Migrate your data warehouse in months, not years — with AI agents under human control.
We use AI agents to analyze, document, and migrate legacy data warehouses. In live production engagements, not proofs of concept.
Why DWH migrations stall
Most enterprises know their data warehouse needs to move. The reason the project keeps getting postponed is almost always one of three things.
Decades of accumulated complexity
Dozens of data marts. Thousands of tables. Business logic scattered across Informatica, DataStage, SSIS, SAS, COBOL, and hand-written SQL, evolved over 20+ years. The systems still work — but no one person understands them end to end.
The knowledge has walked out the door
The developers, architects, and business analysts who built the system are long gone. Documentation is outdated, partial, or missing. Every change is a calculated risk.
Manual migration is too slow to matter
Traditional analyze-and-rewrite programs run for years. Budgets overrun, key people leave mid-project, and the business keeps adding new requirements to the legacy system while you're trying to replace it.
Our approach: agentic engineering for legacy DWH
This is not Copilot with a consulting wrapper. Our framework is a set of specialized AI agents that cover the full data-engineering lifecycle — analysis, architecture, code generation, diff-verification, and testing — with your engineers in control at every gate.
1. Analysis and documentation
Agents walk the existing codebase module by module, layer by layer, and produce a complete as-is specification: data models, lineage, dependencies, and business logic in plain language. This works even for systems where the original knowledge is lost. The overall architecture is loaded into the agent context so it reasons about the system holistically, not file by file.
2. Architecture and mapping
An Architecture Agent produces the mapping from the legacy DWH to your target architecture — typically a cloud lakehouse on Databricks, Snowflake, or Microsoft Fabric. It identifies overlap, gaps, and risk areas between as-is and to-be, and outputs a documented Solution Design ready for migration sign-off.
3. Code generation and diff verification
Coding Agents generate the target code — for example, Python notebooks for Databricks or SQL for the target engine — based on the mapping and your coding standards. Every generated module is then diffed against the original for structural and logical equivalence. Deviations are logged in a bug log, triaged, and iteratively corrected until the code passes structural equivalence. The original can be anything from an ETL tool's XML export to COBOL or PL/SQL source.
4. Testing and quality assurance
Once the code is structurally sound, Test Agents run it end-to-end, compare outputs against the legacy system on representative data, and produce test reports. Bug fixing is orchestrated by the agent and supervised by your engineers. The final gate is an end-to-end reconciliation: Old vs. New, row-level where it matters.
Humans in control
At every step, your architects and lead engineers review and sign off. AI delivers speed and consistency; functional correctness and architectural judgment stay with humans.
Selected projects
Synapse → Databricks in the Insurance Industry
A data mart on Microsoft Synapse needed to move to Databricks under a tight commercial deadline. Our agents reverse-engineered the Synapse JSON pipelines, generated Python notebooks for Databricks, and verified the migration with automated diff and end-to-end reconciliation.
Informatica PowerCenter → PL/SQL — coaching & enablement
The client's own engineering team needed to run the migration in-house. We coached them 1:1 through PowerCenter XML analysis, agent-assisted migration design, and code generation.
COBOL & SAS modernization — time-critical migration
A time-critical migration of COBOL and SAS programs. Agents analyzed the existing estate, produced the as-is lineage, and accelerated the rewrite — from coaching through to automated implementation.
How we handle your code and data
This is the first question your security and compliance team will ask, so we answer it up front.
Our agents run in your Azure, AWS, or GCP tenant, or on-premise. Source code does not leave your environment without your explicit approval. Foundation models are accessed via enterprise endpoints — Azure OpenAI in the EU region, Anthropic via AWS Bedrock, or self-hosted open-weights models — configured to your data-residency and retention requirements. All generated artifacts are your IP from the moment of creation. We operate under Swiss contract law and align with FADP and GDPR.
Why Callista
Three things that distinguish us from a generalist systems integrator with a Copilot subscription.
Purpose-built for legacy DWH
Our agent framework is designed around the shape of data-warehouse work: mappings, slowly-changing dimensions, dimensional models, ETL semantics. It understands what an SCD-Type-2 is, not just what Python looks like.
Diff-based verification as a first-class step
We don't trust AI output on its own. Every generated module is compared to the original — structurally and behaviourally — before it gets to a human review. This is what makes the output safe enough for production.
Enablement, not dependency
Where you want it, we transfer the capability to your team. Our coaching model leaves you with people who can run the next migration themselves, not a permanent retainer.
Productivity in factors, not percentages
AI agents multiply the output of engineers and analysts. The most expensive phases in a migration — analysis, documentation, and testing — are exactly where the factor gains appear. Tasks that historically took weeks complete in hours.
Ready to see it on your systems?
The fastest way to know if this fits your estate is a working session on your actual code. In a half-day workshop, we'll run one of our analysis agents on a representative slice of your legacy DWH and show you what it produces — lineage, documentation, candidate migration plan. No sales pitch. Usable output either way.