Akkio

No-code predictive analytics and AutoML for marketing and operations teams

AI Data AnalysisOverseasβ˜…β˜…β˜…β˜†β˜† 3.0

What is Akkio?

Akkio is a no-code platform for building predictive models from tabular data. You upload a spreadsheet or connect a data source, choose a column to predict, and the service trains and ranks several model types automatically, then exposes predictions through a dashboard, a shareable app or an API. It also generates natural-language summaries of datasets and forecasts, which is aimed at people who need an answer rather than a notebook. Marketing agencies and operations teams use it for lead scoring, churn risk and campaign forecasting. The trade-off is control: you gain speed and accessibility, and give up the ability to tune architectures in detail.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Automatic model training and ranking across several algorithm types
  • Predictions delivered through dashboards, apps or a REST API
  • Natural-language summaries and chat-style questions over your data
  • Data preparation with joins, filters and column transformations
  • Forecasting for time series such as pipeline and demand planning
  • Shareable prediction apps for colleagues without a licence

Pros & cons

Strengths

  • Gets a usable model from a spreadsheet without code
  • Outputs are consumable by non-technical colleagues immediately
  • Good fit for lead scoring, churn and forecasting workflows

Watch out for

  • No access to custom architectures or fine-grained tuning
  • Model quality depends heavily on the data you supply
  • Pricing is quote-based and aimed at business rather than individual users

Best for & use cases

lead scoring, churn prediction, marketing forecasting and no-code automl

If you're comparing similar products, check the alternatives below, or browse all tools in the AI Data Analysis category.

FAQ

Do I need data science experience?

Not to run the platform. You do need to frame the prediction sensibly, choose an appropriate target column and judge whether the reported accuracy is good enough to act on. Those are judgement calls rather than coding tasks.

What kinds of data does it handle?

Mostly structured tabular data - spreadsheets, CRM exports, ad platform reports, databases. Text and image models are outside its focus, and very large datasets may need pre-aggregation before upload.

How does it compare with a full AutoML platform?

It is easier for non-specialists and faster to a first prediction. A full platform offers more control over features, validation strategy and deployment, and it usually requires an engineer to operate.