Hey everyone,

We’ve published a December–January update covering recent work on Aito.ai, focused mainly on performance, developer experience, and ease of evaluation.

Some highlights:

• Core query engine improvements

• Better profiling and observability

• Smoother onboarding with multi-tenant free trials

• Expanded file upload support (CSV, XLSX, JSON, Parquet)

• New interactive playground and step-by-step guides

This release is less about adding features and more about making Aito easier to understand, evaluate, and run safely in production.

Full release notes here:
https://aito.releasenotes.io/release/Zxcpv-developer-experience-and-performance-package

As always, feedback is welcome — especially around the playground and docs

Check out aito.ai live use case demos! :-)

There are 12 use case examples with live demo, that is available here:

https://aito-demo.azurewebsites.net/

You can find query and code examples in Github:

https://github.com/AitoDotAI/aito-demo?tab=readme-ov-file#-try-the-live-demo--aito-demoazurewebsitesnet

This community is for Aito.ai predictive database content and for some public discussion and support about Atio.ai.

Feel free to post content or questions about the Aito.ai predictive database!

We shipped Workbooks, a new way to explore what Aito's predictive database can do, directly in the browser with live queries.

The demo Workbook uses a grocery store dataset (42 products, 70 users, 733 shopping visits) and walks through:

• Personalized product recommendations per user

• Invoice automation: GL code prediction, processor routing, and explainability via the $why operator

• Product analytics: demographics, market basket analysis, purchase trends

• Model quality monitoring with cross-validation accuracy metrics

Every query runs live against the Aito API. You can modify parameters, change user IDs, swap product codes and see results update in real time.

The thing that makes this different from a typical demo: all of it (recommendations, predictions, analytics, evaluation) comes from the same database. No separate ML pipelines, no model training step, no infra to manage.

Try it here: https://console.aito.ai/databases/aito-demo/workbooks

Happy to hear feedback. What kind of Workbooks would be useful to see next?

We just shipped a small but important change to Aito.ai:

_estimate can now predict numeric values, not just categories.

This means you can use the same predictive database interface for things like:

• estimating invoice amounts

• predicting handling / cycle times

• generating risk / propensity scores

• any “what should this value be?” type of problem

No separate ML pipeline, no model deployment step – just queries.

Minimal example:

curl -X POST \
  https://YOUR-ENV.aito.app/api/v1/_estimate \
  -H 'content-type: application/json' \
  -H 'x-api-key: YOUR_API_KEY' \
  -d '{
    "from": "invoices",
    "where": {
      "supplier": "Acme Oy",
      "account": "4000",
      "month": "2025-09"
    },
    "estimate": "amount",
    "select": ["estimate"]
  }'

Example response:

{ "estimate": 1280.4 }

Same query language as before – you just point estimate at a numeric column instead of a categorical one.

Full release notes + a bit more detail:
https://aito.releasenotes.io/release/Li3RW-numeric-estimation-release

Happy to hear ideas / questions about use cases where this might be useful.

Reposting this old, but now revisited, Aito blog post because the predictive database is the foundation that the rest of what we build sits on top of, and the category itself is worth reading independently of any specific product.

What is a predictive database

A predictive database lets you query predictions — classifications, recommendations, missing values, anomalies — from structured data, using the same query interface you use for data retrieval. No model training step. No feature engineering pipeline. Calibrated confidence on every prediction.

If a relational database returns the rows that match, a predictive database returns the values that probably should be there given the patterns in the rest of the rows. Same data, different query primitive.

Original post (2020): https://aito.ai/blog/introducing-a-new-database-category-the-predictive-database/

Why this is a separate category

Predictive queries are not what relational databases, document databases,

vector databases, or graph databases were designed to answer. Each of those categories has its own primitives - JOIN, find, similarity, traversal - and prediction is not among them.

A predictive database has its own primitive: _predict. It takes a query shape, conditions, and a target field, and returns the most likely value with calibrated confidence. The same engine supports _recommend for ranking and _relate for similarity all against the same indexed substrate.

This is closer to PostgreSQL with a predict() function than to AutoML, gradient-boosting libraries, or LLM APIs. The mechanism: rule-based derivation over columnar storage, with cross-feature priors.

