Active records currently stored in the demo dashboard.
Built for field-service operations
TradeFlow turns rough job information into stored, verified, usable work.
This demo shows the actual product behavior that matters: job intake, AI-assisted parsing with guardrails, generated materials, verification, pricing logic, saved records, and retrieval later from the dashboard.
- Creates structured jobs from messy notes
- Stores records for later review and reuse
- Verifies output before it is trusted
- Supports service, rough-in, and quoting flows
Live system snapshot
What TradeFlow does after the user starts entering work
The point is not just getting a file out. The point is structuring work, carrying logic automatically, pricing consistently, saving the result, and keeping the whole record retrievable after the first pass.
Post-generation review result for the current output.
Estimated reduction vs manually sorting, pricing, and reviewing.
Uses explicit pricing logic instead of hidden spreadsheet math.
Interactive demo
Simulate the system behavior
Current job record
What the system remembers
Business value
Why it matters to a real trade business
Less re-entry
One job record carries the address, builder, estimator, selected materials, and output history instead of forcing the office to rebuild the same context repeatedly.
Fewer missed items
Rule-based carryover, AI-assisted parsing, and explicit verification reduce the chance that important line items vanish between notes and ordering.
Faster handoff
Structured material outputs and supplier-facing summaries can be reviewed and sent much faster than manually cleaning up field shorthand.
Explainable pricing
Pricing behavior can be shown and defended because the engine uses visible rules, category handling, and consistent breakdowns.
Next step
Open the walkthrough to see the workflow step by step.
This is where the demo moves from high-level value into actual product behavior.