Research & Evidence
Literature, patents, technical data and evidence synthesis.
Connected to the loop • Platform / 02
Engineering work
The technical work that moves a programme forward.
Bring research, analysis, design, simulation, experiments and verification into one repeatable engineering loop—without stripping out the specialist methods that make the work credible.
Capabilities
Six disciplines, one continuous loop.
Each stage consumes the evidence produced by the last and keeps enough context for the final result to be defended. The methods stay specialist; the handoffs stop being manual.
Scientific Data & Analysis
Interpret experimental, simulation and engineering data.
Connected to the loop •Design & Optimisation
Explore design spaces, trade-offs and engineering alternatives.
Connected to the loop •Simulation & Solvers
Coordinate physics and computational engineering workloads.
Connected to the loop •Experiments & DOE
Plan campaigns, experiments and active-learning loops.
Connected to the loop •Verification & Reporting
Verify outputs and produce traceable engineering artefacts.
Connected to the loop • Impact
What a connected engineering loop buys back.
More designs evaluated
Wider exploration at the same compute budget by escalating fidelity selectively.
Faster iteration
Compress the path from model setup to a reviewed engineering result.
Orphaned results
Every output keeps its inputs, versions and evidence chain attached.
Illustrative platform targets. Actual outcomes vary by workflow, model and deployment environment.
Inside a campaign
Three stages, one continuous evidence chain.
01
Evidence before opinion.
Research agents work across literature, patents, internal reports and prior campaign data, then return findings with citations attached. Claims that cannot be sourced are marked as unsupported rather than smoothed over.
- ✓Internal reports and prior runs searched alongside external literature
- ✓Every synthesised statement linked to the passage or dataset behind it
- ✓Contradictions surfaced explicitly instead of averaged away
EVIDENCE SET / 41 SOURCES
- Supported
- 28 findings · cited
- Contested
- 6 findings · conflicting data
- Unsupported
- 4 claims · flagged
- Internal
- 11 prior campaigns
- Coverage
- 2004 – present
02
Explore the design space instead of defending one point.
Optimisation, DOE and active-learning loops run as governed campaigns. Candidates are generated, screened by cheap methods and escalated to high-fidelity simulation only where the trade-off actually lives.
- ✓Screening, surrogate and high-fidelity stages sequenced automatically
- ✓Constraint violations rejected before compute is committed
- ✓Pareto fronts and sensitivities returned with the runs that produced them
CANDIDATE SCREENING
Fidelity escalates only where the decision is close.
03
Verification produces an artefact, not a screenshot.
Results are checked against the acceptance criteria stated at the start, then written up with inputs, versions, mesh and solver settings, and the evidence chain intact—ready for a review board rather than a rewrite.
- ✓Acceptance criteria evaluated automatically against the original intent
- ✓Solver version, mesh, boundary conditions and inputs captured per result
- ✓Reports exportable into existing review, PLM and QA processes
VERIFICATION / RUN 24
- Mesh independencePassed · 3 refinements
- Boundary conditionsMatched to brief v4
- Thermal margin+9.1 K vs ≥ 8 K target
- Mass delta+2.4% vs ≤ 2% limit
- Reviewer sign-offAwaiting thermal lead
Why the loop holds
Why the loop holds together.
Context survives the handoff
Analysis knows what the research established; verification knows what the design was optimised for. Nothing is re-derived from a filename.
Cheap methods run before expensive ones
Screening, surrogates and analytical checks filter the space so high-fidelity compute is spent where it changes the decision.
The write-up is a by-product
Reporting draws on the same provenance the run already carries, so producing a defensible artefact is not a separate project.
Use cases
Engineering work that maps directly onto this.
Multi-stage CFD and FEA campaigns
Sequence meshing, solving, post-processing and comparison across dozens of variants without manual babysitting.
Design of experiments
Plan physical and virtual campaigns together, with active learning choosing the next most informative run.
Test–simulation correlation
Reconcile rig and field data against models, and quantify where the model is trusted.
Materials and formulation discovery
Run high-throughput screening loops with informatics and targeted experimental confirmation.
“A result without its provenance is a number. A result with its provenance is engineering.”
We optimise for work that still holds up in a design review, an audit or a licensing submission—long after the run has finished.
Talk through a campaign ↗ PerspectivesRequest the briefing pack ↗
Start a mission
Show us the campaign that takes six weeks.
We will walk through where the handoffs, re-meshing and re-explaining actually go—and what the same work looks like as one governed loop.