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Built for enterprises that want AI to understand their business.

Cross-system data lookup
4+ hours
Average resolution time
<1 sec
With verified data
Without Phyvant

A request comes in referencing a product by name. An analyst manually searches multiple ERPs, cross-references spreadsheets, emails colleagues.

→ Check NA ERP... not found
→ Check EU system... maybe?
→ Email sent to regional team
→ Waiting...
With Phyvant

AI assistant receives the same request. Queries Phyvant for the product name. Gets verified codes across all systems instantly.

phyvant.resolve("Widget Pro")
├─ NA: PRD-4412
├─ EU: Item-4418
├─ APAC: P-Dex
└─ Confidence: Verified
97%
Fewer corrections after 6 months
99.9%
Production uptime
The problem

Powerful AI. No idea how your business works.

AI is powerful at general tasks. It fails on your specific business—your products, your systems, your rules. That gap costs real money.

What it looks like today
Your Private Data
ERP System
CRM Data
Internal Docs
No access
AI Tools
ChatGPT
Can't see data
Copilot
Can't see data
Claude
Can't see data
Both Approaches Fail
Option A: Manual copy-paste
User: "Here's our product data..."
AI: "Based on this, PRD-4412 is..."
→ Wrong. No context for internal codes.
Option B: Build an internal agent
Agent: Queries ERP, CRM, Docs...
Agent: "Found PRD-4412 in 3 systems"
→ Still wrong. Doesn't know they're the same product.
Access ≠ Understanding
Even with data access, AI lacks the knowledge to interpret it correctly.
Our Solution

We give your AI tools institutional knowledge.

Phyvant captures your organization's institutional knowledge and delivers it to every AI tool automatically. Your AI stops guessing. Your experts stop repeating themselves.

With Phyvant
How it works
ERP System
CRM Data
Internal Docs
Phyvant
Institutional Knowledge Layer
ChatGPT
Connected
Copilot
Connected
Claude
Connected
Resolve cross-system IDs
"PRD-4412"
NA: PRD-4412
EU: Item-4418
APAC: P-Dex
Look up business rules
"APAC shipment"
Requires Form-7B
5-day lead time
Customs pre-clear
Trace entity relationships
"Acme Holdings"
Parent: GlobalCorp
Subs: Acme EU, Acme Asia
847 linked transactions
Surface historical context
"Project Atlas"
Was "Project Titan"
Renamed Q2 2023
Same project ID: P-0847
How it works

Four steps. Self-improving.

A simple loop that gets smarter every time your team uses it. No extra work required.

The Learning Loop
01
Ingest

We pull in your reports, exports, and docs. Your business knowledge becomes structured data AI can use.

Reports
Exports
Docs
Phyvant Knowledge Graph
02
Query

Your AI tools check with Phyvant before answering. They get verified info about your business instantly.

ChatGPT
query
Phyvant
Q: What is PRD-4412?
A: Product in NA=PRD-4412, EU=Item-4418...
03
Correct

Your experts review AI outputs like normal. When they fix something, we capture it automatically.

AI output: Item-4418
Correction: Item-4418-EU
Captured automatically
04
Improve

Those fixes flow back into the system. Next time, the AI gets it right. Accuracy builds over time.

73%
Month 1
89%
Month 3
97%
Month 6
Accuracy over time
Feeds back into Step 1
Privacy

Your data stays yours.

Your Infrastructure
Deployment Architecture
Your VPC / Data Center
Everything runs inside your perimeter
Your Data
Your Logs
Your Rules
Phyvant
Deployed here
No data leaves this boundary
Full Audit Trail
09:41:23Query: product lookup "PRD-4412"
09:41:24Response: verified, 3 systems matched
09:41:25User: analyst@corp.com via Copilot
09:41:26Access: Read-only, no data exported
Every request logged. Complete visibility for compliance.
FAQ

Frequently asked questions about enterprise AI

An enterprise AI knowledge graph is a structured layer that captures your organization's institutional knowledge—product codes, business rules, entity relationships, and historical context—and makes it accessible to AI tools. Unlike raw data access, a knowledge graph provides semantic understanding so AI can interpret your data correctly.

Phyvant sits between your data systems and AI tools, providing verified context about your business. When an AI tool queries about a product code like 'PRD-4412', Phyvant returns the verified mapping across all your systems (ERP, CRM, regional databases), business rules, and historical context—eliminating guesswork and errors.

AI data reconciliation is the process of resolving conflicting or inconsistent data across enterprise systems so AI tools can provide accurate answers. Without reconciliation, AI may not understand that 'PRD-4412' in your NA ERP is the same product as 'Item-4418' in your EU system. Phyvant automatically reconciles these identities.

Yes, Phyvant is designed for on-premises deployment within your VPC or data center. Your data never leaves your security perimeter. Every query is logged for compliance, and you maintain complete control over access permissions. This makes Phyvant suitable for regulated industries like healthcare, financial services, and legal.

Phyvant uses a self-improving feedback loop. When your experts correct AI outputs during normal work, Phyvant captures those corrections automatically. These improvements flow back into the knowledge graph, so accuracy increases over time—typically reaching 97% accuracy within 6 months without additional training effort.

Phyvant integrates with any AI tool that can make API calls, including ChatGPT, Microsoft Copilot, Claude, and custom AI agents. It acts as a knowledge layer that AI tools query before responding, ensuring they have verified business context regardless of which AI platform you use.

Make AI understand your real data.

We'll connect your AI tools to your real business data. See results in the first week.

See how it works with your data