The data foundation every AI ambition depends on.
Pipelines, lakehouses, and streaming infrastructure that turn scattered operational data — sensors, ERPs, video events, spreadsheets — into an asset AI can actually use.
Why this matters now
Every failed AI project has the same autopsy: the data wasn't there, wasn't clean, or wasn't connected. Fixing that after model development starts costs multiples of doing it first.
Service pillars
Ingestion & streaming
Batch and real-time pipelines from OT, IoT, and IT sources — MQTT to ERP — into governed storage.
Lakehouse architecture
Medallion-pattern lakehouses with quality checks, lineage, and cost-aware design.
Serving for AI
Feature stores, vector indexes, and APIs so models and agents consume data without heroics.
What changes for your operation
- One version of the truth — Operational and business data joined, governed, and documented.
- AI-ready by construction — Every dataset lands with the quality contracts models require.
- Costs under control — Storage tiering and query optimization designed in, not bolted on.
Stack we deploy with
- Databricks / Snowflake
- Kafka / MQTT
- dbt
- Airflow / Dagster
- Great Expectations
Where this service earns its keep
- IoT data platforms
- Enterprise lakehouse builds
- Real-time streaming analytics
- Data-quality programs
Where we deploy it
Questions we hear most
It's our home turf — historians, SCADA, and MQTT streams joined with ERP and CRM data under one governance model, respecting OT network boundaries.
Ready to put data engineering to work?
Book a demo, or start with the AI Readiness Assessment — a 30-minute working session that maps your highest-value first deployment.
- AI that sees, predicts, and acts — not just a dashboard.
- Pilot to fleet rollout in weeks, with a go/no-go you can defend.
- Enterprise-grade security, procurement, and support from day one.
Schedule a demo
See Invexal on your own cameras and data.