$30B+
TAM · ADAS hardware
Investor & strategic narrative · ADAS calibration intelligence
Sensor drift, stopped at fleet scale.
NADIR is the execution layer between vehicle telemetry and calibration outcomes — continuous drift detection, guided interventions, and evidence-grade audit trails for OEM, fleet, repair, and insurance programs.
$8.4B
SAM · calibration services
>80%
Post-repair gap
4.8 min
Mean time to detect
00 · Investor FAQ
Questions we hear in every first meeting
Short answers up front — expand any row for methodology, benchmarks, and deployment detail.
Core questions
What does NADIR actually do in production?
NADIR is a software-first calibration intelligence layer: we ingest existing vehicle telemetry in shadow mode, score extrinsic drift and residual confidence, and produce severity-ranked events plus signed evidence bundles for insurers, fleets, and OEM audit.
We do not require you to replace perception or safety ECUs in pilots — connectors + SDK + cloud normalization ship first; optional hardware attach is a later OEM channel play, not a prerequisite for revenue.
- Typical pilot: 250–500 vehicles, 6 months, one data connector + NADIR SDK.
- Outputs: drift tiers, intervention timelines, REST exports, simulation replay fixtures.
- Mean time to detect target: 4.8 minutes on reference fleet replay.
How is this different from a one-time static calibration?
Bay and factory calibrations are snapshots. NADIR is a continuous observability layer — windshield swaps, curb strikes, temperature cycles, and vibration all move extrinsics after the last procedure.
On public driving datasets (KITTI / nuScenes replay), our MVP targets ±0.05° angular and ±2 mm translation residual bands in controlled simulation — with Mahalanobis gating at 99.7% confidence before an alert fires.
Who pays first — fleet, OEM, or repair?
Near-term: fleet SaaS ($8–15/mo/vehicle) and repair-network compliance programs. Mid-term: insurer data API ($2–5/mo/vehicle) once evidence volume scales. Long-term: OEM/Tier-1 license ($45–75/vehicle) when hardware programs attach — we model software ARR leading hardware by 24+ months.
We sell to fleet ops, safety/compliance leads, MSO technical directors, and claims innovation teams — buyers who feel downtime and liability, not only R&D engineering.
What traction and product maturity exist today?
MVP drift engine and SDK paths are live in shadow mode Q1 2026. Calibration Lab, fleet console, visuals lab, and pilot packages (Amazon / Safelite / Caliber) ship on nadirai.net — synthetic + recorded telemetry without custom silicon.
Fleet LOI in progress (250-vehicle regional delivery pilot). Pre-seed $200K–400K at $4M–6M pre-money. Hardware alpha remains on the roadmap for OEM co-design, not current pilots.
How many ways can NADIR monetize the same signal?
One drift graph feeds multiple SKUs: fleet compliance SaaS, shop validation subscriptions, per-export evidence fees, insurer risk segments, OEM validation APIs, and simulation licensing for digital twin teams.
See Economics for layer-by-layer pricing and margin assumptions.
01 · Problem
The calibration compliance gap
A multi-billion-dollar systemic failure: vehicles leave service with misaligned ADAS sensors and no continuous proof of health.
0.5° pitch≈15–20% AEB range reduction in published sensitivity bands
>80%Industry samples: post-repair vehicles missing proper ADAS validation
$300–1,500Typical aftermarket recalibration cost per event
8–15%Claims severity uplift when sensor health is undocumented
Where liability and safety risk emerge
Drift is rarely visible to drivers until ADAS under-performs in a critical moment. Without a continuous sensor-health record, OEMs, fleets, repair networks, and insurers assign blame after the fact — with no shared evidence of when misalignment began or whether calibration was validated post-repair.
NHTSA’s path to universal AEB (2029) and rising ADAS fitment mean more vehicles on the road with more sensors — but not more proof that those sensors remained within spec after service events.
Liability exposure vs. documented sensor health
Modeled severity uplift when post-repair calibration evidence is absent (industry composite bands).
Severity uplift without audit trail
Continuous monitoring gap
Share of ADAS-equipped vehicles under any drift surveillance today (est.).
Problem emergence timeline
Regulatory + fleet pressure compressing time-to-detect requirements.
Deep dive Q&A
Why do sub-degree errors matter if the car still drives?
ADAS stacks fuse camera, radar, IMU, and map priors. Sub-degree extrinsic error breaks consistency — lane geometry shifts, ACC range bias grows, and AEB may fire late or not at all while the vehicle “feels” normal.
