4
legacy tracks with provenance and SME accountability mappings
Intercambio AI Labs
Conocimiento legador enterprise-ready para equipos de IA
Empaquetamos, etiquetamos y entregamos artefactos COBOL, RPG y mainframe para que los modelos entrenen con trazabilidad.
Diseñado para equipos con necesidades de gobierno, auditoría y trazabilidad obligatoria.
Proof before rollout
What AI labs verify before operating at scale
Each engagement is prepared for ingestion-grade trust: training-data provenance, documented SME review, and policy-aware handoff rules.
No unlabeled assumptions: every claim is traceable from source artifact to AI operations handoff.
94%
source lineage fields preserved across data and specialist handoffs
48h
from sourcing assessment to dual-track training-ready handoff
0
audit findings from policy exceptions entering release
Offer matrix
Three support lanes for AI lab delivery
Combine governed data, AI specialists, and operating support so model teams can work with legacy-system knowledge without losing control.
Package 1
Paquete de evidencia principal
COBOLpro entrega artefactos y etiquetas con permiso; el cliente define gobierno e integración final del modelo.
Includes
- •Artefactos de COBOL, RPG, copybooks y JCL con índice de origen
- •Etiquetas trazables para interfaces, reglas y dependencias
- •Mapa de diccionarios y flujo de control
- •Verificaciones de calidad antes de la entrega
Output: JSON + Markdown + manifiesto PDF
4-step workflow
From source assessment to governed AI-lab handoff
Assess the source domain, design support lanes, validate evidence, and hand off governed data and specialist support into approved AI workflows.
Step 01
Source assessment
Assess legacy estates, ownership, and policy constraints to identify data, specialist, and operating-support requirements.
Step 02
Track design and readiness
Define train/rl readiness criteria, handoff boundaries, and governance checkpoints for each selected track.
Step 03
Dual-track onboarding
Operationalize SME onboarding and dataset preparation in parallel, then validate annotations, quality gates, and policy controls together.
Step 04
Model and RL handoff
Deliver dual-track packages with reproducibility trails, governance evidence, and model- or RL-ready acceptance checkpoints.
Compliance and controls
Auditability for AI lab support workflows
COBOLpro is built for AI labs and model teams requiring evidence, policy enforcement, and reproducible handoff before training or evaluation.
Control layer
- •Stable provenance IDs from legacy source through labeled exports and model-ready checkpoints
- •SME onboarding, reviewer evidence, and ownership boundaries documented for each deliverable
- •Policy, license, and legal reuse constraints embedded in package metadata
- •Redaction, masking, and annotation policy gates preserved in training and RL outputs
- •Versioned approval logs and immutable release checkpoints at every handoff stage
Final controls
Designed for AI labs and model teams that require auditable compliance across annotation, training, and RL operations.
Pedir paquete de demostración¿Listo para un traspaso de datos con control?
Book a scoped intake to validate data provenance, specialist support, governance, and reproducibility before procurement.