01
Sourcing
Sourcing for data products
Case study 01 · BFSI
An agentic framework for the full data-product lifecycle.
NatWest · Visa
Client names refer to programmes led or delivered by Soamiq's founder, including in prior roles. They are shared for reference only and do not imply endorsement.
The challenge
In a large bank, every data product passes through a long chain of specialist hand-offs: sourcing, source analysis, modelling, mapping, build, review, testing and, eventually, retirement. Each step needs expert effort, consistent standards and an evidence trail.
What we built
Mayank Soam led delivery of an agentic framework that develops data products directly through to decommissioning: a chain of 36 agents covering eight lifecycle stages.
02
Source analysis
Analysis of candidate sources
03
Data modelling
Data models for the product
04
STTM generation
Source-to-target mappings
05
Code
Generation and review
06
Pipelines
Generation and review
07
Testing
Test case generation, execution and review
08
Decommissioning
Unused assets retired via lineage
Technology
- Agent orchestration
- LangChain · LangGraph · MCPs
- Knowledge and retrieval
- RAG · Knowledge Graph · VectorDB
- Data and connectivity
- Native connectors · Postgres · MongoDB
- Cloud and models
- AWS (Amazon Bedrock, Amazon SageMaker) · Azure · configurable LLMs (Claude, GPT)
What it delivers
One path, end to end
A single agentic flow from data-product sourcing to retirement, in place of disconnected manual steps.
Quality built into every stage
Dedicated review steps for code, pipelines and tests.
Lineage-driven clean-up
Unused assets are identified and decommissioned using lineage.
Cloud- and model-flexible
Runs on AWS or Azure, with the client's choice of LLM.
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