The foundation AI fails without.
Most AI projects fail on data, not models. iLeaf builds the pipelines, warehouse and semantic layer underneath — then adds prediction and Insight.AI, so your team can ask questions of your own data in plain language and get answers grounded strictly in it, with forecasting rather than another static dashboard.
Ask Lia about data & analyticsWhat this includes
- Pipelines & ingestion
- Batch and streaming ingestion from the systems you already run, with schema checks and replay when a source misbehaves.
- Warehouse modelling
- Dimensional models and a semantic layer, so two teams asking the same question get the same number.
- Data quality
- Freshness, completeness and distribution tests that fail loudly, because silent bad data is worse than no data.
- Predictive models
- Forecasting for demand, churn and risk, trained on your history and evaluated against held-out reality.
- Insight.AI
- Natural-language querying over your own data — read-only by default, answers grounded strictly in your records.
- Retrieval foundations
- Embeddings, chunking and knowledge graphs built so agents retrieve accurately instead of plausibly.
How we run it
Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.
Start from the decision
We work backwards from the decision the data is meant to inform. Anything that does not change a decision does not get built.
Fix trust first
Reconciliation and quality tests come before new dashboards. Numbers nobody believes are worse than none.
Model for questions, not sources
The warehouse is shaped around how the business asks things, not around the shape of whatever system exported it.
Then add intelligence
Prediction and natural-language access go on top of a foundation that is already trustworthy — never as a substitute for one.
What we build it with
- PostgreSQL
- BigQuery
- Snowflake
- dbt
- Airflow
- Kafka
- Python
- pandas
- scikit-learn
- pgvector
- Neo4j
- Metabase
Questions we get asked
Our data is messy. Do we need to fix it before AI?
Partly, and less than you might fear. Retrieval and agents need the specific slices they touch to be correct, not the whole estate. We scope the data that the first use case depends on, make that trustworthy, and expand from there — rather than pausing for a multi-year cleanup.
How is Insight.AI different from a BI dashboard?
A dashboard answers questions someone anticipated. Insight.AI answers the ones they did not, in plain language, and forecasts rather than only reporting. It is read-only by default and grounded strictly in your data, so it will say it does not know instead of inventing a number.
Will our data be used to train models?
No. Retrieval reads your data at query time; it is never absorbed into model weights, and it is never sent to train public models. For clients with stricter requirements we have run the full stack, including the models, on their own infrastructure.
Let’s talk about data & analytics.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.
Data & Analytics in specific sectors
Healthcare data, made usable
EMR data pipelines, clinical reporting and predictive intake models — the data foundation clinical AI fails without.
Read moreFinancial data you can reconcile
Pipelines, warehousing and predictive risk models for lending and payments — built so two teams get the same number.
Read more