A decision support system is human-in-the-loop AI for the moves that matter: market entry, pricing, capacity, a major hire. We build the scenario models on top of your certified metrics so an executive can change an assumption and see the downstream effect, with the inputs traced back to a trusted source. The point is not to automate the call. It is to give the person making it data-backed confidence instead of a gut feel dressed up in a slide.
Human-in-the-loop AI for high-stakes decisions: scenario models built on certified metrics so executives can test assumptions and see defensible, traceable outcomes.
A decision support system is a human-in-the-loop analytics tool that helps people model major decisions. It lets an executive change an assumption, like price or volume, and see the downstream effect on certified business metrics, with every input traceable to a trusted source so the recommendation can be defended.
A decision support system is human-in-the-loop analytics for high-stakes moves like pricing, market entry, or capacity. Thinklytics builds scenario models on your certified metrics so an executive can change an assumption and see the downstream effect, with every input traced to a trusted source. The system frames the trade-offs; the person still makes the call.
Scenario models built on your certified metrics, so changing an assumption updates a number the whole company already agrees on.
Traceable. Every input the model uses links back to a governed source, not a one-off spreadsheet.
Human-in-the-loop. The system frames the trade-offs; the person makes the call and owns it.
Focused on the few decisions worth the work: pricing, market entry, capacity, large investments.
A chatbot that produces a confident answer with no model behind it.
A standalone forecasting tool disconnected from your real metrics.
A replacement for judgment. It gives the decision-maker better inputs, not a verdict.
A scenario model for a specific high-stakes decision, built on your certified metric definitions.
An interface where a decision-maker changes assumptions and sees the downstream effect, with inputs traced to source.
Documented assumptions and sensitivity ranges, so the recommendation survives scrutiny in the room.
An enablement transfer so your team can re-run and extend the model for the next decision.
Revenue discrepancy resolved. Six conflicting metrics reconciled to one certified ARR definition. The data foundation that makes sales AI work.
School districts unified onto one automated reporting pipeline in a single engagement.
Tableau Server response time after rationalization. $6.2M migration avoided. The clean foundation reporting automation runs on.
There was no model to test the assumptions, so the loudest opinion won.
The board asked what happens if volume drops 15 percent and nobody could answer live.
The scenario logic lived in a one-off spreadsheet, not a model anyone could run.
Two teams modeled the same decision and got different answers.
Each used its own metric definitions, so the inputs never agreed.
The inputs were not traceable, so the output could not be defended.
A decision support system is a human-in-the-loop analytics tool that helps people model major decisions. It lets an executive change an assumption, like price or volume, and see the downstream effect on certified business metrics, with every input traceable to a trusted source.
No. The system frames the trade-offs and shows the downstream effects of each assumption. The person makes the call and owns it. We deliberately keep a human in the loop for high-stakes decisions.
A dashboard shows you what happened. A decision support system lets you test what might happen: change an assumption and see the effect on the metrics that matter, with the inputs traced to source so the recommendation holds up under scrutiny.
If the model reads from un-certified numbers, two teams modeling the same decision get different answers. Building on a certified semantic layer means the inputs already agree, so the debate is about the decision, not the data.
The few that are worth the work: pricing changes, market entry, capacity planning, large investments, major hires. Not day-to-day operational reporting, which a dashboard already handles.
You do. We document the assumptions and sensitivity ranges and run an enablement transfer so your team can re-run the model and build the next one without us.
Scenario models run on certified metrics. These are the factors that move the effort.
A single pricing decision models differently from a multi-factor market-entry or capacity call.
Scenario models run on certified metrics; if they are not defined, that comes first.
More levers and sensitivity ranges mean more modeling and validation.
A simple assumption-and-output view differs from a rich interactive model.
You want an executive to change assumptions and see the downstream effect.
You need documented assumptions and sensitivity ranges that survive scrutiny.
You want ongoing questions answered from dashboards: see Agentic BI Implementation.
Your metrics disagree across reports: start with a Semantic Layer.
You need monitoring of live operations: see Real-Time Data Observability.
Ask the model a question in plain language and get a traced answer.
Score whether your data can support trustworthy decision models.