DepthData

Product Introduction

1. Definition: DepthData is an AI Spend and Adoption Intelligence Platform. It is a SaaS (Software-as-a-Service) analytics platform that functions as a system of record by aggregating, normalizing, and analyzing usage and billing data from multiple enterprise AI tools.
2. Core Value Proposition: DepthData exists to solve the problem of fragmented and non-standardized AI tool management. It provides companies with a single, audit-ready source of truth for AI software spend, user adoption metrics, and return on investment (ROI), enabling data-driven decisions for finance, IT, and executive leadership.

Main Features

1. Unified AI Spend Dashboard: This feature provides a consolidated, real-time view of total AI expenditure across all connected vendors (e.g., OpenAI, Anthropic, Microsoft). It normalizes different billing units (tokens, credits, seats) into a single financial model. How it works: DepthData uses read-only OAuth connections to vendor admin APIs and cloud billing portals (like Google Cloud Billing for Gemini) to pull raw spend data, which is then categorized and aggregated into a unified ledger.
2. Cross-Platform Adoption Analytics: This feature defines and tracks a standardized "active user" metric across all AI tools, moving beyond vendor-specific definitions. How it works: The platform ingests usage event logs and session data from each tool's API, applies a consistent activity threshold (e.g., weekly prompts), and calculates adoption rates, user breadth, and depth of use per department or team, enabling comparative analysis.
3. Confidence-Labeled Data & Open Methodology: Every metric displayed is tagged with a data quality label ("Measured," "Derived," or "Modelled") and is backed by a fully documented methodology. How it works: The system's data pipeline and calculation formulas are published. "Measured" data comes directly from vendor APIs; "Derived" data is calculated from measured inputs; "Modelled" data is estimated with disclosed confidence intervals, ensuring transparency for financial auditing.
4. Privacy-First Data Ingestion: The platform is engineered to never access or store prompt content or AI-generated outputs. How it works: DepthData's connectors are configured to discard conversational content at the point of ingestion. For vendors whose APIs do not expose this data by design, it never enters the system, ensuring employee privacy and compliance with strict data governance policies.
5. Coaching & Waste Identification Engine: This feature identifies underutilization and provides actionable insights. How it works: Using pattern detection on normalized usage data, it flags dormant seats, duplicate tool coverage, and teams with low adoption. It then generates targeted nudges and playbooks based on the habits of top-performing users, routing enablement efforts efficiently.

Problems Solved

1. Pain Point: Financial Opacity in AI Spend. Companies lack a clear, aggregated view of their total investment across multiple AI SaaS tools, leading to budget overruns, unmanaged "shadow AI" usage, and an inability to justify or optimize spend.
2. Target Audience: Chief Financial Officers (CFOs), IT Directors, VP of Engineering, Chief Information Officers (CIOs), and Head of Procurement. These personas are responsible for software budget management, technology ROI, license compliance, and enterprise-wide tool strategy.
3. Use Cases:
   • Board Reporting: Generating a single, defensible report on AI initiative health, adoption, and cost for quarterly board meetings.
   • Procurement & Renewal Negotiations: Using data on seat utilization and feature adoption to right-size license contracts and negotiate with vendors.
   • IT Governance: Identifying and reclaiming spend on unused or duplicate licenses (e.g., employees with both GitHub Copilot and Cursor).
   • Enablement Program Measurement: Quantifying the impact of AI training programs by tracking adoption lift and depth-of-use metrics within specific departments.

Unique Advantages

1. Differentiation: Unlike vendor-specific admin consoles (ChatGPT Enterprise, Claude Console) or generic SaaS management platforms, DepthData specializes exclusively in the AI toolchain. It provides cross-tool normalization and AI-specific metrics (like token efficiency and workflow depth) that generic platforms cannot. It also differs by prioritizing data provenance and privacy over black-box analytics.
2. Key Innovation: The "Confidence Label" system and Open Methodology is a key innovation. By explicitly stating how each number was obtained (measured, derived, or modelled), DepthData builds essential trust for financial and audit stakeholders, turning analytics from a opinion into an auditable fact. This approach directly addresses the skepticism common in CFO and finance teams reviewing new technology spend.

Frequently Asked Questions (FAQ)

1. How does DepthData ensure data privacy and security? DepthData uses read-only OAuth scopes to connect to your existing AI tool admin consoles—no agents installed on employee devices. It is engineered to never ingest, store, or process prompt content or AI-generated outputs. The platform is built with enterprise-grade security, including SOC 2 Type II compliance, SSO/SAML, and full audit logging.
2. What AI tools does DepthData integrate with? The platform integrates with major enterprise AI tools including OpenAI ChatGPT Enterprise, Anthropic Claude, Microsoft Copilot, Google Gemini, GitHub Copilot, Cursor, Replit, Vercel AI Gateway, Notion AI, Slack AI, and Perplexity. It provides a public connector matrix detailing exactly what data (seats, usage, spend) each vendor's API exposes.
3. Can DepthData help us reduce our AI software costs? Yes, a primary function is cost optimization. By identifying dormant seats, underutilized licenses, and duplicate tool coverage, DepthData provides line-item recommendations for cost recovery. It also helps justify premium seats by correlating them with high "depth of use" and workload, preventing misguided across-the-board downgrades.
4. How is DepthData different from using vendor exports and a spreadsheet? Manually consolidating data from multiple vendor exports is time-consuming, error-prone, and quickly outdated. DepthData automates this daily, normalizes differing data schemas into a single model, and applies consistent business logic (like defining "active user"). This transforms a weekly manual task into a real-time, scalable system of record.
5. What is the "Workspace Health Score" and how is it calculated? The Workspace Health Score is a composite metric that gives an at-a-glance view of your organization's AI effectiveness. It is a published, weighted formula based on three components: Adoption (40%, active users), Depth (35%, complexity and breadth of use), and Efficiency (25%, cost per outcome). The exact weights are documented and included in all exports.