Published sensitivity bands tie ~0.5° pitch error to ~15–20% AEB range reduction. These failures are silent until homologation audit, warranty dispute, or collision reconstruction — when parties lack a shared sensor-health timeline.
What are the real-world dangers if drift goes undetected?
Safety: false negatives in pedestrian/crossing scenarios; lane-keep fighting the driver; phantom braking from misaligned fusion.
Liability: inability to prove whether ADAS was in-spec at time of loss — fleets, shops, and OEMs absorb legal exposure without telemetry-grade evidence.
Operational: grounded vehicles, repeated bay visits, and OEM line rework when validation is manual and non-repeatable.
Where does the economic pain show up first?
Economic damage concentrates where volume meets thin margins: high-throughput collision repair, delivery fleets with tight uptime SLAs, and OEM lines where 30–60 min EOL calibration caps throughput.
- OEM lines: 30–60 min EOL calibration, $40–80 labor per vehicle at volume.
- Fleets: $200–500/day downtime per grounded unit waiting for bay time.
- Repair networks: rework, chargebacks, and insurer disputes without signed evidence.
- Claims: 8–15% severity uplift when sensor health is undocumented (composite industry bands).
What do regulators and insurers care about?
2029 NHTSA AEB mandate and expanding ADAS fitment increase the number of sensor-rich vehicles — but not the quality of post-repair proof. Regulators want measurable compliance; insurers want causal chains linking interventions to sensor state.
NADIR exports ISO-ready trace completeness targets (99%+) and intervention graphs suitable for claims — not screenshots of a scan tool.
02 · Technology
Software, SDK, and algorithms (hardware-optional)
NADIR ships as calibration intelligence software today — connectors, drift engine, APIs, and evidence exports on your existing sensors and compute. Hardware partnerships are roadmap, not a blocker for pilots.
Platform depth
What is in the NADIR SDK and API surface?
SDK (C++/Python): ingest adapters for camera/radar/IMU/CAN topics, temporal alignment bus, residual scoring hooks, and local alert thresholds. Ships with synthetic fixtures for <90s first replay in dev environments.
REST API: fleet drift tiers, vehicle-level health, intervention dispatch, evidence bundle retrieval, and simulation-oriented export formats for digital-twin teams.
Pilots typically integrate one connector (e.g. fleet telematics bridge or rosbag/MCAP path) — we do not require new ECU programs to begin shadow mode.
What algorithms power drift detection?
Core pipeline: SE(3) extrinsic estimation with sliding-window bundle adjustment; camera–radar–IMU fusion via EKF/UKF; changepoint detection on residual streams; Mahalanobis + χ² gates at 99.7% confidence before escalating severity.
Implementation stack: C++17/20 numerics (Eigen, Ceres, GTSAM), optional CUDA/TensorRT paths for learned residual scorers, OpenCV geometry primitives. Validated on KITTI / nuScenes replays — MVP targets ±0.05° / ±2 mm in simulation harnesses.
What runs on-vehicle vs in cloud today?
Edge (when deployed): time sync, residual computation, gating, and buffered evidence frames — sized to fit existing fleet gateways or development rigs, not a bespoke NADIR box in current pilots.
Cloud: fleet normalization, route/region segment risk, liability timeline assembly, insurer/OEM export formatting, and dashboard surfaces. This is where most pilot integrations start — upload or stream, then score.
How do you integrate without touching the safety path?
Shadow-mode only: read-only ingestion on approved buses/topics, canonical event stream, no actuation. Dispatch outputs are recommendations and work orders to human technicians — not closed-loop braking/steering overrides.
Designed for AUTOSAR-adjacent fleets and repair networks that cannot recertify safety ECUs per software iteration.
Where does hardware fit later?
Reference designs (Orin-class compute, STARVIS-class camera, BMI270-class IMU) inform OEM conversations — planned alpha Q4 2026. Revenue model treats hardware as attach on Tier-1/OEM programs; software ARR is the near-term wedge.
02b · Live demo
Calibration Lab — damage → drift → algorithm benchmark
Browser-native misalignment simulator: pick a vehicle, apply collision or glass damage, watch camera/radar/LiDAR residuals climb, toggle seven detection algorithms, auto-run a full combo leaderboard. Built for LOI and investor walkthroughs — no install.
