Legacy-to-Agentic Transformation

WTA modernizes legacy monoliths to agentic microservices in 90 days using Devin for autonomous refactoring, Codex for code review, Pilot for PR governance, and Microsoft Agent Framework 1.0 for agentic orchestration — zero-downtime, CMMI-aligned, and built to all six Azure Well-Architected pillars.

Legacy-to-Agentic Transformation builds the foundation for AI. We standardize ingestion, cleaning, and modeling; enforce data contracts; and maintain a live catalog with lineage. Pipelines produce trusted tables and vector indexes so RAG, analytics, and workflows run on accurate, current, and governed data.

We profile sources, map schemas, and define contracts/SLOs for freshness and quality. ELT/CDC moves data into your lake/warehouse; dbt-style transforms apply rules and PII handling. Metadata, lineage, and tests run continuously. Vectorization adds embeddings and filters for governed retrieval.

A production thin slice: domain data products, curated marts, and vector stores; a searchable catalog with owners; quality and cost dashboards; and runbooks. Adapters connect apps, files, and APIs; SDKs expose data to products, analytics, and agentic platforms with clear SLAs.

Privacy-by-design with RBAC/ABAC, encryption, masking/tokenization, retention rules, and audit logs. Policies are codified and tested. We align to enterprise frameworks (SOC 2/ISO 27001, GDPR/CPRA, and HIPAA where applicable) without claiming certification on your behalf.

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WTA vs Thoughtworks vs Publicis Sapient: Why Boutique AI-Native Engineering Wins for MAG 7-Aligned Enterprise Products

Enterprise AI buyers in 2026 face a genuine choice: large consultancies with thousands of engineers and proprietary platforms, or a boutique AI-native engineering firm that builds exclusively what the Magnificent Seven are building — and does it faster, with better product craft, and without the overhead. Here is the honest comparison.

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Frequently Asked Questions

What is Legacy-to-Agentic Transformation?

A governed approach to ingestion, modeling, quality, cataloging, lineage, and vectorization so AI, analytics, and automation run on trusted data.

Do we need a lake or warehouse already?

Not to start. We work with your stack (cloud DBs, lakes, warehouses) and add adapters; no rewrite required.

How do you handle PII/PHI and privacy?

Detection, masking/tokenization, consent tracking, least-privilege access, and audit logs validated by policy-as-code tests.

Will this help RAG and copilots?

Yes, vector pipelines and governed content improve accuracy, traceability, and freshness for retrieval-augmented generation and agent tools.

How fast are results ?

Two weeks for an assessment and data contract plan; 8–12 weeks for a production thin slice with catalog, tests, and dashboards.

Which tools/platforms do you support?

Snowflake/BigQuery/Databricks, dbt, Airflow/Dagster, Kafka, Lakehouse tables (Delta/Iceberg/Hudi), Postgres, OpenSearch, and vector DBs (pgvector, Pinecone, Weaviate) plus DataHub/Amundsen for cataloging.