The way your best people work—without the queue. Spotonix captures what your company means, lays out its plan before anything runs, and keeps the definitions your team supplies.
Three business outcomes
Ask → Show → Remember changes more than how an answer is produced. It changes how much hidden work each question creates for your team.
01 Human ControlAsk
When company meaning is missing, Spotonix asks before it runs—so your team fixes the interpretation, not a polished answer built on the wrong one.
> Which product categories do priority customers prefer?
02 Visible LogicShow
The person responsible for the number can inspect the interpretation and planned analysis before execution, then follow the logic and sources behind the result.
Plan before execution
03 Compounding KnowledgeRemember
A definition your team supplies becomes context the workspace can reuse. The next question starts with more of what your company already knows.
> How many priority customers are there?
Not just faster answers. Less hidden work behind every answer.
The work around the answer
Your chosen AI can analyze data. Your experts still supply company meaning and check whether the answer is safe to use. Spotonix makes that work visible and reusable.
Claude, Gemini, and OpenAI models keep getting better at reasoning and query generation. They do not arrive knowing what your company means by “active customer,” which source your team accepts for revenue, or what someone clarified last week.
Spotonix adds that company context, shows the analytical plan before execution, and keeps newly supplied definitions available for reuse.
Better models make Spotonix better—not obsolete.
Works with supported Claude, Gemini, and OpenAI/Codex paths. Keep the model and data stack your team already chose.
See supported models and integrations →Your chosen AI
Reasoning and query generation
Company context + evidence
Spotonix
Your existing data stack
Definitions
Models
Sources
One inspectable path from company meaning to answer.
What it means for you
You are a
business user
Ask the follow-up while the question is still live. See how the answer was interpreted and bring something concrete into the business review. The path goes through your data team’s definitions—never around them.
Sales · Marketing · Operations · Finance
You lead
data and analytics
Start with the definitions and models your team maintains. Every answer brings its plan and sources, so your experts can review instead of reconstruct. Newly supplied context becomes reusable across the workspace.
Head of Data · VP Analytics · BI and Data Product Leaders
You are the
analyst everyone asks
Clarify what the question means before it runs. Inspect the interpretation, logic, and sources. Let the next question start with the context you already supplied. Your knowledge scales past your Slack inbox without making you the gate on every request.
Analysts · Analytics Engineers · Embedded Data Partners
How it works
One question. One inspectable path from business meaning to answer.
01 Human ControlAsk
Ask the question the way the business asks it. Spotonix starts with the definitions and analytical context already available. If “priority customers” is not grounded, it asks what the term means instead of silently choosing one.
New analysis
> Which product categories do priority customers prefer?
What should “priority customers” mean?
02 Visible LogicShow
See the terms, assumptions, filters, grain, planned analysis, and where each concept came from. The person responsible for the number can correct the course before execution—and inspect the logic and sources behind the result afterward.
Which product categories do priority customers prefer?
Review the meaning and analysis before it runs.
03 Compounding KnowledgeRemember
When your team supplies a new definition, Spotonix keeps it as workspace context. The next question can start with that meaning instead of starting from zero.
New analysis
> How many priority customers are there?
Priority customers
Customers with at least 5 orders across store, web, and catalog channelsCount customers that match the team-supplied definition.
The company learns, not just the session.
The questions that come back every month
What changed? Why? What needs attention now? Your team answers those every week—and rebuilds much of the reasoning each time, because the definitions and exceptions live in someone’s head or last quarter’s deck. Spotonix keeps the interpretation attached to the question.
Review 1
Ask and clarify“Priority customers” has no shared meaning yet. Your team supplies one.Review 2
ReuseSame question, fresh data, and the team-supplied definition is already available.Review 3
CompareThe new period reads against the same interpretation, so the change becomes the story.Questions that return every review
Same questions. Fresh data. The reasoning does not restart.
Proof, not another polished demo
Bring 10 real questions your team would not trust a general AI to answer. Run your current path and Spotonix against the same questions and an agreed rubric.
Path A · Baseline
The tools and people your team relies on today—including the work required to decide whether the answer is safe to use.
QuestionThe frozen business question
Drafts + handoffsTools, queries, reports, and people
Human verification + repairCheck meaning, source, logic, and exceptions
Reviewer decisionUse, clarify, refuse, repair, or fail
Human trust work is part of the baseline, not invisible overhead.
Path B · Spotonix
Run the identical set without changing the reviewer, expected disposition, or what counts as right.
QuestionThe identical frozen question
Interpret + show the planAsk where company meaning is missing
Logic + sourcesExpose the basis and supporting artifacts
Reviewer decisionUse, clarify, refuse, repair, or fail
The product exposes the path; the agreed rubric determines whether it helped.
Same questions · Same reviewer · Same rubric
What you can inspect in the product
If your current path is already better on the questions that matter, Spotonix should not win.
Bring 10 real questions. Same questions, both paths, failures included.
Built by people who have lived the problem
Venkatesh Seetharam
Co-founder & CEO
Created Apache Atlas, the first open-source data catalog. It showed us how companies document what their data means—and why a catalog alone cannot interpret the question someone asks today.
Read Venkatesh’s profile →
Harish Butani
Co-founder & CTO
Built OLAP on Spark—the Spark-native analytical engine developed at SparklineData and acquired by Oracle. He also built analytical engines across Oracle, SAP HANA, and Apache Hive, turning business intent into systems that can execute it.
Read Harish’s profile →Spotonix brings those two histories together: company meaning before execution, and an analytical path the accountable team can inspect.
Outside perspective
A practical first step
Freeze 10 real questions from one bounded business domain. Compare your current path and Spotonix. Results within two weeks of data-ready. Data-ready means the access path, credentials, bounded domain, and supported model or semantic reference are confirmed.
How many times you re-ask, rephrase, or re-run before the answer is usable.
The hours analysts spend interpreting, repairing, and defending each answer.
From the question being asked to a result your reviewer will put their name on.
You choose what matters. We show every interpretation, intervention, and failure. If Spotonix does not create a measurable advantage on the metric you chose, stop.
One domain. Same questions. The person who decides what counts as right. Failures included.
