The problem
we solve
Engineering decisions rarely start from zero. The information needed to make them already exists somewhere: in requirements, previous qualifications, test reports, historical projects, material formulations and years of verification data. The problem is that this knowledge is fragmented across documents, systems and formats, making it difficult to retrieve, compare and reuse when a new design has to be developed or validated.
As products become more complex, the cost of rediscovering what has already been learned grows with them. Requirements need to stay consistent, previous qualifications need to be identified, test evidence needs to be traced, and design choices need to be grounded in historical data, without manual reconstruction each time.
Use cases
_Requirements integrity
Turning large sets of technical specifications into structured, traceable information that engineers can inspect and compare.
- Requirement extraction: requirements identified and structured automatically from technical documentation.
- Consistency and traceability: related requirements, dependencies and supporting information connected across documents.
- Specification analysis: similar, conflicting or potentially incomplete requirements surfaced before they propagate downstream.
_Qualification reuse
Surfacing existing evidence that can support a new qualification, without repeating analysis already performed.
- Historical matching: previous qualifications and test evidence matched against new requirements and operating conditions.
- Evidence retrieval: relevant reports, results and supporting documentation surfaced directly from engineering archives.
- Reuse assessment: existing qualification evidence organised so engineers can determine what can be reused and where new verification is still required.
How we do it
#Adaptive AI
#Knowledge & Reasoning
#Predictive Intelligence
Engineering data rarely arrives in a single clean dataset. It lives across specifications, reports, tables, historical tests and domain-specific systems. The first step is therefore to give that information a common structure: extracting what matters, connecting related evidence and keeping every result traceable to its original source.
Language models, semantic retrieval and knowledge graphs make engineering documentation searchable and connected; machine learning and optimisation turn historical experimental data into predictions and candidate designs. The two combine to put relevant evidence in front of engineering judgement sooner: less repeated analysis, more reuse of what has already been validated, faster iteration from requirement to verified design.
Deployed.
Measured.
Proven.
QUALIFICATION REUSE
99%
Accuracy in automated qualification data cleansing and anomaly detection, up from 6% with manual review.
QUALIFICATION REUSE
6x
Faster qualification search across historical evidence and technical catalogues.