Legacy ERP systems can't predict stockouts or match substitutes — build intelligence on top without ripping out what works.
Build parts inventory intelligence using Bruviti's headless APIs. Integrate demand forecasting, substitute matching, and stockout prevention into your ERP without replacing existing systems or vendor lock-in.
Existing inventory systems treat demand forecasting as a closed algorithm. When it misses seasonal HVAC spikes or refrigeration surges, developers can't tune parameters or retrain on appliance-specific failure patterns.
SAP and Oracle demand expensive consultants for custom workflows. Adding substitute parts matching or cross-warehouse visibility requires costly modules that still can't access warranty telemetry or service history.
Parts data lives in one system, warranty claims in another, service tickets in a third. Building inventory intelligence requires ETL pipelines that break when source schemas change, leaving developers debugging instead of building.
Bruviti provides Python and TypeScript SDKs that wrap demand forecasting, substitute matching, and stockout prediction into API calls. Connect your ERP, warranty system, and service database once through standard connectors. The platform handles model retraining on appliance failure patterns, seasonal demand shifts, and parts obsolescence cycles without requiring data science expertise.
Developers control the integration surface. Query inventory recommendations from your service portal, embed parts predictions in technician dispatch tools, or trigger replenishment workflows from custom dashboards. The architecture is API-first — no UI lock-in, no forced workflows, no vendor-specific query languages. You write Python or TypeScript, call endpoints, and own what you build.
Predict HVAC compressor failures before summer peak using installed base age, regional weather patterns, and historical service data.
Technicians snap photos of dishwasher pump assemblies and instantly get part numbers, availability, and substitute options.
Optimize refrigerator door seal inventory across regional warehouses by forecasting demand windows and minimizing carrying costs.
Appliance OEMs manage decades of SKUs — from 1990s refrigerator compressors to 2025 IoT-enabled ovens. The platform ingests warranty claims, service ticket histories, and connected device telemetry to build demand models specific to product generations. When a refrigerator control board goes EOL, the system flags affected models and suggests cross-compatible replacements based on actual field substitutions.
Integration starts with your existing data lakes. SAP connectors pull parts master data and inventory positions. Service system webhooks stream completed repairs and parts consumption. Connected appliance APIs feed operational hours and error codes. Developers map these sources once using Python configuration files, and the platform maintains schema compatibility as upstream systems evolve.
No. Bruviti's platform sits alongside your existing SAP, Oracle, or custom ERP through REST APIs. You continue managing orders and fulfillment in your current system while querying demand forecasts and substitute recommendations via API calls. The architecture is designed for augmentation, not replacement.
Python and TypeScript SDKs are provided with full documentation. The underlying platform exposes standard REST APIs, so any language with HTTP client support can integrate. Most developers use Python for data pipeline work and TypeScript for frontend service portal integrations.
Yes. The platform supports custom model tuning through configuration files that define feature weights, seasonal adjustment factors, and product-specific failure curves. Developers upload historical parts consumption data, and the system retrains forecasts without requiring data science expertise or ML infrastructure.
The substitute matching engine learns from actual field replacements recorded in service histories. When a part goes EOL, it suggests alternatives based on what technicians successfully used in real repairs, not just manufacturer cross-reference tables. This captures tribal knowledge that formal part catalogs miss.
Most developers connect core systems and deploy initial forecasting queries within 2-3 weeks. Full production rollout including substitute matching, multi-warehouse optimization, and custom workflow triggers typically takes 6-8 weeks. The platform includes sandbox environments for testing before production cutover.
SPM systems optimize supply response but miss demand signals outside their inputs. An AI operating layer makes the full picture visible and actionable.
Advanced techniques for accurate parts forecasting.
AI-driven spare parts optimization for field service.
Access developer documentation, sandbox environments, and technical architecture guides.
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