We believe data work
is broken.
Not the data. Not the people. The tools.
Here’s what we see, what we believe, and what we’re building instead.
A decade of better pipes.
The same broken faucets.
The modern data stack gave us faster ingestion, cleaner transformations, bigger warehouses. A $200 billion infrastructure industry built to move bytes from A to B faster than ever.
But the tools where analysts actually think? Those barely changed. Jupyter shipped in 2014. Most BI dashboards are still pixel-perfect reports that nobody updates after week two. The gap between infrastructure and insight keeps growing.
We built world-class plumbing and left the analysts reading tea leaves.
Data teams are drowning in tools — one for querying, one for transforming, one for visualizing, one for sharing, one for governing. Each tool sees a sliver of the picture. None of them see the whole thing. And none of them learn.
AI made it worse
before it can make it better.
When LLMs arrived, the industry bolted “AI-powered” onto everything. Text-to-SQL. Code completion. “Chat with your data.” A thousand demos that work beautifully on sample datasets and crumble on real ones.
Here’s what most got wrong: generating code is not the hard part. Understanding what the code should mean is.
Ask an AI “What was our revenue growth last quarter?” and watch it fail. Not because the model is dumb — because it doesn’t know if you mean ARR or run-rate, which of your three revenue tables is authoritative, or that Q3 data should be ignored because of a migration. It generates confident SQL against the wrong table with the wrong definition.
The problem isn’t intelligence. It’s context.
Context doesn’t come from documentation.
It comes from work.
The industry’s answer is to build “context layers” — YAML files, metadata catalogs, ontology editors. Define everything upfront. Document every business rule. Then deploy your agent.
We think that’s backwards.
Every SQL query an analyst writes reveals which tables they trust. Every correction they make to an AI teaches it what “revenue” actually means. Every markdown cell explains business logic that no YAML file captures. Every conversation with an agent creates a trace of institutional knowledge.
The best context is behavioral, not declarative. It captures what people do, not what they say they do. And the best place to capture it is the surface where the work already happens.
The analysis surface and the context layer
should be the same thing.
This is our core belief. When you work in Qupid Eye, you’re not just analyzing data. You’re teaching the system what your data means.
Every query, every correction, every conversation with the AI agent becomes context that makes the next analysis more accurate. We call this progressive formalization:
No gates. No prerequisites. No six-month ontology project before anyone can ask a question. Just a system that gets smarter the more you use it.
Governance that people
actually want to maintain.
Every data team has a graveyard. Abandoned data dictionaries. Stale YAML definitions. Metadata catalogs that were heroically populated once and never touched again. The problem isn’t that people don’t care about governance — it’s that there’s no incentive to do it.
We flip the economics. In Qupid Eye, confirming that “revenue” means ARR from fct_revenue isn’t paperwork — it’s an investment that makes your agent more accurate on your next analysis. Every business definition you confirm pays you back immediately in better answers.
Governance fails when it’s a compliance chore. It succeeds when maintaining a definition directly benefits the person maintaining it. We don’t mandate governance. We make it self-interested.
The semantic model isn’t a documentation project your data team dreads. It’s a competitive advantage that grows from the natural desire to get better answers, faster. The artifacts everyone says you should produce? In Qupid Eye, people want to produce them.
AI that works in a black box
is AI that nobody uses.
We believe every AI action must be visible, reviewable, and reversible.
Trust isn’t a feature. It’s the foundation everything else is built on.
Charts are arguments,
not decorations.
Most tools give you a chart catalog: pick a type, drag some columns, adjust the colors. The chart is an afterthought — a screenshot for the slide deck. A pretty picture disconnected from the reasoning that produced it.
We think visualization is reasoning. The right chart for revenue-over-time is different from revenue-by-region. A distribution needs bins, not bars. A comparison needs consistent axes, not auto-scaled prettiness. A map needs real geometry, not colored rectangles.
Qupid Eye doesn’t offer a chart picker with a dropdown. It has a chart reasoning engine — the agent looks at your data shape, cardinality, types, and distribution, then produces the visualization that makes the argument clearly. Sixteen chart types, maps, pivot tables, KPI metrics — but you rarely choose one yourself. The system reasons about what your data is trying to say and encodes it accordingly.
A chart catalog asks “what type do you want?” A reasoning engine asks “what is this data trying to say?”
The flywheel
that compounds over time.
Individual features can be copied. A compounding cycle of context and accuracy cannot.
Every notebook interaction makes the AI smarter. Every agent correction teaches the system something new. Every analyst who joins adds their institutional knowledge to the shared context. The platform gets better the more it’s used — not through updates we ship, but through the work your team does every day.
A notebook is a thinking tool.
An app is a communication tool. Same source.
The gap between “I found the insight” and “the whole team acts on it” should be zero. Not “export to PDF,” not “rebuild it in Tableau,” not “schedule a meeting to walk through the notebook.”
Turn any analysis into a live, interactive data app. Add filters, KPI cards, charts, controls. Publish with one click. Embed anywhere. One source of truth, multiple ways to consume it. The notebook stays live — the app stays fresh.
When agents can execute almost anything,
the scarce resource is judgment.
The data professional’s job is changing. Not disappearing — evolving. When AI can write the SQL, run the analysis, and build the chart, what’s left is the part that matters most:
What question should we ask? Which data source do we trust? Does this result make business sense? Is this analysis complete?
We see data professionals becoming agentic architects — people who design the context, governance, and guardrails that make AI agents effective. Not typing queries. Steering intelligence.
Qupid Eye is the workspace for that future.
See what your data
is trying to tell you.
The tools are ready. The agents are waiting.
Your data already knows the answer.