Explain & Explore
AI Decisions everyone can explain
Answer key questions about how and why AI decisions were made. Store reports for future audit or generate custom scenarios on the fly.
Complex model decisions are rendered in easy to understand interpretations using the latest in interpretation techniques and methods.
Once your explanation is rendered, you can easily substitute values and data to understand the full scope of the decision.
Get quality results with high throughput explainable models that store results automatically.

Explainability built into each project

Your project is backed by an explainable model used to help your team make transparent decisions and store analysis for future audit.

  • Automated reporting: Our explainability engine will generate and store an explanation for every decision.
  • Traceable decisions: Surface the tasks that most contributed to any outcome, on-demand.

Find peace of mind in feature analysis

Understand the potential strength and fit of your model based on local expressions of feature importance.

  • Make changes that matter: See which values are most contributing and detached to find new opportunities to improve your model.
  • Measure the impact of transparency: Easily demonstrate model progress across your organization.

Where decisions meet data

No more guessing. Understand how specific data values impact model outcomes. Edit the values in real-time to product custom scenarios that articulate model needs.

  • Worry-free version history: We create and store a report for every data row analyzed by Apres.
  • Edit in real-time: Adjust your data as it's presented and re-run your analysis for better understanding.

"Apres helps our AI team achieve the high-confidence results that we need. It cuts down our development time significantly."

Nazar Hembara, Chief Executive Officer at BotsCrew


Control quality with your entire team

Apres makes it simple for anyone on your team to understand, diagnose and take action on model behavior. Collaborate with your team to adjust and examine custom scenarios.

Explore Data Management
Other ways to use explainable model results
  • Validate compliance against industry regulations.

  • Create reports to share with your team members.

  • Experiment with different mix to impact feature weights.

  • Interact with data attributes to impact pre-processing

Build transparency into your model from start to finish