They’re stuck because the data layer they need doesn’t exist yet.
A mid-market PropTech company had three AI products ready:
Each product was blocked by a different data issue:
Instead, she ran a 90-day data infrastructure sprint, fixing the underlying data layer.
AI products move from internal demos to production deployments faster.
Engineering teams stop maintaining unused models and start generating revenue.
Data infrastructure becomes reusable across multiple AI products instead of being rebuilt each time.
Deduplicate CRM data using probabilistic matching and unify contact records into a single buyer profile. This ensures lead scoring models operate on real buyer intent.
Build OCR scoring, document normalization, and preprocessing layers to make unstructured data usable for AI models.
Integrate fragmented systems into a canonical schema. This includes API ingestion, legacy system extraction, and manual digitization where required.
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