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.
Quantiri77 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.
1,470 employees · 9 job roles · 3 departments
Attrition is a role problem: Sales Representatives leave at 39.8%, Research Directors at 2.5%
Every company tells itself that people leave for personal reasons, unpredictably, one resignation at a time. This dataset says otherwise: who quits is written into the org chart, with 33 of 83 Sales Representatives gone (39.8%) against 2 of 80 Research Directors (2.5%), and overtime nearly tripling the odds of a departure. Attrition here is not weather — it is geography, and the map has exactly three hot zones: low pay, low level, and long hours.
attrition × job role
The revolving door has a name tag — 33 of 83 Sales Representatives left (39.8%), 15.9 times the Research Director rate.
income × job role
Your job title is your paycheck — role alone explains 81.6% of income variance, a $14,556 monthly gap between Manager and Sales Representative.
attrition × overtime
Overtime is the quiet exit sign — 127 of 416 overtime employees left (30.5%) versus 110 of 1,054 without it (10.4%).
Executive summary
People don't leave this company at random — new, junior, underpaid employees working overtime do
Five independent cluster analyses corroborate one another: attrition concentrates where pay, level, and tenure are lowest and hours are longest, peaking at 39.8% for Sales Representatives against a 16.1% baseline. Everything else in the org chart — pay, seniority, even department walls — moves in lockstep with the same hierarchy.
Key findings
attrition × job role
0.98monthly income × job role
1.00attrition × overtime
0.77Four in ten Sales Representatives walk out; one in forty Research Directors does
Walk the sales floor and you are looking at this company's revolving door. 33 of 83 Sales Representatives left (39.8%) — nearly two and a half times the 16.1% company baseline — while only 2 of 80 Research Directors (2.5%) did the same. The gap between the two ends of the org chart spans 37.3 percentage points, and the chi-square test leaves no ambiguity (χ² = 86.2, p ≈ 0).
39.8%
Sales Representative attrition (33 of 83)
2.47× baseline
2.5%
Research Director attrition (2 of 80)
0.16× baseline
16.1%
Baseline across 1,470 employees
237 total leavers
9 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.
QuantiriSix 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 nibras@tryquantiri.com — 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