An AI-powered, conversational data modeling platform that turns raw data into deployed prediction services — no code required. Business analysts upload a spreadsheet, have a conversation with an AI assistant, and walk away with a validated model running behind a live API and an interactive dashboard.
Machine learning is powerful but gatekept. A business analyst who knows their data better than anyone still can't build a model without a data scientist, a weeks-long backlog, and a deployment pipeline they'll never understand. Existing AutoML tools (DataRobot, H2O, AutoSklearn) solve the algorithm problem but not the people problem — they still assume you know what a "hyperparameter" is.
Meanwhile, the analyst's spreadsheet sits in a shared drive, full of patterns no one has time to find.
Primary: Business analysts who are data-literate but not code-literate. They understand their domain deeply (sales patterns, customer behavior, operational metrics) but hit a wall when it's time to go from "I have a hunch" to "I have proof."
Secondary: Small teams without dedicated data science staff who need quick, reliable predictions — not research papers.
AutoModeler meets users where they are: a chat window.
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Upload — Drag in a CSV (or connect a data source). AutoModeler immediately shows what it sees: row counts, column types, patterns, anomalies, and plain-English summaries ("This looks like monthly sales data with 14 product categories").
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Explore — Ask questions in natural language. "Which products are trending up?" "Are there seasonal patterns?" "What's driving returns?" AutoModeler generates charts, statistics, and explanations — not code.
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Shape — AutoModeler suggests features: "I notice
order_datecould be split into day-of-week and month — seasonal patterns might emerge." The user approves, tweaks, or asks "why?" Everything is explained before it's applied. -
Model — Based on the data and the user's goal ("I want to predict next month's revenue"), AutoModeler recommends and trains appropriate models. Results are shown as comparisons: "Model A is more accurate but Model B is easier to explain to your team." The user picks what matters to them.
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Validate — Before anything goes live, AutoModeler walks through what the model gets right, what it gets wrong, and where it's uncertain. "This model is 92% accurate overall, but struggles with new product categories — here's why."
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Deploy — One click. The model becomes a live API endpoint and an interactive prediction dashboard. The analyst can share a link: "Paste in next month's numbers and see the forecast." No DevOps, no Docker, no YAML.
- Conversation over configuration. Every interaction can happen through chat. Forms and buttons exist as shortcuts, not requirements.
- Explain before executing. Never silently transform data or train a model. Always tell the user what's about to happen and why.
- Progressive disclosure. Start simple. Reveal complexity only when the user asks for it or when it matters for the decision.
- Delightful, not just functional. Smooth transitions, clear visualizations, and moments of surprise ("I found something interesting in your data..."). This should feel like working with a smart colleague, not operating a machine.
A business analyst uploads their quarterly sales data during a lunch break. By the end of lunch, they have:
- A clear understanding of what's driving their numbers
- A model that predicts next quarter's revenue by region
- A live dashboard they can share with their VP
- An API their developer can plug into the company's reporting tool
They didn't write a line of code. They didn't read a single documentation page. They had a conversation.
- Not a notebook replacement. Data scientists who want fine-grained control should use Jupyter. AutoModeler is for the other 90% of the organization.
- Not a BI tool. Tableau and Power BI visualize historical data. AutoModeler predicts the future.
- Not a black box. Every model decision is explainable in plain language. If a user asks "why did you pick this model?", they get a real answer.