Predictive answers without a data-science function
Say what you want to know or predict. Assertion builds it on your real data, deploys it on a schedule, and keeps it correct.
Licensed annually per team. Assertion Memory is included.
The problem
A dashboard tells you what happened. It rarely says what to do, and once someone acts on it, no one goes back to see whether the analysis was right. Keeping it fresh is real work, but freshness is not what makes an analysis worth having.
Maintenance is table stakes and we do it. The difference is that every result is a claim you can act on and check later, so the value of the analysis is measured rather than assumed.
Why memory matters here
Every assumption and recommendation goes into the same memory, with the outcome that followed.
See Analytics accessYou shipped the fix on the 9th [a0219]. Accounts whose checkout latency fell below two seconds retained 11 percentage points better than those that remained above it.
How it works
Upload your data as CSV, then say what you want to know or predict in plain language. Assertion explores the data, runs the analysis and builds the dashboard, report or model, showing its work as it goes.
Get a business-language read of what the data shows, backed by the exact SQL, the code, the steps tried and the reason behind each decision. Refine it through conversation.
Promote it to production in one step. Assertion provisions the schedule, runs it on fresh data, refreshes the result and keeps an auditable record of every run, then shares it with your team.
Whatever the question needs
The same workflow produces the right output, and deploys and maintains it.
Descriptive
Ask a question and get a dashboard or report built for you, refreshed on fresh data, monitored for change and shared with your team. No model and no code required. Most analytics is descriptive, and this is the fastest path to a result that stays live.
Predictive
When the question needs a forecast or a score, the same workflow trains a model and explains it plainly, across classification, regression and forecasting, including multi-table data. Feature engineering, tuning, cross-validation, calibration and driver explanations run underneath, so nobody on your team has to operate them.
How we are different
Generate code and can run unattended, but on infrastructure you own and monitor, with no accountability for whether the result stays correct.
Give analysts a place to build dashboards and run queries, by hand, every time. They do not auto-build from a question, refresh and maintain themselves, or train and deploy models.
Auto-builds dashboards, reports and models from a question, deploys them live, and keeps them refreshed and correct. Leverage for your team, without giving up control.
Proof
Both of these are live and open. Every result carries an auditable run record behind it.
A complete retention report produced by Assertion, published exactly as a team would receive it. Read what it concludes and how it argues.
Open the reportOur validation cases and the methodology behind them, published so you can judge the approach rather than take our word for it.
Read the methodologyData and analytics teams that need live, production analytics without standing up a machine-learning function, and owners who answer these questions themselves. Common uses are operations and KPI dashboards, daily refreshed reporting, churn and retention, demand and financial forecasting, and conversion and risk scoring.
Access
Analytics includes Assertion Memory. Pricing depends on your data and your team.
Assertion AI was built in the AI House, which is also an investor. Working on software rather than business data? Assertion Memory is free to start.
Questions
No. Most uses are descriptive, an auto-built dashboard or report that refreshes on a schedule and is shared with your team. Machine learning is there for the questions that need a forecast or a score.
A coding agent writes code you then have to deploy, monitor and maintain yourself. Assertion delivers a dashboard, report or model already deployed on your live data, monitored for drift and kept correct, with an audit trail your team controls.
No. Every query, decision and experiment is recorded in an evidence trail you can read and export, and the result is explained in business language.
Upload a CSV or a few, then describe what you want to know or predict.
Analytics is built on the same memory and includes it. Memory is our other product, for software work, and it is free to start.
The next step for AI
Analytics builds on the same memory that carries software work, so assumptions, recommendations and outcomes stay connected. We think this is the stage after agentic workflows, and the road to human-level reasoning in AI.