4
artefaktklassen mit Source-Links
AI-Labs Austausch
Unternehmensreife Legacy-Wissensbasis für KI-Teams
Verpacken, markieren und übergeben Sie COBOL-, RPG- und Mainframe-Artefakte so, dass KI-Trainingssätze auf nachweisbaren Daten beruhen.
Entworfen für Unternehmen mit Governance-, Audit- und klaren Übergabegrenzen.
Proof before rollout
Ergebnisse vor dem Rollout
Jedes Paket ist auf Rückverfolgbarkeit, konsistente Struktur und klare Übergabegrenzen ausgelegt.
Keine Rohannahmen. Nur Beweise und Lückenanalyse.
94%
quellennachverfolgbare Verbindungen im Gesamtumfang
48h
bereits im ersten Paketstand bereit
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
Specialist Support
COBOLpro provides legacy SMEs and runbooks; your lab retains model training decisions and deployment control.
Includes
- •Legacy operations SMEs, compiler experts, and systems specialists from COBOL, AS/400, mainframe, and RPG estates
- •Structured handoff runbook with role boundaries, escalation paths, and annotation responsibilities
- •SME onboarding cadence for labeling workflows, reviewer lanes, and quality gates
- •Annotation and policy alignment for legal, security, and model-governance controls
Output: Annotated SME roster + onboarding runbook + policy alignment memo
Package 2
Training Data Support
Data packages are delivered with immutable manifests, lineage graphs, and readiness checks for model training and RL environments.
Includes
- •Legacy artifacts and labeled bundles from COBOL, AS/400, mainframe, and RPG sources
- •RL-ready and simulator-ready formats with schema-aligned examples
- •Provenance lineage, extraction logs, and reproducibility metadata per bundle
- •License-safe descriptors and reuse rules for model-training reuse
Output: JSONL + Parquet + manifest + policy map
Package 3
AI Operations Support
COBOLpro supports the operating layer around legacy-system knowledge; your lab controls model training, evaluation, and deployment decisions.
Includes
- •Workflow design for intake, labeling, reviewer lanes, QA, escalation, and delivery checkpoints
- •Specialist review loops for ambiguous legacy-system behavior and source-backed interpretation
- •Operational evidence for governance, safety, policy, and procurement review
- •Controlled handoff into model-training, evaluation, RAG, or simulation workflows
Output: Operating runbook + QA checklist + escalation path + handoff evidence
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.
Testpaket anfragenBereit für einen geprüften Datenübergang?
Starten Sie mit einem begrenzten Scope und validieren Sie Governance, Risiken und Liefermodell.