Ascerta
by Ascerta (formerly Pay-i)
Enterprise AI management: measure the ROI, cost and adoption of every AI initiative, agent and coding tool
Ascerta, formerly Pay-i, is an enterprise AI management platform for CIOs, CFOs and AI leaders who must prove which AI investments pay off. It connects to tools like Microsoft Copilot, Amazon Bedrock AgentCore, Salesforce Agentforce, GitHub Copilot and Claude Code, and ties their usage and cost to business KPIs.
Ascerta is a Bellevue, Washington startup founded in 2024 by three Microsoft veterans: CEO David Tepper, who led generative-AI strategy for Azure's internal use, CTO Doron Holan, a 27-year Microsoft architect of Windows and Azure's throttling layer, and COO Erik Winters. It launched as Pay-i in May 2025 with a $4.9M seed co-led by Fuse Partners and Tola Capital. That product was a real-time dashboard linking every model call, prompt and token to business outcomes such as revenue conversion, task completion time or CSAT, with production A/B testing of models and prompts and an ROI forecasting engine. In September 2026 it rebranded as Ascerta and raised an $18M Series A led by Dell Technologies Capital, with Hitachi Ventures, BGV and Wipro Ventures, for $22.9M in total. The platform now has three products. Atlas measures AI value, adoption and ROI by business unit and portfolio. Forge tracks how engineering teams use coding agents such as GitHub Copilot, Claude Code and Codex. Convoy helps organisations with provisioned AI capacity consolidate workloads and scale use cases without disrupting production. Ascerta measures cost below the token level, including hidden fees and negotiated enterprise discounts, and breaks it down by person, team and tool. Instrumentation uses a Python SDK (decorators and context managers), a proxy and request headers, plus a REST ingest API, with documented OpenAI, Azure OpenAI/Microsoft Foundry, Databricks and n8n integrations. Named customers are Atos and Wipro. Partners include Microsoft, AWS, IBM, Slalom and Trace3. It sits between FinOps cost tools and LLM observability platforms.
A CIO, CFO or Chief AI Officer at a large enterprise running several AI platforms (Copilot, Agentforce, Bedrock, coding agents) who must show the board which AI spend is producing business value.
Per-user, per-team and per-use-case AI cost tied to business KPIs, so leaders can cut wasted AI spend and fund the initiatives that pay back.
At a Glance
- Category
- Governance & Security
- Pricing
- Contact for pricing
- Target Market
- CIOs, CFOs, Chief AI Officers, Engineering Leaders
- Deployment
- Cloud-first, API-based
- Founded
- 2024
- Headquarters
- Bellevue, USA
Key Features
- ✓Atlas
Measures AI value, adoption and ROI across workflows, business units and portfolios, and isolates initiatives that waste money.
- ✓Forge
Tracks how engineering teams use coding agents and connects adoption to development productivity, guiding behaviour and rollout decisions.
- ✓Convoy
Gives real-time visibility into provisioned AI capacity, identifies workloads to consolidate, and shows which use cases can scale safely.
- ✓Sub-token cost accounting
Calculates true cost per model call, including hidden fees and negotiated enterprise discounts, broken down by person, team and tool.
- ✓KPI-linked use cases
Assigns dollar or time values to business KPIs per use case, so model, agent or prompt variants can be compared on business return.
- ✓SDK, proxy and limits
A Python SDK, a proxy and a REST ingest API instrument AI traffic, and configurable limits cap GenAI usage and spend.
Capabilities
Use Cases
- •AI portfolio review
A CIO compares Copilot, Agentforce and in-house agents on cost and business KPIs to decide which programmes to expand or cut.
- •Coding agent rollout
Engineering leaders see which teams actually use GitHub Copilot, Claude Code or Codex and whether adoption improves delivery.
- •Model and prompt A/B testing
Product teams run model or prompt variants in production and see which delivers the strongest return on a chosen KPI.
- •AI spend governance
Finance teams set usage limits and attribute AI spend to departments, catching cases where teams using identical models get very different results.
Ideal For
Best For
- ✓Measuring ROI across a portfolio of enterprise AI initiatives and agents
- ✓Tracking coding-agent adoption and productivity (GitHub Copilot, Claude Code, Codex) across engineering teams
- ✓Allocating AI cost below the token level, including enterprise discounts and hidden fees, to teams and users
- ✓Optimising provisioned AI capacity before scaling new use cases
- ✓Giving finance, technology and business leaders one shared view of AI spend versus outcomes
Not Ideal For
- ✗Teams that only need LLM tracing, debugging and evals; dedicated LLM observability tools go deeper on engineering telemetry
- ✗Buyers who need independently verified ROI; the headline 47% ROI improvement and 86% waste reduction are company-reported with no published methodology
- ✗Organisations needing published pricing or third-party certifications up front; neither appears on Ascerta's site
Integrations
Deployment
Market Analysis
Pros
- ✓Founders bring deep Microsoft Azure GenAI-consumption and hyperscale-throttling experience
- ✓Covers the AI platforms enterprises already run, from Copilot and Agentforce to Bedrock and coding agents
- ✓Designed as a shared view for CIOs, CFOs and AI leaders rather than an engineering-only tool
- ✓Backed by strategic investors (Dell, Hitachi, Wipro) with Atos and Wipro as named customers
Cons
- ✗Headline results (47% better ROI, 24% faster agent launches, 86% less waste) come without sample size, period or methodology (FourWeekMBA)
- ✗AI value attribution across multi-system agent workflows is structurally harder than cloud cost allocation, so accuracy claims need validation (Efficiently Connected)
- ✗Developer-level tracking such as Forge can be perceived as surveillance and hurt adoption
- ✗Early-stage company with a newly self-defined category, no public pricing and no published security certifications
Pricing
Enterprise
Contact for pricing
- ✓Atlas, Forge and Convoy modules
- ✓Integrations with 38+ AI, data and development tools
Ascerta does not publish list pricing on its website or docs. Buyers engage through a sales demo, and none of the funding coverage reported prices, so module packaging (Atlas, Forge, Convoy) and metering basis are unknown and must be confirmed with sales.
Security & Compliance
Sources
This page was written from 9 sources, 8 on domains other than ascerta.com.
- 1.ascerta.com — ascerta.comvendor
- 2.docs.ascerta.com — docs.ascerta.com
- 3.siliconangle.com — startup ascerta collects 18m in funding to help enterprises
- 4.thenextweb.com — ascerta 18m series a dell enterprise ai roi
- 5.unite.ai — ascerta raises 18m series a to turn enterprise ai spend into
- 6.fourweekmba.com — ai pay i ascerta series a ai value
- 7.efficientlyconnected.com — ascerta enterprise ai management series a
- 8.crowdfundinsider.com — 239931 pay i lands 4 9m to tackle genai issues
- 9.globenewswire.com — ascerta raises 18m to help enterprises maximize ai roi
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