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 data models, the AI reads your actual schema, detects what domain you’re in, and proposes an app tailored to your data.How it works
Connect your data source
Choose from 15+ supported sources including Google Sheets, HubSpot, Stripe, Airtable, and Supabase. Authenticate via OAuth or API key.
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.
Start a new project from your data
On the new project page, choose Build from data and select one or more datasets.
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.
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.
What AI analyzes
When you connect data, the DataAnalyzer agent examines:| Signal | What it tells the AI |
|---|---|
| Field names | Domain context (e.g., “deal_amount” signals sales, “due_date” signals project management) |
| Field types | What UI to generate (currency fields get KPI cards, dates get timeline views) |
| Enum values | Pipeline stages, statuses, and categories that map to kanban columns or chart segments |
| Sample rows | Real data patterns that inform layout and feature recommendations |
| Cross-model relationships | How datasets connect (contacts to companies, tasks to projects) |
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
Interactive refinement
The DataAnalyzer doesn’t just guess. It asks you to confirm and refine through interactive options:Example: Refining a sales pipeline app
Example: Refining a sales pipeline app
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
- Show as a filter on the pipeline board
- Create a separate view grouped by owner
- Both
- Comments on individual deals
- Team activity feed on the dashboard
- AI copilot for sales questions
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
Supported data sources
More connectors are added regularly. If you need a source that isn’t listed, contact support.
CRM
| Source | Auth | Capabilities |
|---|---|---|
| HubSpot | OAuth | Read & write contacts, companies, deals |
| Attio | OAuth | Read & write CRM data |
| Folk CRM | API key | Read & write CRM data |
| Salesforce | OAuth | Read & write CRM data |
Databases and spreadsheets
| Source | Auth | Capabilities |
|---|---|---|
| Google Sheets | OAuth | Read & write rows |
| Airtable | OAuth | Read & write bases |
| Supabase | API key + URL | Read & write tables |
Analytics and product
| Source | Auth | Capabilities |
|---|---|---|
| Google Analytics | OAuth | Read analytics data |
| PostHog | API key | Read event and user data |
| Mixpanel | OAuth | Read analytics data |
| Typeform | OAuth | Read form responses |
| Linear | OAuth | Read & write issues |
| Jira | OAuth | Read & write issues |
Payments and data warehouses
| Source | Auth | Capabilities |
|---|---|---|
| Stripe | API key | Read customers, invoices, subscriptions |
| Databricks | Client credentials | Read catalogs, schemas, tables, run SQL |
Build from data vs. describe your app
Not sure which path to choose? Here’s how they compare:| Describe your app | Build from data | |
|---|---|---|
| Best when | Starting from scratch with an idea | You have existing data in external systems |
| Data models | Created from your description | Derived from your existing schema |
| Configuration | Write a prompt | Interactive Q&A with AI |
| Agent pipeline | Planner → Build → Validation | DataAnalyzer → Build → Validation |
| Data | Starts empty, you add data later | Pre-populated from connected sources |
You can combine both approaches. Connect data sources first, then describe additional features you want on top of them in the prompt.