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Start from your data

Already have data in Google Sheets, HubSpot, Stripe, or another tool? You don’t need to describe an app from scratch. Connect your data and Gainable’s DataAnalyzer agent will examine it, recommend features, and build a complete app around it. Your existing data becomes the specification. Instead of writing a prompt that describes your app from scratch, the AI reads your actual schema from your datasets, detects what domain you’re in, and proposes an app tailored to your data.
This path is ideal when you already have data in external systems and want to build views, dashboards, and workflows on top of it.

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How it works

1

Connect your data source

Choose from 15+ supported sources including Google Sheets, HubSpot, Stripe, Airtable, and Supabase. Authenticate via OAuth or API key.
2

Create a dataset and sync

Group your sources into a dataset and sync the data into Gainable. You can connect multiple sources to combine data from different systems.
3

Start a new project from your data

On the new project page, choose Build from data and select one or more datasets.
4

AI analyzes your data

The DataAnalyzer agent examines your schema, sample rows, and field types. It identifies the domain (CRM, project management, finance, etc.) and recommends features matched to your data patterns.
5

Refine through conversation

The agent asks 2-3 clarifying questions through interactive multiple-choice options. You shape the app through a short Q&A conversation.
6

Your app is built

The Build Agent generates a complete application with views, dashboards, and collaboration features, pre-populated with your synced data.

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What AI analyzes

When you connect data, the DataAnalyzer agent examines:

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Domain detection

The agent recognizes common business domains and tailors its recommendations:
  • CRM / Sales — Pipeline boards, deal value dashboards, contact card grids
  • Project management — Kanban boards, timeline views, progress tracking
  • Finance / Payments — Revenue dashboards, transaction tables, KPI cards
  • HR / People — Employee directories, org charts, onboarding trackers
  • Inventory / Operations — Stock level dashboards, order tables, status tracking

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Interactive refinement

The DataAnalyzer doesn’t just guess. It asks you to confirm and refine through interactive options:
After analyzing a Google Sheet with deal data, the agent asks:Question 1: “I see a sales pipeline with 5 stages and deal values. What should be the primary view?”
  • Pipeline Board (kanban by stage)
  • Revenue Dashboard
  • Deal Table
Question 2: “How should I handle the ‘Owner’ field?”
  • Show as a filter on the pipeline board
  • Create a separate view grouped by owner
  • Both
Question 3: “I can add these collaboration features. Which ones?”
  • Comments on individual deals
  • Team activity feed on the dashboard
  • AI copilot for sales questions
Each answer updates the app specification before building begins.
The conversation typically takes 2-3 turns before the spec is finalized. You’re always in control of what gets built.

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What gets built

The output is a complete, working application with features matched to your data:

Views matched to data

Kanban boards for pipeline enums, dashboards for numeric fields, card grids for people data, tables for transactional data

Charts and KPIs

Automatic visualization of key metrics based on currency, count, and date fields in your data

Collaboration

Comments, chat, and activity feeds placed in context based on the domain

AI copilot

An AI assistant configured with knowledge of your data domain, ready to answer questions

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Supported data sources

More connectors are added regularly. If you need a source that isn’t listed, reach out via the live chat in the bottom-left corner of the builder or email support@gainable.dev.

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CRM

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Databases and spreadsheets

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Analytics and product

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Payments and data warehouses

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Build from data vs. describe your app

Not sure which path to choose? Here’s how they compare:
You can combine both approaches. Connect data sources first, then describe additional features you want on top of them in the prompt.

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Next steps

Set up datasets

Detailed guide to creating datasets and connecting data sources

Quickstart

Try the prompt-based path instead

How it works

Understand the full agent pipeline

Data models

Learn how your data is organized