Why Enterprise AI Costs Spiral Out of Control, And What's Missing Is Context

The Problem

Companies deploy Copilot or Claude for Enterprise. Three months later, the API bill is double the projection. A single document summary triggers 20 API calls. An employee asking about Q3 marketing budget status causes the AI to search Slack, Jira, Confluence, and the ERP sequentially, without knowing which source has the answer.

The root cause: AI tools operate without a map of where enterprise information lives. Every query is a fresh start.

flowchart LR
    A[User Query] --> B{No Context Graph}
    B --> C[5 Slack searches]
    B --> D[3 Jira queries]
    B --> E[2 ERP lookups]
    C & D & E --> F[Wrong answer → Re-query]
    F --> B

Why Now

Over 80% of enterprises have deployed AI tools, but 60% report AI costs exceeding budget (2026 McKinsey AI Survey). AI cost optimization has become the top IT budget scrutiny item in 2026. Glean proved the market globally at $300M ARR. Regional versions for markets with distinct enterprise tool stacks remain largely unaddressed.

How to Build It

Two-phase MVP:

Phase 1, Connector Layer: Read-only API connectors for dominant enterprise tools in the target market. Collect metadata (file type, update date, author, related projects) for each data source without storing content.

Phase 2, Context Index: Map data relationships in a graph database. When “Q3 marketing budget” is queried, the graph delivers the 1-2 most relevant sources directly to the AI, bypassing the scatter-search pattern. This alone can cut API calls by 30-50%.

Free cost-savings simulator as onboarding tool reduces sales friction.

Success Criteria

Key assumption: enterprises will grant internal system read access to an AI cost optimizer. Early on, on-premise or private cloud deployment is essential for security-sensitive organizations. Proving 30%+ API call reduction in 3 pilot customers creates the expansion path.