AI Observability Platform
See Every Dollar, Every Tool, Every Session

What Is AI Observability?

AI observability is the ability to see, measure, and understand how AI tools are being used, what they cost, what they produce, and whether that spend is justified — across every provider, department, and user in an organization.

The term borrows from infrastructure observability — the discipline of understanding system behavior through telemetry. But AI observability extends beyond uptime and latency. It asks harder questions: Are we spending in the right places? Are teams duplicating tools? Are we paying for premium models that deliver no more value than standard tiers? Are employees using AI tools that procurement never approved?

AI observability is not a dashboard. It is a discipline. It requires instrumentation across multiple data sources, normalization across inconsistent billing models, and the analytical framework to separate signal from noise. Most enterprises have none of this today — and the cost of that blindness is growing every quarter.

The AI Spend Visibility Problem

AI tool spending is the fastest-growing and least-managed line item in enterprise budgets. Unlike traditional SaaS — where procurement owns the relationship and IT owns the license count — AI spend is fragmented across every function, billing model, and approval chain in the organization.

• No single AI spend owner. Finance sees total vendor payments. IT sees sanctioned tools. Procurement sees contract renewals. Nobody sees all three together. The result is a fractured view where waste, duplication, and risk hide in the gaps between teams.
• Shadow AI purchases bypass procurement entirely. Employees put AI subscriptions on corporate cards. Department heads approve tools without IT review. Contractors bring their own AI stack. These purchases show up on expense reports, not license inventories — invisible to every governance process in the company.
• Embedded AI features are hidden inside existing SaaS bills. Salesforce Einstein, Zoom AI Companion, Slack AI, Microsoft 365 Copilot — vendors are activating AI features inside tools you already pay for. Some are included in your license. Others trigger premium pricing. Most organizations cannot tell the difference until the invoice arrives.
• Developer AI tool spend is invisible to finance. Engineering teams consume AI through APIs with usage-based billing, IDE extensions with per-seat subscriptions, and code-generation tools with token-based pricing. Finance sees a line item from "Anthropic" or "OpenAI" with no context about what that spend produced. Copilot seat waste alone can represent five or six figures annually.
• The board is asking questions nobody can answer. How much are we spending on AI? What is the ROI? Are we governed? How do we compare to peers? These are reasonable questions with no reasonable answers in most enterprises today — because the data infrastructure to answer them does not exist. The CFO cannot answer what the CFO cannot see.

What Coriven Proof Observes

Coriven Proof is purpose-built for the AI observability problem. It is not a SaaS management tool with an AI tab bolted on. Every feature, data model, and detection rule was designed from the ground up for the specific challenge of making AI spend visible, measurable, and accountable.

AI Spend Intelligence

Total AI spend by provider, department, tool, and individual user — consolidated into a single view that updates in real time. Coriven Proof normalizes spend data across usage-based, seat-based, and hybrid billing models so you can compare apples to apples. Every dollar is tagged with a confidence level so you know whether the number is verified from source data, calculated from known inputs, or estimated from patterns.

18 Patent-Pending Waste Detection Rules

Coriven Proof runs 18 automated waste detection rules across your AI portfolio continuously. These are not generic alerts — they are specific, scored findings with dollar-amount recovery estimates. The rules identify:

• Duplicate licenses across departments for the same AI tool
• Unused or severely underutilized seats
• Premium model waste — paying for GPT-4-class models where GPT-3.5-class would deliver identical results
• Shadow AI purchases appearing on expense reports
• Token tier mismatches between actual usage and contracted tiers
• Overlapping tool capabilities across different vendors
• Auto-renewal risk on tools with declining utilization
• Embedded AI features activated without explicit approval

Each finding includes a severity score, affected department, estimated annual waste, and recommended action. Finance teams can prioritize recovery by dollar impact. IT teams can prioritize by security risk. Both views are available in the same platform.

Department Budgets

Real-time AI consumption tracking at the department level. Set budgets per team, receive overspend alerts before costs breach thresholds, and compare actual consumption against forecasts. Department leaders see their own spend. Finance sees the rollup. No more quarterly surprises — the data is live.

Cost Forecasting

Three-month forward projections based on historical consumption patterns, known renewals, and trend analysis. Scenario modeling lets finance teams test the impact of consolidating tools, renegotiating contracts, or shifting to different AI providers. Forecasts carry confidence tags so leadership knows how much weight to put behind each number.