- 5 vehicle models
- 5 damage scenarios
- 7 algorithms + auto-benchmark
- API shadow ingest optional
Default demo opens on a front collision with tier escalation from NOMINAL → CRITICAL as simulation runs. Export benchmark JSON for pilot success metrics discussions.
03 · Market
Market size and segments
Large TAM with fragmented calibration spend — NADIR captures services, tools, and data layers across the value chain.
$30.0BTAM · ADAS hardware (2024)
$8.4BSAM · calibration services (2033)
$5.0BSAM · calibration tools (2035)
$2.2BSOM · NADIR target by Y5
Market size stack (projected)
TAM hardware base; SAM calibration spend; NADIR SOM wedge by Y5 on software + data layers.
Hardware TAM
Calibration SAM
NADIR addressable
Spend by buyer (SAM)
Where calibration dollars flow today — NADIR lands on ops + evidence layers.
5-year revenue ramp (conservative)
Software-led ARR; hardware optional attach later.
Market Q&A
What growth rates underpin the model?
We model calibration spend outpacing raw vehicle unit growth because ADAS sensor count per vehicle is rising — more cameras, more radars, more procedures per repair event.
- Calibration services CAGR: 12.8% (2033 SAM $8.4B)
- Calibration tools CAGR: 13.1% (2035 SAM $5.0B)
- Calibration-as-a-service CAGR: 3.4% on installed base
How is spend distributed by customer type?
Collision repair is the largest line item because post-event procedures are mandatory yet poorly evidenced. Fleet and insurance are faster software buyers — fewer vehicles per contract but higher monitoring ARPU and data attach.
- Collision repair: 42% (~$3.5B) — MSO, dealer, glass channels
- OEM production: 25% (~$2.1B) — EOL + validation programs
- Fleet management: 21% (~$1.8B) — uptime + compliance buyers
- Insurance / warranty: 12% (~$1.0B) — severity + UBI innovation
Who is the ideal ICP in the next 18 months?
Regional delivery fleets (250–2,000 vehicles), multi-shop repair networks modernizing ADAS bays, and simulation/OEM validation teams that need drift-labeled datasets — all integrable without hardware swaps.
04 · Product
Capabilities and positioning
The only neutral platform combining continuous drift detection, validation automation, and fleet intelligence — not a one-time bay tool.
Competitive Q&A
What do Tier-1s and OEM programs miss today?
Static factory calibration and siloed in-house stacks — no cross-OEM aftermarket evidence layer, no continuous fleet normalization, limited insurer-grade exports.
What is uniquely NADIR?
- Continuous SE(3) monitoring vs one-time bay events.
- Vertical coverage: factory → aftermarket → fleet → insurance.
- Automated compliance validation with signed intervention graphs.
- Cross-OEM AUTOSAR-compatible deployment posture.
What do fleets get in week one of a pilot?
Shadow-mode dashboards, drift tiers by route/region, dispatch hooks to bay groups, and sample evidence bundles — typically under 90 seconds to first synthetic replay via SDK fixtures.
05 · Economics
Business model and unit economics
Five monetization layers on one drift signal — software-first today, hardware attach when OEM programs mature.
Layer 1 · Fleet SaaS$8–15/mo/vehicle — monitoring, alerts, compliance dashboards (82% GM target).
Layer 2 · Shop / bay$6–10/mo/bay + validation exports — repair network compliance programs.
Layer 3 · Evidence APIPer-bundle or per-export fees — insurer and legal-ready artifacts.
Layer 4 · Risk data$2–5/mo/vehicle — anonymized segment health for UBI and claims analytics.
Layer 5 · OEM license$45–75/vehicle software+module (future) — Tier-1 embedded distribution.
$45–75OEM module + license / vehicle
$199–349Aftermarket retrofit kit
$8–15/moFleet SaaS / vehicle
82%Target fleet gross margin
Revenue Q&A
What are fleet unit economics?
- CAC: $480 · ARPV: $220 · Gross margin: 82%
- Customer lifetime: 8 years · LTV:CAC target 3:1 by Y2–Y3
What does the 5-year revenue ramp look like?
- Y1 2025: $0.8M · Y2: $4.2M · Y3: $18.5M · Y4: $52.3M · Y5: $124.7M (conservative case)
Account-level progression: $1.0k → $2.8k → $9.6k → $28k → $66k (Y1–Y5).
Which customer bases map to each layer?
