The agentic platform for modern data engineering.
AiDE (Agentic intelligent Data Engineering) replaces manual, code-heavy pipelines with a fleet of specialized AI agents, so your team ships decision-ready data in days, not months, on the Databricks or Snowflake stack you already run.
Native code. Your platform. Zero lock-in.
One conversation. Decision-ready data, end to end.
Ask for what you need in plain English. An Agent Manager reads the intent and directs a team of specialists, including discovery, ingestion, transformation, quality, reconciliation and modeling agents, to turn raw sources into decision-ready data, with your team in the loop only where judgment matters.
Know your data before you touch it
Agents scan every source, build the catalog, and profile each table, covering cardinality, nulls, keys and relationships, before a line of code gets written.
Decision-ready data, not just pipelines
Ingestion, transformation and modeling agents generate native, deployable pipelines and gold-layer models, turning raw sources into decision-ready data products that are reusable from day one.
Trust that’s built in, not bolted on
Quality checks, reconciliation and observability run continuously in the background, with a full audit trail behind every action an agent takes.
From ask to answer, in one continuous flow.
Prompt the interface
“Run data quality on the Study table,” typed in plain language. No ticket required.
Intent gets classified
The Agent Manager reads the request and routes it to the right specialist agent.
Specialist agents run
Agents work from shared context, handing off to each other automatically.
Quality checks itself
Reconciliation and DQ checks run without being asked, and flag what needs a look.
Answers land back with you
Reports, dashboards, job status and documentation, all in the same conversation.
Twelve specialists. Zero repetitive work.
Every stage of the data lifecycle has a trained agent behind it, and each one produces its own artifacts and can trigger the next agent in the chain.
Data Ingestion
Builds PySpark / PyIceberg pipelines and lands data in the bronze layer with audit tables attached.
Data Profiling
Profiles every column for cardinality, uniqueness and duplicates, and exports the full report.
Transformation
Self-healing pipelines that dedupe, merge and upsert, catching and correcting errors as they happen.
Gold Layer Load
Builds fact and dimension workloads plus the DDLs and stored procedures behind them.
Data Quality
Monitors for anomalies, runs compliance checks, and reports on data health in plain language.
Reconciliation
Compares source and target on schema, values and row count, then can build the fix job itself.
NLP → SQL
Turns a plain English question into SQL, fetches the answer, and keeps the conversation going.
Reporting
An assistant that reads the data catalog and audit logs to answer “what happened and when.”
Metadata & Discovery
Scans your source systems, builds the catalog, and keeps metadata current automatically.
Data Observability
Operational dashboards you can query in plain English, with real-time alerts built in.
Resource & Scalability
Predicts workload patterns to auto-scale compute and keep cloud spend in check.
Data Modeling
Maps relationships across tables and recommends a conceptual model to speed up design.
Built for teams where trust in data isn’t optional.
See what AiDE means for your industry, or for the seat you sit in.
Risk & regulatory reporting
Auditable, lineage-rich pipelines for credit risk, capital and ESG reporting, with faster delivery on customer 360 and next-best-offer models.
Clinical trial acceleration
Pull multi-site patient data into a single source of truth fast, with the DQ, lineage and PII controls regulators expect.
Patient & member 360
Unify EMR, claims and CRM data for population health and outcomes reporting, with masking and role-based access built in.
Lakehouse modernization
Move off brittle, hand-coded pipelines onto a governed, reusable framework built for high-volume analytics and ML.
Prove the ROI of your data investment
Faster delivery and lower cost of ownership, with governance metrics your board will actually understand.
Give your team leverage
Agents absorb the repetitive 80% of the work, so your engineers spend their time on architecture and hard problems.
Standards, kept
AiDE builds to your medallion architecture, your data model and your naming conventions, not a black box’s.
Governance before the fact, not after
PII masking, encryption and full audit trails run automatically, before data ever lands in a table.
Native code. Your platform. No lock-in.
AiDE generates native, optimized code for Databricks and Snowflake. You keep the code, the lineage and the control, and every stage runs under one continuous governance layer.
Sources
Ingest & Integrate
Process & Transform
Unified Storage
Outcomes: 40–60% faster delivery · fewer resources, less rework · a production-grade, AI-ready data platform
Everything a data engineering team needs, out of the box.
01Faster ingestion, less build effort
- Agent-driven pipeline development
- Configuration-driven, reusable artifacts
- Managed orchestration and workflow
- NLP-to-SQL querying, agentic report building
02Unified storage, faster transformation
- Bronze-to-silver transformation agents
- Gold-layer fact and dimension builds on your model
- Stored procedures and DDLs built and run automatically
- A shorter path from raw data to analytics
03Smarter governance
- Built-in DQ agents recommend rules and run reports
- Agentic reconciliation validates source vs. target
- Data lineage and a live metadata repository
- Built-in workflow orchestration
04Lower total cost of ownership
- Reusable artifacts cut build and maintenance cost
- Faster insights shorten time to market
- Chat agents and NL querying lift data literacy
- Native code, nothing proprietary to pay for twice
The honest answers, upfront.
No. AiDE generates native SQL, Python and dbt-ready artifacts for Databricks and Snowflake. You keep the code, and it runs with or without AiDE.
Yes. Reproducibility is enforced through prompt versioning and deterministic execution paths, so the same prompt and metadata produce the same output every time.
PII fields are automatically masked or encrypted before they’re written to the platform, and every action is captured in a full audit trail.
No, it augments them. AiDE generates dbt-ready models and tests and hands off execution to your existing orchestration where you want it to.
Your team is. AiDE proposes and executes within defined guardrails, and a human-in-the-loop step is built in wherever judgment matters.
See AiDE work on your data.
Bring a real source system and a real question. We’ll show you the agents building the pipeline live.