Case Study

How a Fortune 50 Commodity Trader Achieved 97% AI Accuracy

Global Commodity TradingFortune 50Multi-Region Operations
0%
Accuracy in 6 months
<1s
Resolution time
0+
Jobs per week
0.0%
Uptime

Company Profile

One of the world's largest private companies — a global commodity trading firm operating across multiple continents with thousands of employees. Following several acquisitions, the company runs 5+ enterprise resource planning systems across regional offices, each with its own product codes, vendor identifiers, and business conventions.

The Challenge

Entity fragmentation

Same products appearing with 5+ different codes across regional systems after M&A. No single source of truth.

AI hallucination on internal data

AI tools had no context for internal product codes, vendor IDs, or business rules. Outputs were unreliable.

Manual reconciliation bottleneck

Analysts spent 4+ hours per data reconciliation request, manually searching ERPs and cross-referencing spreadsheets.

Knowledge trapped in experts

Institutional knowledge — which codes map to which products, regional conventions, historical renames — lived only in senior employees' heads.

The Solution

Phyvant was deployed on-premises within the company's existing infrastructure. The system ingested data from 5+ enterprise systems and began building a knowledge graph of cross-system entity mappings, business rules, and historical context.

Existing AI tools (ChatGPT, Copilot, and custom agents) were connected to Phyvant via API. When any AI tool received a query involving internal data, it checked with Phyvant first — getting verified business context before responding. No changes to analyst workflows were required.

Deployment Timeline

Week 1-2

Integration

On-premises deployment, data source connections, initial ingestion from 5+ enterprise systems.

Month 1

73% Accuracy

First queries live. AI tools begin using Phyvant for entity resolution. Expert corrections start flowing in.

Month 3

89% Accuracy

Self-improving loop compounds corrections. Most common entity mappings are now verified. Resolution time drops to seconds.

Month 6

97% Accuracy

System reaches production maturity. 1,000+ automated jobs per week. 99.8% uptime. Analysts reclaim thousands of hours.

“We went from spending days reconciling data across systems to having AI get it right in seconds. The self-improving loop means it just keeps getting better — every correction our team makes compounds into better accuracy for everyone.”

— VP of Data Operations, Fortune 50 Commodity Trading Firm

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