DeepHow Tech Stack
AI-powered platform capturing and activating operational knowledge for frontline workers
DeepHow builds an operational knowledge management platform targeting manufacturing and frontline operations, combining knowledge capture, AI-driven content structuring, and workflow integration. The tech stack reveals a production ML-heavy operation: PyTorch, TensorFlow, RAG pipelines, and GCP infrastructure (BigQuery, Cloud Run, Kubernetes) point to active deployment of LLM inference at scale. Current project focus on RAG pipeline development, LLM deployment, and operational verification suggests they're scaling from proof-of-concept toward production reliability — a shift underscored by pain points around cost optimization, latency hardening, and production ML stack reliability.
Tech Stack 25 technologies
What DeepHow Is Building
◆Challenges
- Cost optimization at scale
- Latency and reliability optimization
- Production ml stack hardening
- Reducing scrap and downtime
- First-pass yield
- Deviation rates
- Line efficiency
- Turning partner relationships into pipeline
- Making partner channel scalable
- Increasing pipeline and revenue targets
▲Active Projects
- Ai pipeline ingestion and inference
- Llm deployment and scaling
- Rag pipeline development
- Knowledge capture & transfer
- Operational output verification
- Live sop verification
- Joint offering development
- Co-marketing campaigns
- Partner pipeline tracker
- Data-driven demand generation programs