Founder-led platform · Private Beta
SKatalyst AI
Transforms fragmented business data into organized databases, documentation, dashboards, AI assistants and applications customers can own.
Project overview
Transforms fragmented business data into organized databases, documentation, dashboards, AI assistants and applications customers can own.
Problem
Business knowledge is often split across files, systems and undocumented workflows, making trustworthy automation difficult.
Constraints
- Source ownership must remain with the customer
- Outputs need traceability and validation
- Deployment choices cannot create avoidable vendor lock-in
Architecture or approach
Context-aware ingestion organizes source material, governed generators create auditable outputs, and architecture recommendations connect connectors, validation and deployment options.
Key engineering decisions
- Separate ingestion, normalization, generation and validation
- Keep source references attached to generated outputs
- Support customer-owned code and infrastructure
Trade-offs
Ownership and verification add deliberate setup work, but reduce hidden dependencies and improve maintainability.
Outcome or current status
Private Beta. Product validation and architecture refinement are in progress; no public availability or adoption claim is made.
Lessons learned
Reliable AI depends as much on source organization and validation boundaries as on model choice.
What I would improve next
Expand verified connectors and private-beta learning while preserving deployment portability.