Under the hood: the anatomy of an Organization GPT
A pragmatic look at the systems, data flows, and integration patterns that power a defensible, enterprise-grade Organization GPT.
Deep dives for technical evaluators
The Ingestion Engine
How Berry Stenley AI parses, normalizes, and semantically chunks heterogeneous enterprise documents — from PDFs and spreadsheets to email archives and wiki exports.
Advanced Retrieval-Augmented Generation
A pragmatic look at hybrid retrieval, re-ranking, query rewriting, and citation-first response generation designed for enterprise reliability.
Vector Databases in Production
Design tradeoffs across index types, embedding models, refresh strategies, and multi-tenant isolation for regulated deployments.
Enterprise Integration Patterns
Reference architectures for connecting Organization GPTs to ERP, CRM, ITSM, HRIS, and document management systems without leaking data.
Purpose-built for system-to-system intelligence
Every Organization GPT ships with a private API. Below is an illustrative query and response — every answer is grounded, cited, and traceable.
POST /v1/gpt/query
{
"organization_id": "org_9f2a...",
"department": "finance",
"query": "What is our approval limit for capex above 250k?",
"context": {
"user_role": "controller",
"region": "EMEA"
}
}{
"answer": "Capital expenditure above 250,000 EUR requires...",
"citations": [
{ "doc": "Finance-Policy-v4.2.pdf", "page": 12, "score": 0.94 },
{ "doc": "Delegation-of-Authority.xlsx", "page": 3, "score": 0.88 }
],
"audit": {
"trace_id": "trc_38c1...",
"tokens": 412,
"latency_ms": 640
}
}Meets your stack where it lives
Reference connectors for the enterprise systems where organizational knowledge already sits.