Your AI has no idea what good looks like.

Your AI produces code that looks production-ready but doesn't conform to your standards. Invisible drift compounds with every release, costing you rework, refactoring, and velocity.

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Trusted by over 4k+ companies

The Knapsack difference

Anyone can put documents in a folder and point an LLM at it. The hard part is knowing which context to trust, measuring whether your AI's answer conforms to your standards, and proving it got better—not just faster. That is the whole product, and it is genuinely defensible.

Three Principles That Enable Evals

Measurement is the foundation. It reveals what's working, what's drifting, and what governance you need.

Source-defined

Provenance

Every evaluation starts with your source of truth. Your design system. Your patterns. Your decisions. We bring them together as-is, never rebuilt, never reinterpreted. We trace every conformance check back to what you own and control.

MEASURED

Analytics

You'll know if your AI is improving, where it's drifting, and what it costs to fix the drift. Design fidelity. Code quality. Cost per shippable output. Not just whether it works, whether it works for you.

GOVERNED

Reliability

Governance isn't bolt-on. It's built in from the start. When your AI produces output, Knapsack checks it against your standards. That's what makes it safe to ship, not just fast to generate.

One source of truth for context, not scattered across tools

Your standards live across your tools—GitHub, Figma, Jira, Supernova, Storybook. Your AI needs them all to understand what good looks like. Knapsack brings them together as-is, without rebuild. Then we measure whether your AI conforms to what you've already decided.

Problem

Your context is real but fragmented

Your code lives in GitHub. Your components live in Figma. Your specs live in Jira. Your design tokens live somewhere else. Your AI sees access to tools, not understanding of decisions.

Solution

Your standards are already decided. We measure against them.

Bring your own design system, unified or distributed across your tools. We ingest it as-is, normalize it into measurable standards, and evaluate whether your AI conforms to what your organization has decided.

How it works

Step 1

Collect

Pull everything your AI needs to understand what good looks like for your product. Your design system. Your component library. Your standards. From GitHub, Figma, Jira, Supernova, Storybook, wherever your context lives today. No rebuild. No normalization step on your end. We ingest your standards as-is and use them as the baseline for measurement.

Step 2

Connect

Knapsack ingests your context, normalizes it into source-defined knowledge, and makes it reliable and governed. Your AI now understands not just what tools exist, but what decisions they represent.

Step 3

Evaluate

Here's where most AI platforms stop: they measure speed. We measure conformance. Does your AI's output align with your design system? Is it introducing drift? What's the design fidelity, code quality, and cost per shippable output? Evals show you what's working and what's not, so you ship with confidence, not hope.

Step 4

Scale

Deploy the context and standards you've built across your entire AI workflow. Push decision-based governance through our intelligent tools. Every output informed by your system. Every update measurable. Live today.

Step 5

Learn and Improve

Continuous learning loop. AI improves based on eval feedback, org standards evolve based on real-world outcomes. Positions Knapsack as an ongoing intelligence system, not one-time assessment.

Evals outcomes

60%

Less cost to land
a governed build

~7 in 10

drfited AI styling decisions detected

95%

Fewer invented
token names

100%

AI reached for the design system unprompted

Let’s get started

How much does your AI actually know?

Your product has standards. Your decisions matter. Take a quick assessment to discover how well your AI understands what 'good' looks like for your team.

Learn more

Measure what matters, before you ship.

Design fidelity. Code quality. Conformance to your standards. Evals show you what's working and what's not—so you ship with confidence, not hope.

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Where we learn together

Patterns is our thought leadership hub. Live demos. Workshops. Conversations about the future of AI context. It's where we show up for our community, and where you show up for us.

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Measuring AI quality

How do you actually measure AI quality? We're trying to understand how organizations define and measure AI-generated code quality. Your input helps us build better solutions.

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