Equitus.ai has standardized their Data Conversion Services (DCS) to solve the "siloed data" problem for both DoD and commercial enterprises. By utilizing the Knowledge Graph Neural Network (KGNN), they transform stagnant, legacy data into an "AI-ready" format without the need for manual ETL (Extract, Transform, Load) processes.
The service is orchestrated by an Equitus Architect Engineer who oversees the transition from fragmented silos to a unified knowledge graph.
The Service Model: Pricing & Structure
Equitus follows a clear-cut pricing model designed to reduce the high entry costs typically associated with enterprise AI.
* Time-Set-Up Fee (Starting at ~$135,000): This covers the initial "Rapid Resolution" deployment. It includes the physical hardware (typically an IBM Power 1022 or Dell XR7620), the pre-installed KGNN/EVS software stack, and the initial integration of up to three primary data sources.
* Service Options: After setup, Equitus offers scalable "Capability as a Service" (CaaS) options. This can include continuous data mining, deep document processing (handling up to 100,000+ pages of PDFs/unstructured data), and training for the client’s internal technical staff.
The Architect Engineer’s Workflow (The Ingestion Process)
The Equitus Architect Engineer serves as the technical lead, ensuring the ingestion is "zero-movement" and secure. Here is how they handle the process:
Phase 1: Semantic Discovery & Audit
Instead of writing custom scripts for every silo, the engineer uses KGNN to auto-discover entities.
* They perform a "Data Audit" to identify all legacy formats (e.g., SQL databases, old CAD files, PDF archives, and local spreadsheets).
* The engineer configures the KGNN Software Connectors to "listen" to these sources.
Phase 2: Intelligent Ingestion (The Handshake)
The engineer leverages the IBM Power 10/11 Matrix Math Accelerator (MMA) to run deep learning models directly on the ingestion stream.
* Metadata Extraction: As data flows in, the engineer supervises the KGNN as it extracts facts, not just files.
* Contextual Linking: The engineer validates the "Semantic Layer"—ensuring the system correctly links a "Part Number" in a 20-year-old CAD file to a "Supply Chain Record" in a modern ERP.
Phase 3: Validation & Secure Hand-off
* On-Prem Security: The engineer ensures all processing happens behind the client's firewall (SIPR/JWICS or Private Enterprise).
* Data Exploitation: Finally, the engineer integrates the now-unified knowledge graph into the client's chosen GUI or application stack (like Onebrief for the DoD or IBM watsonx for commercial clients).
Why this matters for e& or DoD clients:
This approach eliminates the "Research Project" phase of AI. By the time the Equitus Architect Engineer finishes the 30-to-60-day setup, the client has a "living" digital ecosystem that is fully searchable and ready for Agentic AI, rather than just a migrated database.
Would you like me to detail a specific "DCS Statement of Work" (SOW) outline for a legacy system migration project?
