[ MANUFACTURING ]
Prism
Manufacturing Costing and Intelligence Platform
A six-category cost engine designed to mirror the factory's own cost-sheet methodology, with Gemini-powered PDF extraction and an approval-driven quotation workflow.
Domain
MANUFACTURING
Status
Built, deployed against a single mid-market manufacturer
Use when
Mid-market manufacturers need to retire desktop quotation systems while preserving the operational discipline of their cost-sheet methodology.
Reference
A mid-market paint and plastics manufacturer
01. The problem
Mid-market manufacturers run quotation on a maze of spreadsheets and a desktop system that grew up with the founder. The cost sheet methodology is precious operational discipline, and any replacement that loses it is dead on arrival. Off-the-shelf ERPs do not match the multi-layer costing reality (machines, components, products) that an actual factory runs on.
02. The approach
Prism is a multi-layer costing platform that mirrors how the factory actually thinks: machines with shift tariffs, components with their own material BOMs, finished products assembled from components plus overheads. The cost engine breaks every quotation into the same six categories the factory uses on its physical sheet, and is calibrated against the customer's existing methodology so the numbers it produces match the discipline already in place.
03. The architecture
Built as a modular monolith on Next.js 14 (App Router), deployed to Firebase App Hosting (Cloud Run). PostgreSQL is the primary data store; Firestore handles real-time approval notifications. The cost snapshot per quotation line stores the full category breakdown as JSONB so historical quotations do not drift when machine tariffs or item prices change later. AI-powered PDF extraction uses Vertex AI Gemini 3 Flash to parse OEM requirement PDFs, then `pg_trgm` fuzzy matching against the catalogue with confidence classification (auto, review, unmatched). The quotation workspace converges three input methods (manual product add, AI PDF extraction, OEM template) into the same cost engine. Approval workflows fire Firestore notifications in real time.
04. The outcome
A modern web quotation platform that mid-market leadership trusts because it produces the same numbers the cost sheet produces. AI lifts the data-entry burden of OEM requirement PDFs without anyone having to trust an LLM with the actual costing logic. The approval workflow brings real-time visibility to a process that used to live in a manager's inbox.
[ ATI SHAPE ]
Predictive intelligence
Cost prediction across multi-layer BOM, with the model calibrated to physical cost sheets
Agentic execution
Gemini-powered PDF extraction with `pg_trgm` fuzzy matching for catalogue resolution
Secure data
Private PostgreSQL on the customer's cloud; no data leaves their perimeter
Actionable outcomes
Quotation decisions with an approval workflow and real-time notifications
[ IMPACT ]
Multi-layer
BOM costing across machines, components, and finished products
6
Cost categories matching the physical sheet methodology
AI-extracted
OEM requirement PDFs into catalogue matches
Auditable
Cost-sheet snapshots per quotation line
[ NEXT STEP ]
See Prism in your data.
[ READY FOR YOUR STORY? ]