DATA & DECISIONS
A dashboard nobody opens isn't analytics. It's decoration.
Stop drowning in dashboards. Our AI doesn't just visualize your data, it surfaces insights you didn't know to look for and tells you exactly what to do next.
Why it matters
The case for doing this now.
Most companies aren't short on data. They're short on time to read it. The dashboards proliferate, the weekly reports get longer, and the actual decisions still come down to a gut call in a meeting because nobody had time to dig.
We build analytics that work the other direction: an AI layer that watches your data continuously, surfaces what changed and why, and tells you what to do - in plain English, in the channel you already use.
What’s included
How we ship this.
Unified data layer
Pipelines that pull together the systems you already pay for - CRM, billing, product analytics, ad platforms - into one queryable source.
Custom dashboards built around your workflow
Not a generic template. The metrics you actually run the business on, in the layout your team will actually open.
Anomaly detection and proactive alerts
Slack or email pings when something meaningful shifts - with the context, not just the chart.
Natural-language analyst
Ask questions in plain English and get back charts, breakdowns, and a written summary - without learning SQL or pinging the data team.
Data points
The numbers behind the case.
Sources are linked beneath each number. Items marked typical range come from our own engagements rather than a published study.
~20%
of knowledge-worker time spent searching for information instead of acting on it
typical rangeAIIM / McKinsey range
Days → minutes
typical compression in time-to-insight after AI analytics rollout
typical rangeWhat we've seen
<30%
of legacy BI dashboards see weekly active use after launch
typical rangeIndustry estimate
Where this shows up
What this looks like in practice.
An e-commerce brand running on Shopify, Klaviyo, and Meta Ads
We unified spend, attribution, and product margin into one daily view, and added an AI summary that flags which SKUs and creatives are quietly losing money. Decisions about ad budgets moved from a monthly meeting to a same-day shift.
Representative engagement
A B2B SaaS company drowning in feature-usage data
Built a usage map showing which 12% of features were driving real engagement and which were dead weight. The product team reprioritized the roadmap and killed two underused modules without a guess.
Representative engagement
Next step
What would you do if your data answered back?
Bring us the question your dashboards never quite answer. We'll prototype an AI analytics layer against your real data in days, not quarters.