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Cycle time on engineering reviews fell by 23%.

A top-five upstream operator wanted fewer surprises at FEED handover. We built an AI-assisted review layer that reads drawings, checks them against the standards, and tells reviewers exactly where to look.

At a glance

What changed after six months in production.

23%Reduction in engineering review cycle time across two greenfield projects
$4.1MEstimated late-stage rework prevented in the first program
11 daysAverage package review now closes faster than the prior baseline
100%Findings traceable to a specific code clause and drawing region
Context

Reviewers were the bottleneck — and the bottleneck was getting worse.

A top-five upstream operator runs multiple capital projects in parallel, each producing thousands of P&IDs, isometrics, datasheets, and equipment specs that must be cross-checked against API, ASME, and a thick stack of internal engineering practices. The work was being done by senior engineers who were retiring faster than they could be replaced, and every project was sliding right by two to four weeks in the review gate.

The downstream cost was worse than the calendar slip. Issues caught after IFC — a missing relief path here, a wrong material class there — were turning into change orders during fabrication, with the usual multiplier on cost and schedule. Internal audits showed the same handful of clause categories showing up over and over.

The team had tried a rules engine. It found the trivial things and missed everything that required judgement. They wanted something that read like a junior reviewer who had already memorized every standard on the shelf.

"Our best reviewers were spending half their day flipping between a PDF of a standard and a drawing on another monitor. That's not engineering — that's clerical work with a stamp at the end."
What we built

A review assistant, not a black box.

Every finding cites the clause, the drawing region, and the prior project where it was last seen. Reviewers stay in charge; the system handles the page-flipping.

Drawing parser

Vision pipeline that reads P&IDs and isometrics into a structured graph — equipment, lines, instruments, and tie-ins, with the title-block metadata attached.

Standards RAG

Retrieval index over API, ASME, NACE, and the client's internal engineering practices. Every clause is chunked at section level and version-tracked.

Inconsistency detection

Cross-checks line specs against datasheets, valve schedules against P&IDs, and equipment tags across discipline packages. Flags mismatches with a confidence score.

Reviewer UI

Side-by-side drawing viewer with a findings panel. One click jumps to the cited clause; another marks the finding accepted, deferred, or false positive.

JIRA integration

Accepted findings become tickets in the engineering change workflow, pre-filled with the discipline, the drawing, and the clause text.

False-positive feedback loop

Every reviewer disposition feeds the next model release. The system gets quieter and more precise project over project.

How we delivered it

Four phases, one program, no pilot purgatory.

Phase 1

Discovery

Three weeks shadowing reviewers and cataloguing the top failure modes. We picked the eight clause categories responsible for most of the rework and scoped the pilot around them.

Phase 2

Pilot

One brownfield package, one greenfield package. We ran the assistant in shadow mode for six weeks before any reviewer was asked to use it — so the false-positive rate was already tuned when it went live.

Phase 3

Hardening

SSO, audit logging, role-based access, and the JIRA bridge. Performance work on the drawing parser so a 200-page package returns findings inside an hour.

Phase 4

Rollout

Two additional project teams, a quarterly model release cadence, and an evaluation harness the client's own engineering team now runs without us.

The result

The review gate stopped being the schedule risk.

Six months after go-live, the operator's program management office reported that engineering review had moved from the top schedule risk on every project tracker to the middle of the pack. Reviewers stopped opening PDFs of standards on a second monitor; the assistant surfaced the relevant clause text inline, with the right edition cited.

Just as important: the kinds of findings that used to surface during fabrication walkdowns — wrong material class on a relief tie-in, missing inspection requirement, an isometric that disagreed with its line list — were being caught in IFR. The change-order log on the first program after go-live was the cleanest the client had recorded in five years.

72%Of high-severity findings now caught before IFC, up from 31% baseline
4.6 hrsAverage reviewer time saved per drawing package
0.18False-positive rate per finding after three model releases
Why they re-engaged us

"It reads the drawings the way our best reviewers do — just faster."

After the first program landed, the client expanded the contract to cover two more capital projects and a midstream pipeline integrity workstream. The reference is available; we just need a call first.

Talk to a reference →

"We didn't want a tool that replaced our reviewers. We wanted a tool that gave them their afternoon back. East Reach built exactly that — and the auditors actually like it, because every flag points to a clause."

D. Rourke Director, Project Engineering · Upstream operator

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Reference architecture, evaluation methodology, and a redacted sample of the findings dashboard — sent over after a short call.

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