Data analysis
you can actually
put your name on.
Quantiri reads your dataset and writes a finished intelligence report — where every number is computed, never guessed. The statistics are done by code; the AI only writes them up. You get a board-ready report in minutes, not days.
77 products. 6 brands. Five findings — none on the front of the pack.
The label only shows you one.
When you check fibre, you are predicting a second nutrient with 81% accuracy.
“You do not need to read two columns. One number already tells most of the story.”
Most AI tools guess.
Quantiri computes.
The difference is architectural. Statistics are computed first, deterministically. Language is applied second, constrained to what was computed.
Statistics first. Language second.
Every mean, correlation, effect size, and share is computed deterministically by the signal emitters before the language model runs. The LLM receives the numbers — it never invents them.
Ranked by impact, not p-value.
Findings are ordered by how much they would change a decision, gated on real statistical thresholds. The most important finding is always first — nothing is buried in an appendix.
Zero hallucination, by architecture.
The narrative pass is constrained to the evidence payload: if a number isn't in the computed evidence, it cannot appear in the report. This is enforced structurally, not promised in copy.
This is what your
report looks like.
50,000 device users · 4 occupation groups · 2 platforms
Professionals log a quarter of all weekend screen hours — yet Android and iOS split the market almost perfectly in half
Two very different stories live inside this dataset. On one side, occupation carves up digital time with surprising sharpness — Professionals alone account for 25.4% of all weekend screen hours across four groups that should, in theory, share it equally. On the other, the platform war that has defined a decade of tech strategy turns out to be essentially a draw: Android holds 50.16% of devices, iOS 49.84%, a gap of just 160 users across 50,000. The data is telling us that who you are professionally shapes how much you stare at a screen on Saturday, but the device in your pocket is essentially a coin toss.
Weekend screen time × Occupation
Professionals account for 25.4% of all weekend screen hours — the single largest share among four occupation groups that together cover the entire dataset
App usage count × Occupation
The same Professional group drives 25.3% of total app usage, confirming that occupation is the master variable for digital engagement intensity
Device type split
Android leads iOS by just 160 devices across 50,000 users — a 0.32-percentage-point margin that is statistically indistinguishable from parity
Gender distribution
Male, Female, and Other each hold almost exactly one-third of the sample, making gender the most balanced dimension in the entire dataset
Executive summary
In 50,000 users, occupation is the only dimension that actually divides people — everything else is a coin toss
Professionals dominate both weekend screen time and app launches with a signal strength six times greater than gender and twice that of device type — occupation is the variable that earns attention. Platform, gender, and every other demographic cut in this dataset resolves to statistical noise; the question worth asking is not who someone is, but what they do for a living.
Key findings
Weekend screen time by occupation
0.47App usage count by occupation
0.46Device type split
0.22Professionals own a quarter of all weekend screen hours across just four groups
Imagine four occupation groups splitting a weekend equally — each should claim roughly 25% of total screen time. Professionals do claim 25.4%, but the gap between the top group and the bottom is 1,676.9 hours in aggregate, and the p90-to-p50 spread of 1,396.2 hours dwarfs the p50-to-p10 spread of just 281.1 hours, meaning the concentration lives entirely at the top. The data is not describing a gentle lean; it is describing a ceiling that only one occupation group consistently touches.
101,609.1
Professional weekend hours
Largest single-group total
25.38%
Share of total
Of 50,000-user dataset
1,396.2 hrs
P90–P50 gap
Top-to-median spread
75.18%
Top-3 combined share
Three groups, three-quarters of hours
3 more findings — plus interactive charts, spectrum analysis, and the full closing
Generated from 50,000 real user records in under 5 minutes.
Three moments.
One finished report.
Not a process. Not a workflow. Three distinct moments that replace what used to take your team an entire week. Each one is complete in itself.
Drop it in.
Any shape. Any mess.
CSV. Excel. Direct API call. A database export you haven't cleaned. Quantiri doesn't require tidy data — it reads what you have. Column names don't need to be perfect. Missing values don't need filling.
Quantiri reads
what actually matters.
Most data tools show you everything. Quantiri finds the handful of patterns genuinely worth a decision-maker's attention — and ranks them by how much they would change what someone does next, not by statistical significance.
A finished document.
Walk in ready.
The report arrives as a complete intelligence document: written chapters with narrative prose, interactive charts embedded where the story needs them, and a prioritised list of actions. Copy the link. Walk into the room.
Six components.
One fewer thing
to build yourself.
Every piece of a Quantiri report exists because it eliminates a task your team used to do manually. Nothing is decorative. Every feature has a specific job it does so you don't have to.
Prose that reads like
a person wrote it.
Findings arrive as structured chapters with narrative body text — not a bullet list, not a table, not a summary. Written clearly enough that any stakeholder can follow it without a briefing.
The most important finding is always first — ranked by how much it would change a decision, not by p-value. Nothing is buried. Nothing requires hunting.
Charts appear inside the narrative, right where the text references them. No flipping to appendices. Hover over any data point. Filter. Explore.
Where a finding benefits from exploration, the report embeds a focused widget — a spectrum comparison or a calculator you can adjust to test the numbers against the computed evidence yourself.
The report streams in as it's written. No spinner, no blank loading state — watching the reasoning emerge in real time makes every finding easier to trust.
Every report closes with numbered, sequenced actions — ranked by impact, grounded in evidence, written so anyone on the team can present them without re-explaining the analysis.
Same data.
Different reality.
What goes in on the left. What comes out on the right. The only thing that changes is whether Quantiri is in between.
77 rows × 16 columns. The answer is in here. Somewhere.
The interesting part isn't
that it uses AI.
Everything uses AI now. What decides whether the output can be trusted is the system around the model — and that's where Quantiri is engineered to a different standard.
The same dataset always yields the same numbers. Statistics are computed by code, not generated by a model.
Nothing surfaces on fluency alone. A pattern must clear real significance and effect-size bars to become a finding.
Stages run independently. One degrading never takes down the report — it simply contributes less.
One typed schema flows from the analysis engine through the API to the rendered report. The contract is checked, not hoped for.
The ones people
actually ask.
Still unclear? Write to hello@quantiri.io — we reply to every message.
Not yet — Quantiri is in early access, opening in small cohorts. You can read a complete, real report in the live demo today, and join the early-access list to run it on your own data. We'll reach out when your spot is ready.
Stop presenting data.
Start presenting answers.
Your next meeting has a dataset behind it. Your team already ran the analysis. The only question is whether the story gets told clearly — or not at all.
Early access is opening gradually · No card required