Renewal Intelligence

Every AI tool contract approaches renewal with the same question: should we renew, renegotiate, or replace? Coriven Proof arms procurement teams with the utilization data to negotiate from strength. Actual usage against licensed capacity. Cost per active user. Feature overlap with other tools in the portfolio. Benchmark pricing from industry data. The days of renewing AI contracts blind are over.

Developer Productivity Intelligence

Engineering teams are the heaviest AI consumers in most organizations, and the hardest to measure. Coriven Proof provides developer productivity intelligence through 13-category deterministic task classification — inspired by CodeBurn's approach to activity classification — that categorizes every AI interaction by type: code generation, debugging, refactoring, documentation, test writing, code review, and more.

The platform measures one-shot success rates (how often the AI-generated output is accepted without modification), detects model tier waste (developers using expensive models for simple tasks), and attributes costs to specific projects. Engineering leaders get a clear picture of whether AI tools are accelerating development or just adding cost.

Embedded AI Tracking

Coriven Proof detects AI features embedded within existing SaaS products — Salesforce Einstein, Zoom AI Companion, Slack AI, Microsoft 365 Copilot, Adobe Firefly, Notion AI, Grammarly Business, and others. These features often activate silently through license upgrades or vendor bundles. The platform surfaces which embedded AI features are active, what they cost (or what premium they trigger), and whether anyone is actually using them.

Expense and Financial Discovery

Shadow AI hides on corporate cards. Coriven Proof integrates with Brex, Ramp, Expensify, and other expense platforms to detect AI-related purchases that bypass procurement. When an employee expenses a ChatGPT Plus subscription, an AI image generation tool, or a niche department-specific AI product, Coriven flags it, categorizes it, and rolls it into the total AI spend picture.

Confidence Tags — How You Know What You're Looking At

Every data point in Coriven Proof carries a confidence tag. This is not cosmetic. It is the foundation of the platform's credibility, and it is what separates Coriven from every other tool in the market that presents numbers without context.

Verified means the data comes directly from an authoritative source — an API response, an uploaded invoice, a financial system integration. The number is as accurate as the source itself.

Calculated means the number was derived from verified inputs using a known formula. A department's total AI spend is calculated by summing the verified costs of each tool assigned to that department. The formula is transparent and auditable.

Estimated means the value is projected based on patterns, benchmarks, or incomplete data. An estimated number might represent projected annual spend based on three months of usage, or a benchmark-derived cost for a tool where direct billing data is not yet connected.

Every other platform in this space shows you a number and asks you to trust it. Coriven shows you a number and tells you why you should trust it — or how much caution to apply. When the CFO presents AI spend data to the board, confidence tags are the difference between "we think" and "we know."

How Coriven Proof Collects Data

AI observability requires data from sources that no single existing platform was designed to access. Coriven Proof collects through six distinct ingestion paths, each targeting a different layer of the AI spend stack.

AI Observability vs SaaS Management

Traditional SaaS management platforms — Productiv, Zylo, Torii, Zluri — were built to solve a different problem. They track SaaS licenses, measure seat utilization, and optimize software portfolios. They do this well. But they are structurally incapable of solving the AI observability problem.

The gap is not a feature deficit. It is an architectural mismatch.

SaaS management tools see software licenses. AI spend lives in API bills, developer tools, browser extensions, corporate expense reports, and features embedded inside existing products. These are entirely different data surfaces that require entirely different instrumentation. Plugging an AI module into a SaaS management platform is like plugging a fuel gauge into an electric car — the architecture does not support the measurement.

Organizations that rely on SaaS management for AI visibility will continue to have blind spots in exactly the places where AI spend is growing fastest: usage-based consumption, shadow purchases, and embedded features.

Getting Started

Coriven delivers AI observability through the Proof Snapshot — a complete AI spend audit with findings, waste identification, governance scoring, and an actionable recommendation set. The engagement is fixed-scope, fixed-price, and designed to deliver value in the first week.

The Proof Snapshot is the fastest path to AI visibility. No six-month implementation. No platform migration. No disruption to existing workflows. Connect your data sources, and Coriven maps the landscape. Start the intake process here.

Frequently Asked Questions