From specification
to certainty.
Three steps · One prediction engine · No more surprise blockers
[ Active failure modes ]
07 signals- Input specs
- Knowledge graph
- Supply chain scan
- Manufacturing check
- Regulatory mapping
- Confidence score
- Recommendation delivered
Feed it your design.
Upload technical specifications — CAD files, simulation outputs, material choices, performance targets. Realise reads the language engineers actually use.
- ·CAD ingest
- ·Simulation parse
- ·Spec normalisation
The engine thinks.
Hybrid AI — a symbolic knowledge graph encoding industrial constraints, coupled with ML trained on historical deeptech project data — maps your design against 10,000+ known failure patterns.
- ·Supply chain graph
- ·Manufacturing constraints
- ·Regulatory matrix
Recommendations you can act on.
Not a black box. Not 'consult an expert'. Specific, explainable alerts with impact estimates — so engineers can make the call, not guess.
- ·Explainable alerts
- ·Impact estimates
- ·Pivot suggestions
Explainable alerts.
⚠ Supply chain risk — High
Nickel-90% spec creates 8-month sourcing constraint. Your timeline: 3 months. Suggested pivot: Nickel-72% alloy, 2-week lead time, −3% performance impact.
⚠ Manufacturing — Medium
Electrode coating uniformity at ±2μm validated at lab scale. Industrial deposition processes achieve ±8μm. Recommend tolerance review before tooling investment.
✓ Regulatory — Clear
ISO 22734 compliance pathway mapped. DEKRA certification: 4 months, no redesign required if gas diffusion layer spec maintained.
Explainable by design.
We didn't build a black box. Realise uses symbolic AI — rules and relationships engineers can read and challenge — combined with machine learning for pattern detection. Every recommendation shows its reasoning. Because engineers don't trust what they can't interrogate.
lower carbon footprint than LLM alternatives
fewer emissions per query vs GPT-4
black-box outputs