> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dacard.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Benchmark against industry

> Read your scores against peers by stage, size, and sector.

<Note>
  Raw scores need context. Benchmarks tell you what 52 means at seed vs. Series B vs. enterprise. Use them to set realistic targets and to give resource conversations a defensible ground truth.
</Note>

# Benchmark against industry

As a leader, you want to read your scores against peers so you can set realistic targets, justify investment, and read where your team sits relative to the market.

## How benchmarking works

We compare your scores against the aggregate dataset of all scored products, segmented by company stage, team size, and product category. Benchmarks give you the context raw scores can't.

## Reading relative performance

A score of 52 means different things depending on context.

| Company stage       | Read on 52                                                                  |
| ------------------- | --------------------------------------------------------------------------- |
| **Pre-seed / Seed** | Strong. Ahead of most early-stage teams.                                    |
| **Series A**        | On track. Typical for teams that have established initial process.          |
| **Series B+**       | Below expectations. Teams at this stage typically land 60+.                 |
| **Enterprise**      | Concerning. Enterprise teams with resources should sit at Scaling or above. |

## Access benchmark data

<Steps>
  <Step title="Score your product">
    You need at least one scored product to benchmark. Head to `/score` and run a score if you haven't already.
  </Step>

  <Step title="Open your maturity report">
    Open the scored product's report page.
  </Step>

  <Step title="Ask DAC for benchmarks">
    Hit **Ask DAC** and ask: "how does my score compare to Series B companies?" or "is a score of 52 good for a 15-person team?"
  </Step>
</Steps>

## Key benchmarking dimensions

Not all dimensions matter equally at every stage. Focus benchmarking on the dimensions that matter for your current phase.

<Tabs>
  <Tab title="Early stage (seed to Series A)">
    **Priority dimensions:**

    * Delivery velocity (can you ship fast?)
    * Experience design (is the product usable?)
    * Customer signal synthesis (are you listening to users?)
    * Market intelligence (do you read your market?)

    Teams at this stage shouldn't worry about low scores on cost and token economics or team orchestration. Those become critical later.
  </Tab>

  <Tab title="Growth stage (Series B to C)">
    **Priority dimensions:**

    * All early-stage dimensions plus:
    * Data flywheel (are you building data moats?)
    * Product analytics (do you measure what matters?)
    * Roadmap discipline (can you say no to the right things?)
    * Architecture and systems (can your platform scale?)

    This is the stage where operational maturity separates winning teams from struggling ones.
  </Tab>

  <Tab title="Scale stage (Series D+, enterprise)">
    **Priority dimensions:**

    * All growth dimensions plus:
    * Team orchestration (can you coordinate across 50+ people?)
    * Process iteration (do processes improve themselves?)
    * Cost and token economics (are you managing inference cost at scale?)
    * Pricing and packaging (does monetization reflect value?)

    At this stage, every dimension matters. Systemic weaknesses cap your ceiling.
  </Tab>
</Tabs>

## Using benchmarks in planning

### Setting targets

Use benchmarks to set realistic moves.

1. **Find your stage peers.** Ask DAC: "what's the typical score range for a Series B team with 20 engineers?"
2. **Surface where you trail.** Compare dimension by dimension. Focus where you sit more than 1 point below the benchmark.
3. **Set quarterly targets.** Moving a dimension from 1 to 2 is achievable in one quarter. 2 to 3 typically takes two.

### Justifying investment

Benchmarks give resource conversations a defensible ground truth.

* "Our delivery velocity reads 1.5 while the Series B median is 2.8. CI/CD and testing infrastructure investment would close this gap."
* "Our data flywheel reads 1 point below benchmark. This is capping our ability to build features that compound."
* "Our GTM function averages 1.8 while peers average 2.5. We need dedicated product marketing resources."

## Benchmark caveats

<AccordionGroup>
  <Accordion title="URL-only scores have limits">
    Benchmark data includes both URL-only and adapter-enriched scores. If you're scoring URL-only, your scores may run lower than teams with adapters connected. That doesn't mean you're worse; it means we have less evidence.
  </Accordion>

  <Accordion title="Industry context matters">
    A developer tools company will naturally score higher on Development dimensions than a healthcare company. Use benchmarks as directional guidance, not absolute targets.
  </Accordion>

  <Accordion title="Scores tighten over time">
    Our scoring engine improves as the platform processes more products. Minor score moves between assessments may reflect engine improvements, not product changes. We surface engine refresh moves in the changelog so you can tell them apart.
  </Accordion>
</AccordionGroup>

## What's next

<CardGroup cols={2}>
  <Card title="Track your portfolio" icon="chart-mixed" href="/knowledge-base/leader-portfolio">
    See how benchmarks apply across your entire portfolio.
  </Card>

  <Card title="Run the full diagnostic" icon="magnifying-glass" href="/knowledge-base/leader-diagnostic">
    Go past benchmarks with a cross-framework diagnostic.
  </Card>

  <Card title="Share with investors" icon="share" href="/knowledge-base/founder-share">
    Frame benchmark data for investor and board conversations.
  </Card>

  <Card title="Coach with DAC" icon="message-bot" href="/knowledge-base/ic-coaching">
    Ask DAC to build a plan grounded in your benchmark gaps.
  </Card>
</CardGroup>