5 min
Plain English in. Working analysis out. No engineer required.
Research what's out there. Build what isn't.
5 min
Plain English in. Working analysis out. No engineer required.
Any data
Your CRM, public datasets, open APIs. The agent works on whatever fits the question.
Cited
Every answer comes with the data it learned from. No hallucinations.
24/7
Runs on every new event. Schedule it, share it, keep it.
Dashboards count. Chatbots guess. Most companies have data and questions, with nothing in between actually doing the work.
When will MRR drop?
unanswered 23% risk · 30dWhich leads convert?
unanswered 12 above 80% scoreWhat predicts churn?
unanswered 4 features · 91% AUCDashboards count things. Spreadsheets summarize them. The questions worth asking — patterns, predictions, classifications — never actually get run on your data.
Will Sarah K. churn this month?
I don't have access to your customer data — I can only suggest general churn-prevention strategies.
no answer · generic87% likely · within 14 days. Based on declining sessions, 3 unresolved tickets, and a missed renewal email.
predicted · citedGeneric LLMs are trained on the public web. Your business runs on what's private — sales pipeline, support tickets, product events. They write paragraphs; they can't tell you which customer cancels next.
Support tickets, product events, sales calls, internal docs. The signal is in there. Without an agent that can actually work with it, it's just storage.
Every build comes out the same shape: a model trained on your data, the interface your team uses, and the agent that keeps them both running. Here's what that looks like, for one of the most common ones — catching churn before it cancels.
Trained on your last 18 months of churn. Returns a risk score per account, daily. Re-trains weekly as new outcomes land. Drift detected before it bites.
A panel your CS team opens daily. Click a row, see the reasons. Shareable URL, no Signal account, no training needed.
Wakes up every morning, scores every account, pings Slack on threshold crossings, re-trains on the schedule. The agent stays on the question after you've stopped asking.
Plug in your tools. Signal trains a neural network on your data — with one head for classification and one for prediction — and ships live analytics that update with every new event.
Hover any source or output to trace its path.
Most software stops at the search bar.
We think the interesting work starts there.
The questions worth asking don't have a Wikipedia page. They span domains. They require sifting open data, dirty data, and live data all at once. They end with something built — a spreadsheet, an interface, a quiet alert at 6 AM when the world has changed.
Signal exists for that work. It listens carefully, pulls from anywhere, cites everything, and assembles whatever instrument the question demands. Then it leaves the instrument behind, so you can use it again.
Notebooks show the work. AutoML ships a model. Signal ships the instrument.
Tune in.
Builds compile into reusable Machines you can run, share, or schedule.
Every research artifact ships with click-to-source citations and a confidence indicator.
Charts, photos, screenshots, and PDFs are first-class inputs for research runs.
Connect Google Drive — research and builds can read your documents directly.
No. Signal trains specialized ML models on your actual data — classifiers, predictors, forecasters. GPT writes paragraphs. Signal builds prediction systems that run every day on your business.
Your data stays in your tenant. Models train and run in isolated sandboxes you control. We don't train shared foundation models on your data, and you can wipe everything in one click.
No. Describe what you want in plain English. Signal handles the schema, the joins, the cleaning, the training, and the deployment.
Most models train in under 5 minutes. Forecast and anomaly models can run on a schedule and stay continuously updated as new data arrives.
Postgres, Snowflake, BigQuery, Stripe, HubSpot, Salesforce, Mixpanel, Notion, Slack, Zendesk, Shopify, and 30+ more. CSV and Excel uploads work too. Custom APIs in 10 minutes.
Cleaning is part of training. Signal handles missing values, schema drift, duplicate keys, and weird edge cases as it goes — and tells you what it cleaned so nothing is silent.
Free during beta. No credit card. Production pricing rolls out at 1.0 — early-access pricing locks in for beta users.
Early alpha · Apply for access
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