October 8, 2024

Confident AI Closes Seed Round to Build the Testing Infrastructure AI Teams Actually Need

By Jeffrey Ip, CEO & Co-Founder · 7 min read

Production AI failures do not announce themselves. They accumulate in user feedback tickets, in support queues, in the quiet churn of customers who stopped trusting the product. That is the gap Confident AI was built to close, and today we are announcing our Seed Round to do it at scale.

We are not going to disclose the investment amount, but we will tell you exactly what we are building with it: deeper evaluation infrastructure, expanded model coverage, and an enterprise tier designed for teams deploying LLMs in regulated industries.

Why This Problem Does Not Get Solved by Adding More Prompts

The current dominant approach to LLM quality is informal: a developer writes a few test prompts, eyeballs the outputs, and ships. This works until the model provider updates their weights, the system prompt drifts during a sprint, or a new user population surfaces use cases the internal team never considered.

What actually works is treating LLM evaluation the same way software teams treat unit and integration testing: systematic, automated, version-controlled, and blocking. You define what correct looks like. You run those definitions on every build. You stop deploys that fail.

The reason most teams do not do this is not laziness. It is that the tooling did not exist. Evaluation was either hand-rolled and brittle or expensive consulting engagements. Confident AI fills that gap with a platform any engineering team can adopt in a sprint.

What We Have Built So Far

Since our private launch earlier this year, we have run over 10,000 LLM evaluation suites across teams in financial services, healthcare, enterprise software, and consumer AI. A few things stand out from that data:

• Hallucination rate variance across providers is large. For the same task, we have measured hallucination rates ranging from 3% to 31% across commercial LLM APIs. Model card benchmarks do not predict real task-specific performance.
• Prompt injection is underestimated. In our red-teaming coverage, 76% of production chatbots we evaluated had at least one exploitable prompt injection vector that internal teams had not caught.
• Regression happens silently. Provider model updates do not always come with changelogs that predict quality regressions. Automated evaluation on every deploy is the only reliable defense.

Our platform today handles automated test suite execution, multi-layer hallucination detection, CI/CD gating, and quality metric dashboards. We support OpenAI, Anthropic, Mistral, Llama 3, Gemini, and any model accessible via a REST endpoint.

What the Seed Round Funds

Three priority areas:

1. Evaluation coverage depth. We are expanding our built-in evaluator library from the current 40+ metrics to over 200, with specialized evaluators for RAG pipelines, agentic workflows, and multi-turn conversation quality. Assessing a single-turn chatbot and an autonomous agent require fundamentally different test strategies.

2. Enterprise compliance layer. Teams in finance and healthcare need evaluation results that are auditable, signed, and traceable to specific model versions and deployment timestamps. We are building that audit trail natively into the platform.

3. Team scale. We are tripling engineering headcount and opening a research function dedicated to evaluation methodology. If your background is in ML evaluation, interpretability, or AI safety, we want to talk to you.

A Note on Why Menlo Park

We chose to stay in Menlo Park deliberately. The concentration of foundation model teams, enterprise AI adopters, and research institutions here is unmatched. We want to be where the people building production AI systems actually are, not where the conferences happen to be held.

3000 Sand Hill Road is also a useful reminder that evaluation infrastructure is not an academic exercise. The investors who work here deploy capital based on confidence in outcomes. We help AI teams develop that same confidence about what they are shipping.

What This Means for Existing Customers

Nothing changes in pricing or access for teams already on our Developer and Team plans. The Seed Round funds R&D and go-to-market for the enterprise tier. If you are on Developer today, your plan terms stay exactly the same.

We will announce specific product releases on this blog. Subscribe below if you want them when they publish, not when they make it to HN.

Building the Evaluation Layer That AI Infrastructure Deserves

The software engineering discipline spent decades building testing infrastructure: unit tests, integration tests, load tests, security audits, static analysis. That discipline is what makes it possible to deploy software to billions of users without catastrophic failure rates.

AI engineering is still in its "we'll just test it in production" phase. That is not sustainable as AI moves into healthcare decisions, financial advice, legal research, and customer-facing applications where errors have real consequences.

Confident AI exists to bring the same rigor that software testing brought to code deployment to the question of what happens when your LLM answers a real user's real question. We are just getting started.