Companies of all sizes—from early startups to large enterprises—leverage AI in apps to drive outcomes
Turbocharge product experiences with AI to grow customer adoption, account expansion, and net new acquisitions.
Leverage LLMs to reduce development costs and accelerate time to market. Keep your team focused on your core.
Run more experiments with greater agility and tighter feedback loops to learn faster and execute smarter.
Connect any data source to start building apps immediately with beautiful out of the box components for Tables, Charts, Maps, Lists, Alerts, Automations and more—no engineering required.
Completely customize and integrate with full-code extensibility.
Let the people closest to the business problem build better solutions with the full trust of their technical teams.
Securely share or embed app experiences with fine grained access controls, and rest assured with performance monitoring, reliability safeguards and Git integration.
Manage all of your data apps across their full development lifecycle.
If you can't connect the real data, we'll automatically generate a synthetic data set so you can test live, functional prototypes for higher resolution feedback.
Run multiple experiments in parallel to better validate use case requirements and your app specifications.
When you're ready to push your app into production with real users, track usage analytics for insights and monitor logs to troubleshoot any issues.
To iterate, simply create a new branch, invite your collaborators, and run tests to make sure your improvements are working before you release them.
Extend smart data apps to your customers, and charge a premium for your most valuable features.
Leverage our monetization tools out of the box to offer custom pricing models and configurations.
A data app platform is software that helps people efficiently build solutions to business problems and make work easier. Think of it as a set of building blocks that are straightforward to assemble into useful things.
For example, consider a customer-facing usage analytics dashboard. Rather than requiring a software engineering team to build all the individual visualizations, filters, pivots, queries and access controls from scratch, companies can leverage pre-built parts out of the box and focus their technical teams on higher value work.
This often means fewer people—including non-technical domain experts—can build what they need for themselves and deliver better solutions faster and cheaper.
Inventive is for product and engineering teams across the application development lifecycle and shines most when helping those teams drive more and better 'product-market-fit.'
With Inventive's data app platform, they can rapidly: prototype with live data to learn detailed technical specifications; ship near real-time operational solutions at full scale; run multiple experiments in parallel across different user segments to validate user demand for various solutions; collaborate across teams and organizations to evolve the underlying data model with tighter feedback loops; monitor, manage and maximize custom app adoption, performance and reliability; tailor and monetize new features to specific customer segments, and so on.
Historically, companies considering the "build vs. buy" decision for their data experiences have often turned to "embedded business intelligence and analytics" (a.k.a. "embedded analytics") to provide largely static, informational reports and dashboards.
In contrast, Inventive's approachable and flexible data app building blocks lets companies provide higher value and higher complexity interactive application experiences, much faster and cheaper. Think: operational apps where users to get their work done (vs. limited and often generic charts) in days, not quarters.
Born in the "Era of AI", Inventive has been architected to accelerate teams looking to augment their users with "smart" AI-powered data apps.
Because Inventive sits on top of the modern data stack to help teams with the last mile of delivering great data app experiences, data science teams can write their model outputs to the database, or they can leverage integrations that pass those model outputs to Inventive directly.
Behind the scenes, Inventive also leverages LLMs and other ML models to help companies deliver their smarter data app experiences in easier and faster ways.