Why use your data?
A model that never sees your records guesses. Company facts, metrics, and recent events get filled in with something that only sounds right.
Platform
Consequential Insights for the Enterprise
Enabling Super Intelligence on enterprise data converts proprietary assets—customer records, sales metrics, and domain knowledge—into a significant competitive advantage.
Grounded AI provides AI insights that are far more accurate and relevant than public-data models alone, uncovering patterns, accelerating decisions, personalizing experiences, optimizing operations, and reducing human review costs.
Unleash Super Intelligence on your data — safely
Ground answers in the data you already own, without sending it to a public model.
Companies that let their rich internal data remain siloed miss out on substantial value, while those that leverage AI against this data enhance their business intelligence and quickly gain an edge in performance, efficiency, and enduring advantage, all while ensuring data security.
Public models are not safe
Send customer records, financials, or IP to a public chat and you lose control of it. It can be logged, stored, or used for training.
Ungoverned AI
When everyone chats on their own, answers contradict, sensitive data leaks, and there is nothing to audit or improve.
Shared analysis
SEQUITUR replaces one-off chats with reusable prompts for dashboards, reports, and workflows — the same grounded query, every time.
Features that give you the Advantage
Reuse an answer you already paid for, share the review, and turn a finding into work someone owns.
Caching strategy
A successful prompt is stored in layers: the SQL, the rows, the written answer, and the chart. The next run reuses the deepest layer that is still valid. A full hit skips the model and returns that answer with zero tokens. A question the team asks again should not cost another wait or another run.
Preconfigured dashboards and analyses
A dashboard asks up to four questions and shows each result as a chart. An analysis asks related questions in order and ends with what to fix, what can wait, and what done means. Write those questions once for a topic and a horizon, and the team runs the same review. A shared configuration is how a best practice becomes the review everyone opens.
Vibe Charting
Ask for the data and the chart as two prompts, and see the chart as you change either one. When the data question stays the same, the rows are kept and only the chart is drawn again. You can explore the dataset in real time, trying the next chart without starting the query over.
Insights generation
Insights reads the schema of the dataset you selected and suggests five questions written for those tables. Run one, run all five, or save them as a new analysis. Use it when you want questions the data can answer that you had not written yet.
Action Planner
A finding on an analysis section, or on the conclusion, becomes a project with a title, tasks, priorities, and dates. Each task is assigned to a person, with a due date and a definition of done. An insight only changes the work if someone is accountable for it.
Custom dashboards and analyses
- Create and edit Manager and creator roles test prompts, then create dashboards and analysis reports in a new topic or by editing one that already exists.
- From insights Turn generated insights into a new analysis.
- Access by role Roles decide who can open the pages you create.
- Cached for everyone else Those prompts are reused and cached for the people who run them, so the next visit costs less time and no tokens.
Security
Built so that private data stays private.
Public chat tools either guess without your data or send your data to someone else’s servers. SEQUITUR is the third path: grounded queries, in an environment you control.
Control
- No shared pool Imported data is visible only to people you grant access.
- VPC when you need it The enterprise install runs in your AWS virtual private cloud.
- Your keys Use your own enterprise LLM API keys. All Frontier and Open models are supported.
The model boundary
- Not for training Customer data is not used to train general-purpose models.
- Not raw exposure Your data is not directly exposed to the underlying model in training or inference.
- Governed prompts A shared library replaces one-off chats that leak data and contradict each other.
Pricing
Pay for use, subscribe, bring an engineer to customize.
Prices in USD
Pay per use
$50
Per million total tokens, counting input and output. Start with $100 of usage credit. Buy more by credit card or Cash App.
Subscription
$2,000
Per user, on an annual contract, with capped token usage. $1,000 per user per year when you bring your own LLM.
Embedded engineer
$20K
80 hours. Annual contract value, payable when the custom solution is delivered and signed off. Most of this work lands within a month.
For a walkthrough on your own data, email .
Questions
Short answers.
How do I let SEQUITUR access my data?
SEQUITUR normally deploys a staged dataset of your actual data. Import CSV, Excel, or a MySQL dump, or connect MySQL. You can also deploy an ETL script or configure an automated Data Pipeline, or run SEQUITUR in your AWS VPC against your private database. Each source becomes a dataset you select for questions, dashboards, and reports.
How secure is my data?
People you do not grant cannot see imported data. Enterprise deployment can sit in your AWS VPC. Your data is not directly exposed to the underlying model in training or inference, and you may use your own Frontier or Open model keys.
Can I have a private demo on my data?
Yes. Email with the situation — IT Operations, Revenue Operations, or both — and we will scope the demo.
Does it only answer from my data?
Chat and reports are grounded in the selected dataset and the prompt result. SEQUITUR is designed not to fill company-specific gaps with invented facts. You still review outputs before you act on them.
What does sovereign super intelligence mean?
An administrator chooses the language model for the agents at every level of the analytics work. Any Frontier or Open model can be configured. The price is higher when we supply that model.
How does SEQUITUR help me save time and cost?
A successful prompt is stored in layers: the SQL, the rows, the written answer, and the chart. The next run reuses the deepest layer that is still valid. A full hit skips the model and returns that answer with zero tokens, so a question the team asks again does not cost another wait or another run.