$ proof

Proof, measured — not promised.

Synthetic Engineering is built by the same senior team as Synthetic Users — people who have delivered AI transformation as a preferred partner to demanding organisations. Every workflow carries a baseline and a delta.

Delivered for organisations including

01 / Case points

Described by mechanism. We do not publish invented numbers — specific, cleared figures and named references are shared under NDA at the intro call.

Technical documentation /0.1

Authoring that keeps pace with engineering

Tier-1 automotive supplier · service & repair content

Problem: A DITA-based authoring team maintained six document families across model variants and markets. Time-tracking in the diagnostic showed the largest single block of authoring hours went to cross-checking released engineering data — torque specs, part references, variant applicability — not to writing.
What we did: Six-week baseline of hours per document type across the delivery line. Then a two-week throwaway prototype: a drafting agent over an LLM API with retrieval on released PLM extracts, proposing updates with source citations. Two-week limited deployment with five senior authors, every output behind a validation gate owned by the lead author.
How it's measured: Instrumented as hours per document type vs. the pre-agent baseline, plus a citation-accuracy audit on every gated output. Drafting cleared the gate and scaled to the full team; a second candidate — auto-translation of safety warnings — was killed at the two-week mark for unacceptable review burden.

Localization /0.2

Localization past release cadence

Consumer electronics · 100+ locales

Problem: Release windows compressed from weeks to days while human review throughput stayed flat. The bottleneck wasn't machine translation quality — it was reviewers re-reading whole files because they couldn't trust which segments needed attention.
What we did: Mapped the data landscape first: translation memories, termbases and style guides per locale tier. Staged an LLM post-edit pipeline inside the existing TMS — no platform swap — starting with the four highest-volume European pairs plus Japanese, with reviewer-in-the-loop sampling QA and per-segment confidence routing deciding what humans actually read.
How it's measured: Measured as reviewed words per reviewer-hour by language tier, with LQA error rates held against the pre-pipeline baseline. Tier-1 pairs cleared the gate and expanded tier by tier; two low-volume tiers stayed fully human — the delta never justified the review overhead.

Engineering change /0.3

Change impact, traced in minutes

Industrial group · SAP change records

Problem: Every engineering change order triggered a manual hunt: analysts traced affected manuals, parts catalogues and training content by memory and spreadsheet, typically days per change, with misses surfacing months later as field-documentation defects.
What we did: Two-week prototype on off-the-shelf orchestration: an agent walking change records against BOM structures and content references, producing a ranked impact list with confidence per item. Kept deliberately human-gated — the analyst signs off the list; the agent never edits content.
How it's measured: Measured as analyst time-to-impact-list vs. baseline, and missed-impact rate audited against a set of historical changes with known outcomes. Cleared the gate on time; the miss-rate audit is what earned analyst trust and adoption.

Parts data /0.4

One defensible dataset, many workflows

Off-highway equipment · parts & catalogue data

Problem: The same part carried conflicting attributes across the dealer catalogue, ERP and a legacy PIM. Every automation candidate downstream — search, cross-sell, documentation reuse — stalled on the same root cause.
What we did: Data-quality diagnostic first: duplicate and conflict rates quantified by attribute class across the three systems. Then the one custom build the gates justified — a curated golden-record dataset with a matching pipeline and a human adjudication queue for the ambiguous tail, instead of another agent on top of bad data.
How it's measured: Measured as records reconciled per adjudicator-hour and downstream error rate in the workflows the dataset feeds. The dataset is the moat: three previously-killed automation candidates were re-scored against it and two re-entered the build queue.

02 / Measurement philosophy

The analytics layer decides what productizes and what dies.

Baseline and delta on every workflow. Internal wins are proven first; only what clears the bar is scaled, and only what scales is ever considered for productization. Nothing ships on a hunch.

03 / Sectors & work types

Technical authoring
Engineering-change / BOM impact
Localization at scale (100+ languages)
Parts / catalogue data
Training content
3D / visual assets

note: specific engagement detail and references available under NDA at the intro call.

Want the specifics?

We share cleared metrics and named references under NDA on the intro call.

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