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ByJamie Thompson·Founder & CEO, Sprinklenet·March 7, 2026·4 min readUpdated May 2, 2026

AI Vendor Evaluation

Most AI vendor selections go wrong for a simple reason: buyers overvalue the demo and undervalue the operating model. Good evaluation means testing the platform on your data, your workflows, and your control requirements.

• Start with a sandbox, not a slideshow.
• Evaluate integration, governance, and deployment flexibility, not just features.
• Choose the vendor team and trajectory, not just the current product screen.

Sprinklenet sits on both sides of enterprise AI deals. We build and sell an AI platform, and we also evaluate tools continuously for our own stack and for clients through fractional AI leadership engagements. That perspective makes one point very clear: most AI vendor evaluations focus on the wrong things.

Teams get pulled toward benchmark claims, polished demos, and architecture diagrams that look impressive but reveal very little about what happens when real users hit the system at scale. The more reliable path is to evaluate how the platform behaves under your conditions.

Jump to:Sandbox FirstQuestions That MatterBenchmark On Your WorkloadEvaluate The Vendor

Start With A Sandbox, Not The Demo

Every vendor has a polished demo environment. That is expected. The important next step is understanding how the product performs outside that environment.

Ask for a sandbox. Bring representative data. Use real tasks. Let your team spend a week trying to make the platform useful on an actual workflow. If a vendor is confident in the product, they will welcome that level of scrutiny.

This phase matters because it reveals the things a demo hides: integration friction, user workflow mismatches, data quality assumptions, latency under normal usage, and the shape of the support model when the system does not behave perfectly.

Ask The Questions That Matter

Surface-level evaluation checklists miss the details that determine long-term value. The questions worth asking go deeper than feature checkboxes.

Data Handling

Where does the data live? What happens in transit and at rest? Can the customer control encryption and deletion at contract end?

Model Flexibility

Is the platform locked to one provider, or can the customer switch models without rebuilding prompts and workflows?

Governance

How granular is the logging? Can the buyer see which model, which context, and which user produced each response?

Deployment

Can the platform run in the buyer’s cloud, on-premises, or in a restricted environment if the mission requires it?

These questions matter because they expose maturity. Vendors that answer them with specifics have usually built for enterprise use. Vendors that stay vague are often still selling aspiration.

Benchmark On Your Workload, Not Theirs

Published benchmarks are fine for orientation, but they are insufficient for your use case. A model that performs well on generic public tests may behave very differently on your internal documents, your acronyms, and your operational edge cases.

1

Build A Real Evaluation Set

Take 50 to 100 questions that users would actually ask. Include edge cases and cases where the correct answer is that there is not enough information.

2

Score What Actually Matters

Measure accuracy, citation quality, latency, refusal behavior, and hallucination rate on your own material.

3

Test The Control Model

In government and regulated environments, evaluate prompt injection handling, scope control, and whether the system stays inside authorized boundaries.

This takes effort. It is still one of the highest-value parts of the evaluation process because it replaces marketing confidence with operational evidence.

Evaluate The Vendor, Not Just The Product

Products evolve. The vendor behind the product determines the trajectory of that evolution.

• Engineering investment. Is the company clearly investing in product depth or primarily in sales motion?
• Customer mix. Does the vendor work with organizations that have similar constraints to yours?
• Integration depth. Can the platform connect to your systems without extensive custom reinvention?
• Pricing clarity. Do you understand what usage actually drives cost before you sign?
• Compliance path. If you need stronger controls over time, is there a funded roadmap or just vague intent?

An AI platform is not just a software choice. It is a partnership choice with long-term operational consequences.

The best evaluations treat vendor selection with the same rigor applied to any strategic platform decision: due diligence, clear success criteria, and a bias toward long-term alignment rather than short-term demo quality.

About the authorJamie Thompson is the founder and CEO of Sprinklenet. He has been an AI entrepreneur for over twenty years, having started one of the first computer vision companies in the early 2000s in Boston. For the past fifteen years he has consulted to CEOs, investors, and senior executives, working with venture investors, startup founders, and large companies on strategy and implementation of their strategic AI initiatives. He often leads and manages development teams directly. Today he is increasingly focused on growing Knowledge Spaces, Sprinklenet’s middleware control and configuration layer that helps enterprises, government agencies, and startups manage their knowledge and the knowledge of their clients. Connect with Jamie on LinkedIn.