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Startup Ascerta collects $18M in funding to help enterprises understand the value AI provides

From SiliconAngle

By Mike Wheatley

September 30, 2026

Startup Ascerta collects $18M in funding to help enterprises understand the value AI provides

Startup Ascerta collects $18M in funding to help enterprises understand the value AI provides

Many businesses are spending enormous sums of money on artificial intelligence, but they don’t have any way to understand how much value those investments are returning — and that’s a challenge that a startup called Ascerta Inc. wants to solve after raising $18 million in an early-stage round of funding today.

Today’s Series A round was led by Dell Technologies Capital and saw participation from Hitachi Ventures, BGV and Wipro Ventures, bringing the startup’s total funding to date to $22.9 million.

Ascerta’s goal is to help businesses maximize their return on investment in AI, which is something that has proved to be a major challenge. Though most companies have no trouble counting their token consumption and measuring how many lines of AI-generated code they’ve generated, few are able to answer the very simple question: Which AI projects are actually creating value and making us money?

With traditional software, most companies were able to answer this question with basic financial operations or FinOps tools that shed light on how much they’re spending on things like cloud infrastructure resources. However, these tools aren’t very useful when it comes to trying to understand the real-world business outcomes achieved by an AI agent or coding assistant, said Ascerta co-founder and Chief Executive David Tepper.

According to Tepper, the only thing that most businesses see is “meaningless vanity metrics,” counting things like token consumption and agent runs. “They’re struggling to derive the real impact AI has on their business,” he explained. “We built Ascerta to cut through the noise and give organizations the means to win in the AI-era with insights specific to their business, people and use cases.”

Ascerta provides that guidance through a single, unified system that reveals exactly how a company is using AI, what the AI is doing and whether those efforts are translating into real revenue or savings. Its platform can connect to all of the most popular enterprise-grade AI tools, such as Microsoft Corp.’s CoConvoypilot, Anthropic PBC’s Claude, Amazon Web Services Inc.’s Bedrock and Salesforce Inc.’s Agentforce platform.

Once connected, it tracks the economics of AI down to the individual users, teams and applications. It can even unmask the hidden fees, enterprise discounts and sub-token costs missed by other tools, Tepper promised. “[We provide] purpose-built tools to prevent waste and aggressively optimize AI for value,” he said.

There are three specific components of Ascerta’s platform. Atlas measures AI adoption, the value it generates and its return on investment. Forge provides insights into how engineering teams are using coding agents and how they affect their productivity. Finally, Convoy helps companies to understand how much computing resources they’re using for AI, so they can consolidate workloads and optimize where possible without disrupting their business operations.

Ascerta has already helped companies like Atos SE and Wipro Ltd. to reap the rewards of their early AI investments, improving their return on investment by an average of 47% while reducing AI agent launch times by 24% and cutting wasted AI resource spending by 86%.  Atos Group Chief AI Officer Florin Ratar said Ascerta’s platform has been “instrumental” in helping his company to scale its Sovereign Agentic Studios initiative, providing it with “visibility and control.”

Ramana Khanna, managing director of Dell Technologies Capital, said he’s backing Ascerta because it’s building a system of record for AI value creation. “Most enterprises are moving beyond broad AI experimentation and focusing their investments on what delivers measurable business value,” he said. “Ascerta is giving leaders the visibility and rigor they need to understand what’s working, optimize spending and scale their most successful AI initiatives.”

View original article on siliconangle.com

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