95% of enterprise AI deployments fail. We've cracked the code on why, and how to be in the 5% that succeeds.
After evaluating AI initiatives across 50+ Fortune 500s and Silicon Valley companies, we've identified the root cause: enterprises aren't failing at AI because of the technology – they're failing because they're using the wrong AI approach for their specific problems.
Some teams build custom AI where simple configuration of a latest generation LLM would have worked. Others force off-the-shelf solutions onto problems requiring deep institutional knowledge. The result? Millions wasted, teams demoralized, and boards losing faith in AI.
What’s missing is an objective rubric to select the right solution and approach for each AI use case – paired with strong evals to measure whether the solution actually delivers against your objectives.
Think of it as an SAT for your AI. We are calling it the GAT – GenAI Assessment Test.
We’re launching the GAT Design Service: a vendor-agnostic diagnostic to help AI leaders build a custom GAT for their initiatives. Your domain-specific GAT provides a structured way to you evaluate AI solutions and measure its improvement.
We'll also tell you which combination of Approach (off‑the‑shelf, framework, or custom) and Capability Mode (Search, Act, or Solve) will help achieve the best GAT scores, most economically using our proven 3x3 framework.
What you get in 5 working days working with an AI engineer that has shipped AI projects in production:
- A custom GAT: Evals and a scoring rubric across accuracy and NFRs like latency
- 3x3 recommendation: The approach x capability mode that meets your bar most economically, with risks and trade‑offs
A GAT and its recommendation report makes vendor claims and in-house solutions testable, aligns stakeholders and shortens the path from PoC to production.
The 3 key pillars to our methodology:
- Evals are your only defense against AI snake oil: Evals are well known amongst AI researchers and engineers. However, we've realized that it is in fact the most critical asset for an AI leader to create clarity in driving a GenAI project. Whether it's a product that is purchased or a solution that is built in-house. Evals are the only way to define the scope of an AI system, and the only way to assess its current capability level.
- Institutional knowledge determines the approach: Every Gen AI system that integrates with proprietary data and systems is dependent on integrating institutional knowledge (documented, tribal or tacit). Understanding the extent of that dependence, and the lifecycle of that institutional knowledge is critical to evaluating the right AI approach.
- Mapping workloads to LLM output types optimizes model family: Today there are 3 primary types of workloads that map directly to generative outputs that LLMs are optimized for, viz. 1) user-defined natural language output 2) tool calling output & 3) domain-specific language output (eg: code). We ensure your workloads map directly to what LLMs actually do well, making you resilient to constant model updates and also continuously benefit from these updates.
Interested in a GAT for your AI initiative? Reach out to us to book your GAT Design Service.
