Product
Spotonix makes company meaning visible
before analysis runs.
Keep the model and data stack your team already chose. Spotonix captures what your company means by a question, shows the analytical plan before execution, and keeps new definitions available for the workspace.
Ask
When company meaning is missing.Show
The interpretation, plan, logic, and sources.Remember
Definitions your team supplies.Company context → explicit plan → inspectable answer → reusable learning
One product · three behaviors
Ask. Show. Remember.
Each step removes hidden work.
The product does not begin and end with a generated query. It makes company meaning explicit before execution and keeps the answer basis available to inspect afterward.
01 Human ControlAsk
Surface the missing meaning.
When a business term cannot be grounded, Spotonix can ask your team instead of silently inventing a definition.
> Which categories do priority customers prefer?
02 Visible LogicShow
Make the interpretation reviewable.
See the terms, assumptions, filters, grain, and planned analysis before execution—then inspect the logic and sources behind the result.
Plan before execution
- Segment
- Priority customers
- Group by
- Product category
- Measure
- Purchase count
03 Compounding KnowledgeRemember
Start the next question with more context.
A term your team supplies can become workspace context, so a later question does not have to start from zero.
> How many priority customers are there?
The company learns, not just the session.
The product boundary
See what Spotonix thinks you mean
before it queries your data.
The plan turns a plausible interpretation into something the accountable person can actually read. It is the bridge between the question and the generated analysis—not a confidence badge added afterward.
What becomes explicit
- 01The business interpretation
Which meaning of the terms the analysis will use.
- 02The analytical shape
Population, measure, filters, grouping, grain, and assumptions.
- 03The answer basis
Concept origins, generated logic or SQL when present, sources, and result artifacts.
Visible work does not guarantee a perfect answer. It gives your team something concrete to check.
Which product categories do priority customers prefer?
Interpretation
priority customers
Customers with at least 5 orders across store, web, and catalog channels.
Defined by your teamAnalytical plan
- Segment
- Priority customersDefined by your team
- Group by
- Product categoryFound in your model
- Measure
- Purchase countAssembled by Spotonix
- Grain
- Customer × categoryAssembled by Spotonix
Read the interpretation and plan. Missing meaning is visible before the analysis runs.
Inspect the basis of the result. The answer does not have to hide behind a chat response.
What changes in the current workflow
Take the hand-teaching and answer-checking work
off the critical path.
Your experts still decide what the business means and what counts as right. Spotonix changes how often they have to reconstruct, repeat, and defend that meaning one question at a time.
The current path
AI interface + expert trust work
The tools and people your team relies on today—including the work required to decide whether the answer is safe to use.
With Spotonix
Company context + visible plan + evidence
Run the same question with the interpretation visible before execution and the answer basis available to inspect.
Reused from workspace context A new team-supplied term can remain available as workspace context for later questions.
Works with your choices
Keep the model your team already chose.
Add the context it does not have.
Spotonix is the analytical context and evidence layer around supported AI paths and your configured data stack. It is not a replacement model, warehouse, or BI system.
01
Your chosen AI
Claude, Gemini, or OpenAI/Codex
Use a supported model path for reasoning and generation. Exact behavior depends on the configured path.
02
Spotonix
Company context, plan, and evidence
Resolve the question against available meaning, ask where it is missing, and expose the answer basis.
03
Your data stack
Data and analytical assets
Connect the supported warehouse, models, queries, and artifacts agreed for the engagement.
Your model + Spotonix company context and evidence + your data stack
Inspectable product boundaries
Know what you can test today.
Know what you are not being asked to assume.
Inspect in the product
The work behind the answer
- Interpretation, terms, filters, grain, and assumptions
- Plan provenance for every answer: team, model, question, workspace, or Spotonix
- Generated logic or SQL when present
- Sources and supporting result artifacts
- Clarifications, failures, and expert interventions
Keep human judgment
Your team still decides what counts as right
- No promise that every question resolves automatically
- No claim that visible SQL proves correctness
- No organization-wide author, approval, version, or conflict history for definitions
- No assumption that a plausible answer is safe to use
Confirm per engagement
The exact deployment and support path
- Model and warehouse compatibility
- Credential and network boundaries
- Supported source and semantic artifacts
- Retained artifacts, logs, and telemetry
- Identity and access behavior
A practical product test
Prove it on the questions
that matter.
Freeze 10 real questions from one bounded domain. Run your current path and Spotonix against the same questions and agreed rubric. Inspect the wins, the interventions, and every failure.
Bring
A test grounded in your work
- 10 frozen real questions
- One bounded business domain
- Your current path
- The person who decides what counts as right
Run
The same questions through both paths
Inspect
One evidence pack, failures included
- Interpretation and expected disposition
- Logic, sources, and result evidence
- Retries and expert interventions
- One buyer-selected measure of value
If Spotonix does not create a safer, more useful path with a measurable advantage on the metric you chose, stop.
One domain. Same questions. Same rubric. Every failure visible.