Product Development

Improve designs faster,
with confidence

Tabbird removes the manual burden while building a living engineering knowledge graph. The result is simple. Teams can prove failure modes, quantify impact, and iterate designs faster without sacrificing rigor or credibility.

Data integration visualization showing connections between vehicles, equipment, claims, and code integrations with SAP and IoT systems

The Engineering Reality Today

The pressure to deliver is high, yet the tools you rely on are the bottleneck.

Bottlenecked validation

Manual analysis and scarce data science resources slow down root cause validation. This delay limits exploration and increases warranty exposure across fleets and variants.

Manual data drudgery

Engineers waste hours on clerical work like decoding dealer notes and manually assembling reports (RCA, FMEA, 8D) instead of solving high-value problems.

Lost institutional memory

Critical knowledge is buried in static "lesson learned" files and shared drives. Teams are forced to re-investigate known failures and repeat analysis rather than leveraging past solutions.

Why Product Development Teams Choose Tabbird

Data Agent visualization showing stiff steering symptom traced to seal damage failure mode with pressure leak signature analysis

Validate Root Cause

Tabbird understands BOM, system hierarchy, and sensor behavior, so you can utilize our Data Agents to run instant anomaly detection and regression tests. Validate root causes across all variants and deliver defensible conclusions that withstand rigorous design review.

Claims
Telematics
BOM
Tech Spec
Failure Modes

Automate Paperwork

Auto-compile ECNs, RCA reports, and evidence packages with full traceability. Accelerate approvals, justify ROI, and communicate design changes by eliminating manual reporting drudgery.

FMEA Worksheet showing AI Recommendations for Power Steering Pump with potential cause selection interface

Active Institutional Memory

Stop solving the same problem twice. Instantly connect current issues to systemically similar past cases, turning static lessons-learned into a living engineering memory that speeds up design iteration.

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