Resolve ad-hoc customer requests automagically

Customization & control

Fine-tune LLMs on your unique data and tailor the experience to match your brand.

Tracking & management

Monitor usage and impact across all your diverse, personalized chat experiences.

Performance & reliability

Serve relevant, precise and thorough answers with minimal response latency.

Observability & trust

Share the reasoning behind each response to help users and data teams iterate.

Safety & security

Apply fine-grained access controls with strong permissioning and SAML SSO.

Connect your data and start chatting with it immediately

Embed to meet users wherever they need data

Continuously learn and improve with usage

Why it matters

Partner Success

Ecommerce

Partners can't run on hallucinated data

Our brands need to self-serve on our inventory data and be able to trust it. Partners can't run on hallucinated data — a great AI Chat experience needs to work

IT Security

Financial Services

Safety and security are our top priorities

We work diligently to protect our clients' assets and interests. The data we share with them needs to stay private. Safety and security are our top priorities

Product Management

Logistics & Supply Chain

Supply chain lives and dies by data sharing

Transparency is critical for our upstream and downstream partners, and without self-serve data access we all suffer and eat the costs.

Customer Success

SaaS technology

Our customers need more from us

Our customers love our product, but we're too slow responding to their data needs. It sucks - we're unnecessarily losing customers

Sharpen your competitive edge

Embed AI Chat to accelerate time-to-market for your revenue generating AI product roadmap, while focusing your expensive technical investments on higher value core competencies.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Connect chat to any data source

Snowflake
Looker
Redshift
Tableau
BigQuery
dbt
Postgres
PowerBI
Databricks
Looker
Azure
Sheets
Google Drive
Facebook
Linkedin
PDFs
Dynamics
Box
Sheets
Slack
Salesforce
Snowflake
Teams
Jira
Microsoft Dynamics
Box
Salesforce
Sheets
PDFs
Slack
Dynamics
Snowflake
Teams
Jira
Microsoft Dynamics
BigQuery
dbt
Hubspot
Looker
PDFs
Dropbox
CSVs
Quickbooks
Quickbooks
Youtube
Xero
Quickbooks
Dropbox
Quickbooks
Quickbooks
Quickbooks
Youtube
Xero
Quickbooks
Dropbox
Quickbooks

Embed to answer any question

News & articles

June 20, 2023

What's new with Inventive: 2023 the year of AI

Read More
June 20, 2023

LLMs and the future of app development

Read More
June 20, 2023

The anatomy of a smart data app in 2023

Read More

Frequently asked questions

1.  What is a Data App Platform?
Expand

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.

2.  What is Inventive used for?
Expand

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.

3.  Why use Inventive vs. Embedded analytics?
Expand

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.

4.   How does Inventive use AI For its Data App Platform?
Expand

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.

Request a free trial

Inventive AI Chat is in Private Alpha — Try us on your data.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.