Powered by Conscia's Hybrid AI Orchestration Layer
DeepShift AI is an automated migration and documentation engine designed to radically compress the transition from monolith to MACH.
Instead of months of manual mapping, schema wrangling, and reverse-engineering legacy systems, DeepShift AI uses:
- The MACH Alliance's Open Data Model (ODM)
- LLM-powered schema understanding
- Conscia's Hybrid AI Orchestration Engine
to complete end-to-end migration preparation and documentation in hours instead of 6--9 months.
The result is a clean, validated, fully documented target schema and mapping layer --- ready to feed MACH systems, agentic interfaces, and DX Graph in production.
DeepShift AI replaces the following manual challenges:
Teams end up operating the monolith and MACH in parallel because data isn't reliably mapped or standardized.
Legacy systems rarely have clean schemas. Relationships are implicit. Naming conventions are inconsistent. Historical decisions live only in tribal knowledge.
Without a unifying data model, every MACH implementation becomes a bespoke cleanup effort.
Teams remain tied up doing: - Change management\
- Bug triage\
- Ad-hoc mappings\
- Version reconciliation\
- Missing documentation
Knowledge disappears once contractors roll off.
DeepShift AI is powered by Conscia's Hybrid AI Orchestration Engine, which executes the following flow:
📂 Source Data (JSON, CSV, API)
The process begins by collecting representative samples of the
customer's data from the monolith or legacy system, including:
- product catalogs\
- content records\
- taxonomy structures\
- images and metadata\
- relationship data
DeepShift works even when the source system does not provide a formal schema.
An LLM analyzes the raw samples and infers:
- field names\
- data types\
- nested structures\
- relationships\
- inconsistencies
This produces a Source Schema that becomes the foundation for automated mapping.
The LLM receives contextual inputs:
- Historical Conscia Object Mapper configurations\
- Prompt configuration (instructions, heuristics, transformation rules)\
- Target Schema (ODM or custom)\
- Generated Source Schema
Using these, the LLM produces a complete Object Mapper configuration.
🗺️ Object Mapper Component (JSON Config)
Includes:
- field-level mappings\
- transform rules\
- relationship stitching\
- normalization logic\
- validation rules\
- output shape in ODM format
The config is published directly into the customer's DX Engine environment.
Once deployed, the DX Engine:
- Extracts raw source data\
- Applies LLM-generated transformations\
- Ensures schema alignment\
- Handles sequencing, retries, and logging\
- Validates and normalizes records\
- Prepares data for ingestion into DX Graph
AI stops here; deterministic orchestration takes over.
📊 DX Graph (Open Data Model Collections)
DX Graph provides:
- Normalized, MACH-ready data\
- Indexed collections\
- Federated access for frontends and agents\
- Real-time REST/GraphQL APIs
The frontend (e.g., Vercel) requests:
- ODM-formatted product record\
- content relationships\
- media assets\
- inventory, price, enrichment
All via a single DX Engine orchestration flow.
DX Engine stitches together:
- DX Graph data\
- Storyblok content\
- Other sources
Ensuring:
- no frontend glue code\
- no multiple API calls\
- consistent schema\
- simpler, faster frontends
The orchestrated response flows to DX Graph for:
- storage\
- real-time consumption\
- agentic access
DX Graph becomes the authoritative MACH-ready data layer, featuring:
- normalized schemas\
- validated models\
- unified relationships\
- consistent identifiers\
- fast APIs\
- optional semantic indexing
It also powers LLM agents via MCP Server.
DeepShift autogenerates:
- JSON record of every mapping\
- versioned schema diffs\
- impact analysis\
- cross-system lineage\
- engineering documentation\
- content editor--friendly documentation
Eliminates months of post-migration documentation work.
Licensed based on:
- number of records\
- number of source systems\
- number of target systems
If DX Graph is also the Experience API:
- API call volume contributes to licensing
- Configuration costs included\
- Real-time inference billed by model provider
For details:
📧 sales@conscia.ai
🌐 conscia.ai
Special thanks to partner companies, technologies, Cuesta Partners & Dr. Martens for contributions during the MACH X Hackathon.
👉 Live demo, implementation details, and strategy breakdown available.
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DeepShift AI was developed with support from engineering, product, and MACH Alliance contributors committed to accelerating enterprise migrations using AI, orchestration, and open standards.
