


By Sagar Shankaran, Founder of CallSphere
Data migration is often the longest pole in any data-platform program. The heavy lifting — translating a mapping document into reliable transformation logic — is slow, repetitive, and unforgiving of small errors.
So we set out to answer a focused question: can generative AI take a mapping document and produce complete, reliable dbt models — under strict governance, with a human expert in the loop?
The short answer is yes. Below are two use cases we validated, the four-step flow that keeps the output consistent, and the controls that keep it trustworthy.
flowchart LR
A["Mapping document"] --> B["Structured prompt"]
E["Existing SQL (incremental)"] --> B
B --> C["Generative AI"]
C --> D["Draft dbt model / SQL"]
D --> V{"SME validation & tuning"}
V -->|"Needs changes"| B
V -->|"Approved"| S["Verified model shipped"]
AI-enabled data migration is the use of generative AI to produce and update the transformation code — here, dbt (data build tool) models and SQL — that moves and reshapes data from source to target. Existing mapping specifications are the input, and a subject-matter expert stays in the validation loop. The goal isn't to remove engineers; it's to compress the most repetitive part of migration while keeping human judgment where it matters.
Both use cases run through the same four steps:
The validation step is the trust anchor. Structured prompting and good inputs get you a strong draft; the SME makes it production-ready.
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Here the AI builds complete dbt models from scratch, using the mapping document as the single source of truth. We used it to generate migration scripts for areas such as Additional Info and Contracts. The input is a mapping document; the output is generated SQL, reviewed before use.
The second use case handles change. We feed the existing SQL plus the mapping into the same flow, and the AI returns an updated model that adds new fields and updates — without a full rebuild. A representative example: adding Supplier Performance Reports to an existing model.
Three lessons stood out:
Because this involves enterprise data, the guardrails came first, not last:
In practice that means anonymising all terminology to prevent data leakage, keeping data strictly within approved cloud environments, and applying a security-first approach to every code-generation task.
What is AI-enabled data migration?
It's the use of generative AI to produce and update the transformation code — dbt models and SQL — that moves data from source to target, using existing mapping documents as input while keeping a subject-matter expert in the validation loop.
Can AI generate dbt models from scratch?
Yes. In our initial-load use case, the AI generates complete dbt models directly from a mapping document, which an SME then validates and tunes before use.
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How does AI handle changes to existing models?
Our incremental use case feeds the existing SQL plus the mapping into the same flow, and the AI returns an updated model that adds new fields or changes — without rebuilding from scratch.
Is AI-generated migration SQL reliable?
Reliability comes from three things: structured prompting, precise mapping documents, and mandatory human SME review. The AI accelerates the draft; the SME verifies and tunes every model before it ships.
How is data privacy protected when using AI?
All terminology is masked so no sensitive data is exposed, work stays strictly within approved cloud environments, and every code-generation task follows a security-first approach under the official company AI policy.
Does this replace data engineers?
No. The flow is human-in-the-loop by design. SMEs remain essential for validating logic, tuning models, and ensuring the output meets requirements.
We're continuing to refine this flow. If you're running a migration program and weighing where AI fits — and where humans must stay in the loop — we'd value your questions and feedback. Learn more at Circini Limited.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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