |
| Requirements engineering | Translated evolving client business needs into technical specifications |
| Security | Delivered critical security requirements within a two-week turnaround |
| Client communication | Maintained ongoing iterative communication with client and contractors |
| Scale | Processed approximately 100,000 technical documents in production |
The engagement accounts for the client's confidentiality requirements and internal procedures for handling technical documentation, reporting, and contractor interactions. The solution is designed around transparency of document processing and traceability of results, so every extracted data point and flagged discrepancy can be traced back to its source document. When client security expectations were clarified mid-engagement, the team reprioritized and delivered the critical security requirements within two weeks to protect the project and maintain client trust.
The engagement runs on a Time and Material basis and has been active for roughly six months, with the client extending the contract for at least another year as new document types and use cases continue to expand the scope.
The team works in a Kanban delivery model, prioritizing iterative delivery as requirements and document types evolve:
The pipeline has moved from concept to production scale, processing approximately 100,000 technical documents, real volume that validates the approach well beyond a pilot.
For standard document packages, agentic AI-driven extraction and discrepancy detection shifted the bulk of the work from fully manual to semi-automated, where the system handles extraction and initial checking and a specialist verifies the result.
Time spent preparing data for reporting dropped substantially compared to a fully manual process, freeing engineering time for higher-value review work instead of data wrangling.
The client moved from a process dependent on individual experts manually reviewing every document to a more transparent, scalable infrastructure-backed workflow that doesn't break down as volume grows or specific people become unavailable.
What this engagement demonstrates beyond the numbers:
The decision to integrate directly into the client's existing Knowledge Hub, rather than building a separate AI tool, is what made adoption real. Engineers didn't have to change how they worked; the AI simply made the system they already trusted smarter and faster.
AutoCAD drawings, P&ID diagrams, and technical schematics carry meaning in geometry and layout, not just text. Treating engineering documents as a specialized domain rather than applying generic OCR is what makes extraction from this kind of documentation actually reliable.
When client expectations on security shifted mid-engagement, the team reprioritized and delivered the critical pieces in two weeks. That kind of responsiveness is part of why this dedicated team engagement got extended rather than ended.
In industries where external contractors continuously submit documentation in varying formats, a static, rule-based system breaks down fast. A pipeline designed to absorb new document types and evolving requirements is what keeps working a year in.
Enterprise data platform modernization: analytical pipelines, data marts, and orchestration workflows across hundreds of flows and thousands of structures, delivered over a 24+ month embedded engagement.
Petrochemical inventory platform: 7-year dedicated team covering development, AI-assisted nomenclature matching, on-premise DevOps, and test automation, addressing high costs from unclaimed stock.
Energy sector document workflow QA: role-based access testing, multi-stage document lifecycles across 10 user roles, and AI documentation assistant validation over 6-8 months.
Enterprise construction cost estimation platform for oil & gas projects with complex cross-module calculation dependencies.
Oilfield reservoir analytics platform with 4 modules in 4 months, computational core rebuilt in 10 days, and ~1000X reduction in numerical precision loss after enterprise QA.
Sharepoint migration and portal consolidation for oil and gas, 30+ portals consolidated, 100,000+ content entities migrated, 15%+ customizations beyond initial estimation.
Last updated