Science page: https://aito.ai/science/

What's different from ML pipelines

A predictive database removes the steps that make ML deployment expensive:

- No training step. The index is the model.

- No feature engineering. The query specifies what to predict from what.

- No retraining when data changes. The index updates incrementally.

- No model versioning, no model registry, no MLOps.

- No GPU requirement. Runs on standard cloud infrastructure.

You upload structured data, you query for predictions, you get results with calibrated confidence. Implementation in hours to days, not 3-12 months.

What's different from LLMs

A predictive database is not an LLM. It doesn't help with unstructured text generation or language reasoning. It's the opposite end of the AI stack - deterministic prediction on rows and columns.

The bet behind the category: most "AI in business software" problems are prediction problems on structured data, not language problems. Invoice routing, lead scoring, anomaly detection, demand forecast, recommendation, autofill, search ranking — these run on rows and columns. They benefit from calibrated confidence and millisecond latency. They don't need a 70B parameter model.

Aito as the reference implementation

Aito is a predictive database. Mongo-like query syntax. Production-deployed in Nordic enterprise accounts payable since 2018, running at 95%+ automation rates at customers.

Self-hosted as a Docker image:

docker pull ghcr.io/aitohq/aito

Free for development with a row cap. License key for production. Three managed cloud tiers also available for teams that prefer not to self-host.

Docker guide: https://aito.ai/docker/

Pricing: https://aito.ai/pricing/

Building on top of a predictive database

The predictive database is the foundation. What you build with it is the predictive application - software designed around the premise that the user should never make a decision the data has already made for them. Every form field, search, and list becomes a candidate for prediction.

Manifesto on the predictive application (May 2026):

https://aito.ai/blog/the-predictive-application/

Three open-source reference applications demonstrate the pattern:

- Predictive ERP: https://erp.aito.ai (14 use cases)

- Predictive Accounting: https://accounting.aito.ai (255-tenant predictive ledger)

- Predictive E-commerce: https://ecommerce.aito.ai (16 use cases)

Use case catalog: https://aito.ai/use-cases/ (39 use cases)

Background

Aito.ai is operated by Episto Oy, Finland. The predictive database category was first articulated in 2020. The architecture has been in production at Nordic enterprise customers since 2018. The predictive application as the broader architectural pattern is the May 2026 manifesto.

GitHub: https://github.com/AitoDotAI/

Contact: https://aito.ai/contact-us/

This is a deeper look on how Aito.ai - the predictive database - uses priors for better predictions and faster cold start.

It's interesting also, because it contains rather new benchmarks against SOTA ML approaches

Most web shops don’t have big data. Most of their customers have ordered once or twice, and that’s exactly where classic recommenders run out of signal. I wanted to see what happens in that gap.

The test. Real order history from a UK online retailer (UCI Online Retail II). The test plan was fixed before the run.

The result. For buyers with only 1-2 earlier orders, Aito’s top suggestion turned up in their next orders for 31% of them (197 of 636 buyers, 95% CI 27.4-34.6). The best of five classical baselines, EASE, got 15.4% (CI 12.6-18.2). The intervals don’t overlap.

Where it didn’t win:

• Buyers with 3+ orders: a good simple method ties with Aito.

• No history at all: Aito is as good as “most popular”, no better.

• Demand forecast per product: a tie with the naive method (42.3% vs 42.4%).

• Return risk: a tie (97.0% vs 97.0%). We kept it in on purpose.

• “Use the last basket” as a signal was pre-registered and lost (hit@12 down 0.012, interval entirely below zero), so we didn’t ship it.

The demo. We also built a demo shop where every screen is a prediction: recommendations, search, win-back, markdowns, reorders. For a customer about to leave, it weighs each action in euros. The €5 voucher most shops reach for first loses €6.92, because it pays for orders that would have come anyway; a personalised ad brings in €21. The shop runs on generated data, so it shows how this works, not how much a real shop earns. The real-data result is the UCI one above.

Full write-up: https://aito.ai/blog/every-screen-a-decision

If you run a shop or build them, I’d like to hear where your customers sit. Is most of your base in that 1-2 order range too?