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DeepShift Migration AI

Migration mapping that ran 6 to 9 months now finishes in hours, with the write-up produced alongside.

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Compresses monolith-to-MACH migration from months to hours with LLM-driven schema mapping.

What the agent does

Infers a legacy schema, generates the field mappings, validates its own output, and writes the migration documentation alongside.

Contributed by
MACH Alliance (Hub operator)
Industry
Apparel & Footwear
Audience
B2C
Category
Integration & OrchestrationData & AnalyticsContent & Experience
01
The problem

Why this exists

The longest, most expensive part of a MACH migration is not standing up the new stack. It is reverse-engineering the old one. A legacy monolith encodes years of accumulated business logic in inconsistent schemas, undocumented relationships, and exception cases nobody can fully explain. The team that knows the old system best is usually the team busy keeping it running.

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In practice the team spends 6 to 9 months mapping legacy fields to new ones, normalizing inconsistencies, writing transformation code, and documenting the result well enough that the future organization can maintain it. That is the work DeepShift compresses.

02
Outcomes

What this demonstrates

  • Schema inference and mapping that used to take 6 to 9 months runs in hours.
  • The target schema lands clean and validated, expressed against the MACH Open Data Model so downstream systems plug in without further translation.
  • Documentation is generated alongside the mapping (field-by-field rationale, lineage, transform rules, impact analysis) instead of being a back-half project nobody has time for.
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  • Migration teams move from manual mapping to review-and-approve. The skilled time gets spent on the edge cases, not the routine 80%.
  • The resulting mapping config is auditable and re-runnable, so a partial migration or a regression can be redone deterministically.
03
How it works

Architecture

DeepShift ingests representative samples of the source data: JSON, CSV, or live API responses from the legacy system. An LLM-driven schema understanding step infers the source's actual structure: field names, types, relationships, and inconsistencies. Output is a candidate schema annotated with confidence and with the edge cases the model flagged for human review.

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A second step generates the mapping rules between that inferred source schema and the target. The target is the MACH Open Data Model by default, but DeepShift accepts a custom target schema for organizations whose model differs. Mapping rules are expressed as Object Mapper config: JSON with field mappings, transform rules, normalization steps, and validation predicates.

Conscia's DX Engine executes the deterministic part. Given the mapping config and a source record, DX Engine extracts, transforms, validates, and normalizes the output. Clean records land in DX Graph; from there they feed into MACH systems: Storyblok for content, the downstream commerce or operations platform for the rest.

The system also auto-generates documentation. For every field, it produces lineage (where the value came from), transform rules in plain language, impact analysis (what depends on this), and engineering and content-editor versions of the same story. The documentation lives next to the data, so the operations team that takes over the new stack inherits the reasoning, not just the result.

DeepShift ingests samples of the source data as JSON, CSV, or live API responses. An LLM-driven step infers the source schema with confidence levels and flags edge cases for human review. A second step generates mapping rules, as Object Mapper config, from that schema to the target, which is the MACH Open Data Model by default or a custom schema. Conscia's DX Engine executes the deterministic extraction, transformation, validation, and normalization, and clean records land in DX Graph, which feeds Storyblok for content and the commerce or operations platform for the rest. The system also generates documentation for every field: lineage, plain-language transform rules, and impact analysis.

DeepShift ingests samples of the source data as JSON, CSV, or live API responses. An LLM-driven step infers the source schema with confidence levels and flags edge cases for human review. A second step generates mapping rules, as Object Mapper config, from that schema to the target, which is the MACH Open Data Model by default or a custom schema. Conscia's DX Engine executes the deterministic extraction, transformation, validation, and normalization, and clean records land in DX Graph, which feeds Storyblok for content and the commerce or operations platform for the rest. The system also generates documentation for every field: lineage, plain-language transform rules, and impact analysis.
Tech stack
Conscia DX EngineConscia DX GraphMACH Open Data Model (ODM)StoryblokVueTypeScriptTailwind CSSClaudeVercel
Builders

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Verification

Public code

The implementation is published in full, so anything on this page can be checked against the repository. Anything the repository does not show is not established here.

Status: Reference prototypeAdded May 26, 2026A listing is an editorial decision, not a MACH Alliance certification.
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