PRISM · Cognitive Intelligence · Automotive
CelesticLabs · prism.celesticlabs.org

Your vehicles make
thousands of AI
decisions every
second.
We make every
one explainable.

Automotive AI that shows its reasoning — to your engineers, regulators, and fleet operators — in plain language they can act on. ISO 26262 · ISO/PAS 8800 · EU AI Act ready.

See how your system explains itself Explore capabilities →
$57B
Annual OEM warranty & recall cost
63%
Rise in 10 years driven by AI complexity
2026
EU AI Act Art. 86 enforcement begins
01 — The Problem

Automotive AI is a
black box. That's a
liability, not a feature.

Every time your ADAS brakes, your predictive model flags a component, or your AD system overrides the driver — it makes a decision nobody can fully explain. That gap between output and reasoning is where recalls happen, audits fail, and liability lands.

01
Recall exposure you can't see coming

Warranty & recall costs have risen to $57B/year globally. When an AI-driven fault diagnosis misses a pattern, the first signal is often a field complaint — by which time you've shipped the defect to thousands more vehicles. Root-cause analysis on a black-box model takes weeks.

Predictive Maintenance · Battery · ADAS
02
Audits that stall homologation

ISO 26262 requires a traceable safety case from hazard to software behaviour. ISO/PAS 8800:2024 now extends this to AI/ML components — demanding explainability, audit trails, and data lineage. A neural network that outputs a number isn't a safety argument.

Type Approval · Homologation · Safety Case
03
Driver distrust kills adoption

Public polling shows AV trust tracks directly with perceived explainability. Systems that generate human-readable justifications receive significantly higher acceptance scores. Opacity isn't just a technical problem — it's a market problem.

ADAS · AD Systems · Fleet Ops
"We need diagnostics our safety team can certify — not another accuracy number on a model we can't inspect." — VP Engineering, high-volume automotive OEM
02 — How It Works

One layer.
Every decision explained.

CelesticLabs wraps your existing AI systems with a reasoning interface — no model replacement, no retraining. The same output, now with a traceable explanation attached.

01
Your AI makes a decision
ADAS override, fault flag, risk score, lane change, maintenance alert — any model output from any system.
02
CelesticLabs wraps it with reasoning
Our Cognitive Intelligence layer attaches the decision logic, contributing factors, confidence bounds, and regulatory-aligned audit record to every output.
03
Engineer / regulator / operator understands why
Plain-language explanation, structured audit log, and safety evidence package — ready for your safety team, homologation engineer, and fleet manager.
// Example output — Predictive Maintenance
CelesticLabs · Cognitive Reasoning Output
decisionMAINTENANCE ALERT — Front-left wheel bearing
confidence94.2%
primary_reasonVibration frequency pattern consistent with inner race fatigue, not age-related wear
contributing_factors
→ Lateral G-load asymmetry: +18% vs fleet baseline for this route profile
→ Temperature delta at bearing: 11°C above ambient at 80 km/h steady-state
→ Wheel speed sensor variance: 0.3% irregularity over 200 km window
recommended_actionInspect mounting torque and hub runout at next service. Do not defer beyond 800 km.
iso_26262_artifact✓ Audit record generated · Traceable to safety goal SG-04
03 — Capabilities

Cognitive intelligence
across every layer of
the vehicle stack.

Eight productized services. Deployable on-vehicle, at the edge, or in your cloud.

01
Predictive Maintenance

Know why a component will fail — weeks before it does, not after the field complaint lands.

OEM Warranty · Fleet Operators
02
ADAS / AD Transparency

Every override, lane change, and brake intervention — logged with a reason your homologation team can sign off on.

Safety Engineering · Homologation
03
Driver Risk Profiling

A risk score that says why — not just 7.2 out of 10. Actionable for fleet managers and priceable by insurers.

Fleet Operators · Commercial Insurers
04
Sensor Fusion Validation

When camera, LiDAR, and radar disagree — you'll know which sensor was wrong, by how much, and what it cost you.

