Platform

Decision intelligence for AI-native product teams

The first platform that treats the entire product team as a measurable, improvable, compounding system. Three layers, five unique capabilities, one compound intelligence flywheel.

Score live today · Intelligence Platform + Compound Engine on the roadmap

Most teams are bolt-on, not AI-native.

They see AI-powered features in their product and conclude they are on their way. They are not. They are bolt-on companies with good marketing.

Bolt-on
AI features live in a separate section
Data architecture built for CRUD, not ML
AI automates existing workflows
Per-seat pricing ignores AI economics
AI is a separate team, separate roadmap
AI-native
AI is woven into every interaction
Data strategy designed for machine consumption
AI creates entirely new value
Pricing captures AI-driven value delivery
AI capability embedded across the org

The honest question is not "Are we using AI?" It is "Could our product exist without it?" If the answer is yes, you are bolt-on.

Insights and recommendations, not dashboards

Dacard doesn't just measure. It analyzes signals across all six team functions and surfaces prioritized recommendations with evidence and financial attribution.

29/40
AI-First
SampleCo high confidence
Value Prop
4/4
Architecture
3/4
Data Strategy
3/4
Team
3/4
Moat
2/4
Legacy Curious Enhanced AI-First Native
01

Automated Scoring

AI evaluates your product team across 20 dimensions using structured rubrics. Every score is backed by evidence, not opinion.

02

Prioritized Recommendations

Get actionable, prioritized recommendations specific to your team's maturity stage. Know exactly what to improve next, and why.

03

Coaching and Roadmaps

90-day transformation roadmaps tailored to your gaps. Benchmark your team against anonymized industry data to understand what "good" looks like.

A typical strategy engagement costs $200K+ for a one-time snapshot. Dacard provides continuous, self-serve intelligence starting at $49/mo for individuals, $299/mo for teams.

Connect once. Intelligence everywhere.

Your stack feeds signal into Dacard. AI correlates across functions, scores decisions, and compounds organizational learning.

Connected Sources
Linear
GitHub
Figma
PostHog
Intercom
Slack
Stripe
HubSpot
Attio
Jellyfish
MCP / REST / Webhooks
Dacard Intelligence
Structured. Evidence-based. Agent-accessible.
Scoring Engine
Cross-Function Correlation
Citation & Traceability
Decision Intelligence
Financial Attribution
Coaching Layer
3
Frameworks
30
Dimensions
8
MCP Tools
Consumers
Dashboard
MCP Server
REST API
Webhooks
Claude / ChatGPT
Slack / Teams
Reports
Analytics

Intelligence that compounds

The Compound Intelligence Flywheel is the core product concept. Each revolution of the cycle makes the next faster, more accurate, and more valuable.

1
Score
Assess maturity across 20 dimensions
2
Connect
Integrate your tools and data sources
3
Correlate
Cross-function signal intelligence
4
Act
Agent-driven automation and briefs
5
Learn
Track outcomes against decisions
Score (better)
Historical data improves every assessment
A competitor can copy the framework. They can't copy 12 months of your team's decision intelligence data.

Three layers that compound

Start with a free score. Scale to connected intelligence. Unlock compound learning.

Live

The Score

Paste any URL or take the 20-question assessment. AI scores across 20 dimensions with evidence, confidence levels, anti-pattern detection, and stage classification. Free, shareable, instantly valuable.

20 dimensions · 6 functions · Free
Coming Soon

The Intelligence Platform

Connect the tools your team already uses. Cross-function signal correlation, citation and traceability, decision intelligence scoring, financial attribution, and coaching. Not a dashboard. A decision intelligence system.

5 unique capabilities · Connected sources
Roadmap

The Compound Engine

Autonomous agents that don't just observe, they act. Auto-create briefs when metrics drift. Auto-correlate launch outcomes with original hypotheses. Organizational learning that accelerates with every cycle.

Autonomous agents · Compound intelligence
Now The Score
Next Intelligence Platform
Future Compound Engine

Intelligence no single tool can provide

Five capabilities that don't exist anywhere else. Each one is powered by cross-function data correlation, not isolated metrics.

01

Cross-Function Signal Correlation

A competitive move detected in market data auto-triggers a design audit of the affected feature, which correlates with support ticket volume and feature adoption metrics. No human had to connect those dots.

Example Support tickets spiked 340% after last deploy. Correlated with adoption drop in PostHog and 3 related Linear issues. Auto-generated brief for product review.
02

Citation and Traceability

Every insight links back to the specific commits, designs, conversations, tickets, and metrics that generated it. Product leaders can trace the evidence chain and decide for themselves.

Example "Design system inconsistency" traced to 3 Figma files, 12 GitHub commits, and 3 duplicate Linear tickets across product areas.
03

Decision Intelligence Scoring

Measures whether what teams shipped was the right thing to ship. By correlating launch decisions with outcome data, Dacard builds a decision quality score that improves over time.

Example Q4 decision quality: 73% hypothesis match rate (up from 51% last quarter). 23 decisions tracked with outcome correlation.
04

Financial Attribution

Every insight carries a dollar estimate. Product ops stops being a cost center conversation and becomes an ROI conversation. Evidence your CFO can read.

Example Duplicated design effort across 3 product areas costs $340K/yr. Medium fix effort, 6-week payback period.
05

Coaching Layer

Explains what "good" looks like at the next maturity stage for your specific team, with anonymized patterns from the benchmark database.

Example To reach AI-First: connect 3+ data sources for cross-function intelligence. 82% of Stage 4 teams completed this within 60 days.

AI-native from the API out

MCP-first, agent-accessible, zero-trust. 8 tools expose every framework dimension to AI assistants.

The white space nobody owns

Every competitor measures engineering only, using pre-AI metrics. Dacard created the decision intelligence category for product teams. No one else covers all six functions.

Full Product Team Coverage ↑ AI-Native Focus →
Dacard
Jellyfish
LinearB
Swarmia
DX
DORA
McKinsey
Gartner

Every comparable measures engineering only. None measure the full product team. None measure AI-native maturity.

JellyfishEngineering management platform
LinearBEngineering analytics
DX (Atlassian)Developer experience surveys
DORA / SPACEDelivery and developer metrics

Dacard measures all 5 product functions. The other 4 have zero measurement tools today.

Team efficiency and effectiveness, across all six functions

Most tools measure engineering activity. Dacard measures what product leaders actually care about: whether your team is getting faster, building better, and driving the outcomes that matter.

Efficiency

Delivery speed
Quality signals
Decision patterns
Systems health
Collaboration
Team satisfaction

Effectiveness

Decision quality
Feature adoption
Revenue attribution
AI maturity
Compound rate
Market readiness

Connect your entire stack

Dacard integrates with the tools your product team already uses. More connectors shipping continuously.

Version Control

GitHub, GitLab

Project Mgmt

Linear, Jira, Asana

Communication

Slack, Teams

Analytics

Amplitude, Mixpanel

Dev Analytics

Jellyfish, LinearB, DX, Swarmia, Waydev

CRM / CSM

HubSpot, Attio

Custom / API

REST, MCP, Webhooks

Start measuring what matters

Take the free assessment or book a call to discuss decision intelligence for your product team.