Your COBOL, mainframe logs, and proprietary documentation contain irreplaceable business rules. Generic AI fails here. We train custom language models on your specific legacy formats, creating an intelligent bridge to modern applications.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Unlock the business logic trapped in your legacy systems by training AI to understand their unique languages.
Your COBOL, mainframe logs, and proprietary documentation contain irreplaceable business rules. Generic AI fails here. We train custom language models on your specific legacy formats, creating an intelligent bridge to modern applications.
Reduce the time to query legacy data by 80% and enable natural language interfaces for systems built decades ago.
Llama 3 or Mistral on your JCL scripts, AS/400 outputs, and custom data schemas.This service is part of our broader Domain-Specific Language Model (DSLM) Training pillar, which also includes Proprietary Codebase Language Modeling. For securing the entire deployment pipeline, explore our Confidential Computing for AI Workloads services.
Our specialized training bridges decades-old systems and modern AI, unlocking trapped operational data and automating manual processes. The result is measurable efficiency gains, cost reduction, and new revenue streams from legacy assets.
Deploy AI agents that understand legacy command syntax, JCL, and green-screen outputs to automate batch jobs, data queries, and system monitoring without costly re-platforming.
Transform decades of scanned manuals, schematics, and proprietary format reports into a queryable knowledge base using models trained on your specific documentation lexicon.
Create a secure, real-time API facade over legacy databases and systems, enabling modern applications to safely interact with core business logic without direct integration risk.
Apply AI to historical system logs and telemetry to predict hardware failures in legacy infrastructure and identify anomalous patterns indicating security or performance issues.
Continuously monitor legacy system outputs and user interactions against regulatory frameworks (SOX, GDPR), automatically generating audit trails and compliance reports.
Build AI-powered copilots that guide new engineers through complex legacy workflows, capturing and operationalizing institutional knowledge before expert retirement.
A clear breakdown of our phased approach to integrating AI with your legacy systems, from initial analysis to full-scale deployment and ongoing support.
| Phase & Key Deliverables | Discovery & Analysis (Weeks 1-2) | Prototype & Integration (Weeks 3-6) | Deployment & Scaling (Weeks 7-10) | Ongoing Support & Evolution |
|---|---|---|---|---|
Legacy System Documentation Analysis & Corpus Creation | Quarterly Reviews | |||
Custom DSLM Training on Legacy Formats & Outputs | Continuous Learning Pipeline | |||
Secure API Bridge to Legacy Databases/Interfaces | 99.9% Uptime SLA | |||
Pilot AI Interface (Chat, Copilot, or API Endpoint) | Performance Monitoring | |||
Full-Scale Deployment & User Training | Dedicated Support Engineer | |||
Hallucination Rate Benchmark & Reduction Report | Ongoing Optimization | |||
Security & Compliance Audit (Data Flow, Access) | Vulnerability Assessments | |||
Total Project Timeline | 2 weeks | 6 weeks | 10 weeks | Ongoing |
Typical Investment Range | $15K - $25K | $40K - $70K | $30K - $50K | Custom SLA |
Our specialized language models are trained to understand the unique languages of your legacy systems—from COBOL mainframe outputs to proprietary documentation formats. This bridges decades-old infrastructure with modern AI, unlocking trapped operational data and automating complex workflows without costly system replacement.
Train AI to interpret JCL, CICS transactions, and COBOL program outputs. Automate green screen interactions, generate modern API wrappers for legacy logic, and create conversational interfaces for system operators, reducing reliance on scarce specialist knowledge.
Develop AI copilots that understand proprietary SAP R/3, Oracle E-Business Suite, or custom AS/400 data schemas. Enable natural language querying of complex tables, automate data migration scripts, and generate real-time reports from siloed systems.
Transform decades of unstructured manuals, engineering drawings (PDFs, TIFFs), and change logs into a searchable knowledge base. Our models extract procedures, parts lists, and troubleshooting guides, powering AI assistants for field technicians and support teams.
Integrate AI with legacy SCADA, PLCs, and MES systems. Train models to parse proprietary log formats and alarm codes, predict equipment failures from historical telemetry, and generate plain-English summaries of complex production line status.
Bridge legacy core banking systems (like Tandem, IBM z/OS) with modern fintech APIs. Train AI to understand transaction codes, batch processing reports, and compliance logs for automated reconciliation, audit trail generation, and real-time fraud monitoring.
Enable AI to interface with older HL7 v2, MUMPS, and proprietary EMR systems. Automate patient data abstraction for clinical trials, translate legacy codes to FHIR standards, and create ambient documentation assistants that work alongside existing systems.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear, specific answers to the most common questions about integrating modern AI with your legacy systems, mainframes, and proprietary data formats.
We follow a structured, four-phase methodology proven across 50+ legacy integration projects:
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