AV Validation · Tier-1 Suppliers
05
Compliance Reporting

ISO 26262, SOTIF, ISO/PAS 8800, EU AI Act evidence — generated automatically, not assembled by hand over six weeks.

Homologation · Legal · Safety Assessors
06
EV Battery Intelligence

Predict pack life and trace exactly which charge cycles, temperatures, or routes are burning it down.

EV OEMs · Battery Suppliers
07
In-Cabin Monitoring

DMS alerts that explain the trigger — so drivers stop disabling the system and Euro NCAP scores go up.

OEM Safety Systems · Fleet HR
08
Assembly Line Quality

Defect spotted. Root cause named — tooling, material, or process drift. Station corrected before the next shift.

Manufacturing Eng · Quality Assurance
04 — Why Now

The regulatory window
is closing. Explainability
is no longer optional.

Three converging pressures — standards, regulation, and market — are forcing automotive AI to become transparent. The companies building that infrastructure now will own the compliance stack for the next decade.

ISO 26262:2018
Functional Safety for Road Vehicles

Requires a traceable safety case from hazard analysis to every software function. AI/ML components cannot satisfy this with statistical accuracy alone — decision logic must be auditable.

ISO/PAS 8800:2024
Road Vehicles — AI Safety

Published December 2024. Explicitly mandates XAI methods, audit trails, and data lineage for any AI component in a safety-critical function. Bridges ISO 26262 and SOTIF for ML models.

ISO 21448 (SOTIF)
Safety of Intended Functionality

Addresses hazards from functional insufficiencies — the gap where AI fails in edge cases. Requires scenario coverage evidence and explanation of model behaviour boundaries.

EU AI Act · Art. 86
Explainability for High-Risk AI

From 2 August 2026, AI in safety-critical vehicle functions is classified high-risk. Article 86 requires meaningful explanation for every significant decision. Non-compliance: up to €30M or 6% of global turnover.

UNECE GTR 2026
Global Technical Regulation on AVs

Proposed January 2026. Aligned with EU AI Act and Regulation 2019/2144 — mandates transparency layers for AV decision-making. US, EU, Japan, China all participating.

NHTSA
AV Transparency & Audit Requirements

The NHTSA's forensic root-cause process requires attributable decision logs. Without explainability infrastructure, incident investigation on black-box AV systems is legally untenable for OEMs.

05 — Who We Work With

Built for the teams that
certify, deploy, and operate
automotive AI.

OEM R&D Engineering

Teams building ADAS, electrification, and autonomous features who need their AI models to produce evidence, not just outputs. Buyer: VP Engineering or Chief Safety Officer.

Predictive maintenance · ADAS transparency · Compliance reporting
Tier-1 Automotive Suppliers

Suppliers embedding AI in perception, battery management, or chassis control who must demonstrate to OEM customers that their AI component is certifiable. Buyer: Technical Director.

Sensor fusion validation · ISO/PAS 8800 evidence · Safety case documentation
Fleet Operators

Logistics and commercial fleet operators who use AI for route optimisation, driver monitoring, and predictive service — and need to explain AI decisions to drivers, insurers, and regulators.

Driver risk profiling · Maintenance intelligence · Insurer reporting
AV Validation Teams

Engineers running disengagement analysis, sensor fusion testing, and safety case assembly for autonomous programmes — who need AI decisions to be reconstructable for forensic and regulatory review.

Sensor fusion audit · Decision reconstruction · SOTIF evidence
06 — Engage

See how your
system explains
itself.

Send us one AI subsystem — a fault model, an ADAS module, a battery estimator. We'll show you what Cognitive Intelligence wrapping looks like on your own data within two weeks.

  • No model replacement or retraining required
  • Works with your existing MLOps and toolchain
  • Proof of concept scoped to one use case
  • ISO 26262 / ISO/PAS 8800 alignment reviewed together
Request received — we'll respond within 2 business days.