Fleets buy Layer 1 first (uptime + safety ops). Repair networks buy Layer 2–3 (bay compliance + evidence). Insurers buy Layer 4 once export volume proves lift. OEM/Tier-1 activate Layer 5 when validation APIs embed in production and service tooling.
We can land multiple layers on one logo over 24 months without separate product builds — the drift graph is shared infrastructure.
06 · Go-to-market
Phased market entry
Fleet → aftermarket → OEM — each phase funds the next with measurable compliance and savings KPIs.
Y1–Y2
Phase 1 — Fleet. 50–500 vehicle operators. Target $200–500/vehicle/year savings, <6 month payback. Success: 10K vehicles, 3–5 anchor logos.
Y2–Y3
Phase 2 — Aftermarket. MSO, dealer service, glass channels. $199–349 retrofit + $6–10/mo/bay. Target 500+ shops, compliance 80%+ (from <20%).
Y3–Y5
Phase 3 — OEM. Tier-1 partnerships (Bosch, Continental, Aptiv). $40–80 labor savings, +8–12 vehicles/day/line. 1–2 OEM programs, 200K+ vehicles/year.
Why start with fleet instead of OEM?
Faster procurement, measurable ROI in months, and dense telemetry for algorithm hardening — OEM cycles are 18–36 months; fleet pilots fund engineering and case studies for Tier-1 conversations.
07 · Traction
Milestones and proof points
Started Q1 2026 — Calibration Lab + console live on production, three enterprise pilot packages + micro-fleet template, outreach pipeline active, fundraising open.
Q1 2026
MVP algorithms Done — KITTI / nuScenes validation, ±0.05° / ±2 mm sim targets.
Q2 2026
Calibration Lab + Console Live — production deploy, collision default scenario, demo seed script.
Q2 2026
Pilot packages Shipped — Amazon, Safelite, Caliber one-pagers + LOI redlines + /pilot hub.
Q2 2026
Pre-seed In progress — $200K–400K close, 4–6 engineers, 500-unit beta.
Q4 2026
SDK + API GA path Planned — pilot-hardened connectors, evidence exports, developer docs.
2027+
Seed path — $8–15M after traction; Tier-1 OEM roadmap discussions.
08 · Team
Founding team
Deep technical roots in perception, embedded systems, and automotive software — hiring perception + full-stack + embedded next.
Who is building NADIR?
Dhruv Hegde — Co-Founder & CEO. Michigan CS + Math; computer vision, camera calibration, low-level automotive sensor programming.
Srivatsan Balaji — Co-Founder & CTO. Michigan Computer Engineering; embedded systems, real-time optimization, sensor fusion architecture.
What roles are you hiring in the next 12 months?
- Founding perception engineer
- Full-stack founding engineer (React/TypeScript/API)
- Embedded / optimization engineer
09 · Funding
Pre-seed round
Capital to harden pilots, ship beta hardware, and reach seed-ready ARR and certification pathway.
$200–400KRaise target · $4–6M pre-money
40%Use of funds · team
30%Hardware & mfg
18 moTo seed prep milestones
What will you achieve in 18 months on this round?
- M1–6: core hires, beta production, first 500-vehicle pilot.
- M7–12: 3–5 pilots, ~3K vehicles, 15 shops, $500K–1M ARR validation.
- M13–18: seed prep ($8–15M), ISO pathway start, $2–3M ARR target.
How is capital allocated?
- 40% team · 30% hardware/manufacturing · 20% pilot deployments · 10% infra/ops
10 · Risk
Risks and mitigations
Transparent view of what can slow us down — and how we sequence revenue to de-risk.
OEM sales cycles (18–36 months)
Mitigation: fleet + aftermarket ARR first; Tier-1 channel partnerships; diversified revenue mix before OEM dependence.
Tier-1 / OEM in-house competition
Mitigation: cross-OEM neutral platform, IP posture, defensible aftermarket evidence moat, faster iteration vs siloed programs.
ISO 26262 certification cost and time
Mitigation: Phase 1 on ASIL-B profile for fleet/aftermarket; parallel consultants (TÜV SÜD, SGS); seed budget line for ASIL-D path.
Early LTV:CAC below target
Mitigation: pilot case studies, channel partnerships, insurance co-marketing to fleets.
Build accountable ADAS programs with us
Strategic partners, investors, and pilot customers — we respond within one business day.
dhruv@nadir.ai · srivatsan@nadir.ai · NADIR Technologies Inc. · San Francisco · Founded 2025
NADIR pitch deck · nadirai.net · visuals lab