Agentic analysis across planning and operations reports, turning scattered documents into decisions with sources attached.
- Document understanding across operational reports
- RAG grounding against your enterprise knowledge
- Validated, self-checked analysis outputs
One ontology across products, suppliers, pricing and customers, engineered at a scale measured in millions of records.
- Harmonization at multi-million record scale
- Attribute, semantic and LLM-layer matching
- Pricing engines on the unified foundation
Market basket and spend analysis as an on-demand answer rather than a monthly manual exercise.
- Spend and supplier analysis on demand
- Category and sourcing insight
- Decision support grounded in your data
Contracts, rebates and RFPs processed at scale, with responses assembled from your data instead of your weekends.
- Contract and rebate term extraction
- RFP response assembly from governed data
- Review workflows with team sign-off
Validated data flowing to operational systems, with AI catching errors before they jam a dock, a shelf or a screen.
- AI validation before data reaches operations
- Systems integration across sites
- New items live in minutes, not days
RAG-grounded assistants that give floor and field teams cited answers from SOPs, manuals and histories.
- Copilots grounded in your documentation
- Cited, auditable answers by default
- Built for mobile, on-site use
Supply Chain Data · Fortune 500 · Catalog Harmonization at Extreme Scale
The Challenge
- The same product described differently across every source
- Manual, time-intensive matching across catalogues and formats
- Complex pricing across millions of combinations
Our Approach
- Document processing capturing product and pricing data at scale
- Attributes normalized to a unified ontology
- Product matching with attribute, semantic and LLM layers
- A pricing engine normalizing units, contracts and rebates
Outcomes
Procurement Intelligence · Same Value Chain · Market Basket Analysis On Demand
The Challenge
- Market basket analysis as a monthly manual exercise
- Procurement questions waiting on analyst cycles
- Spend insight arriving after the decision
Our Approach
- Procurement data unified for AI consumption
- Analysis automated on the harmonized foundation
- Insight delivered on demand, not on schedule
Outcomes
Last Mile· Same Value Chain · AI Validation at the Point of Sale
The Challenge
- Manual, error-prone item data entry
- Content slow and costly to produce
- Bad data driving support tickets across sites
Our Approach
- AI validation of data before it reaches the POS
- AI imagery generated on demand
- Cloud signage and systems integration
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Data Truth as a Design Input
Production, Not Pilots
Discover
Design
Build
Run & Scale
It is the starting point. Harmonization is the first build, and it is exactly the problem we have solved at multi-million record scale.
It phases: the ontology proves itself on one category first, then absorbs the rest, so value does not wait for the full catalog.
Yes. The harmonized layer sits across what you already run and feeds it back cleaner data, rather than replacing it.
It gets caught at the gate: AI validation screens inbound data before it reaches operations, and exceptions route to people.
Yes, in plain language, with every answer grounded in your data and carrying its source.
