Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Public models lack context for your private libraries, frameworks, and architectural patterns.
Off-the-shelf coding assistants operate on a shallow understanding of public code. They cannot reason about your proprietary logic, internal APIs, or unique development standards. This leads to low-quality suggestions, increased technical debt, and slower developer velocity.
We train language models directly on your private repositories to build an AI that speaks your team's language.
@company/ui-kit), and legacy system patterns.Move from generic autocomplete to a true AI pair programmer. This is a core component of our Domain-Specific Language Model (DSLM) Training service, delivering intelligent tools for Enterprise AI Copilot Customization and secure Confidential Computing for AI Workloads.
Training a language model on your private codebase delivers more than a tool; it creates a strategic asset. Move beyond generic AI coding assistants to achieve measurable improvements in developer velocity, code quality, and architectural consistency.
New engineers become productive in weeks, not months. A custom Code LLM acts as an expert mentor, providing context-aware code examples, explaining internal architectural patterns, and answering questions specific to your codebase, drastically reducing the learning curve for proprietary systems.
Enforce architectural patterns and coding standards automatically. The model learns from your best-reviewed, production-grade code, generating suggestions that adhere to your internal style guides and flagging anti-patterns before they are committed, leading to more maintainable and secure code.
Generate boilerplate, unit tests, and documentation that understands your specific libraries and frameworks. The model can propose complex refactors by understanding cross-repository dependencies, enabling safe, large-scale migrations and modernizations that generic tools cannot handle.
Integrate security best practices directly into the development workflow. A custom model trained on your secure coding guidelines and past vulnerability fixes can suggest remediations, detect insecure patterns in generated code, and act as a first-line defense against common security flaws (e.g., SQLi, XSS).
Prevent critical institutional knowledge from walking out the door. The model codifies the expertise of your senior architects and engineers, making it accessible to the entire team. This creates a resilient, searchable knowledge base that survives team changes and scales with your organization.
Achieve superior accuracy on your unique frameworks, internal SDKs, and legacy systems. Unlike generic models that struggle with proprietary APIs, a custom Code LLM delivers precise function calls, understands deprecated library nuances, and provides relevant documentation links from your internal wiki.
A clear, phased roadmap for developing and deploying a custom coding assistant trained on your private repositories, from initial assessment to full-scale integration.
| Phase & Key Deliverables | Timeline | Core Activities | Outcome |
|---|---|---|---|
Phase 1: Discovery & Codebase Analysis | 1-2 Weeks | Repository audit, architecture review, and hallucination risk assessment for your specific codebase. | Detailed project blueprint and data preparation strategy. |
Phase 2: Secure Data Pipeline & Model Selection | 1-2 Weeks | Establish air-gapped data ingestion, implement semantic chunking, and select optimal base model (e.g., CodeLlama, DeepSeek-Coder). | Fully prepared training dataset and finalized model architecture. |
Phase 3: Domain-Specific Training & Fine-Tuning | 2-4 Weeks | Custom pre-training and instruction fine-tuning on your proprietary code, libraries, and patterns. | A specialized model with demonstrably reduced hallucination rates on your code. |
Phase 4: Integration & Pilot Deployment | 1-2 Weeks | Deploy as a secure API or VS Code extension; conduct pilot testing with a developer team. | A functional coding assistant integrated into your development environment. |
Phase 5: Performance Benchmarking & Optimization | Ongoing | Rigorous evaluation against custom metrics (e.g., code acceptance rate, time-to-resolution). | Quantified performance report and optimization roadmap. |
Total Project Duration (Typical) | 4-8 Weeks | End-to-end development from kickoff to pilot-ready assistant. | A production-ready, intelligent coding copilot tailored to your stack. |
Ongoing Support & MLOps | Post-Launch | Optional SLA for model retraining, performance monitoring, and security updates. | Guaranteed model accuracy and compliance over time. |
A custom language model trained on your private repositories delivers transformative efficiency and accuracy for teams building, maintaining, and scaling complex software. Here are the primary beneficiaries.
Accelerate development velocity and reduce context-switching for large teams working across monolithic codebases or microservices. Our models understand your unique architectural patterns, internal libraries, and coding standards, providing relevant, compliant code suggestions.
Learn more about our approach to Enterprise AI Copilot Customization.
Build intelligent, context-aware features directly into your product. Train a model on your API documentation, SDKs, and customer support logs to power next-generation developer tools, in-app coding assistants, or automated support agents that speak your product's language.
Ensure compliance and security while automating code review for trading algorithms, risk models, and regulatory reporting systems. Our confidential training pipelines and models trained on proprietary financial logic reduce errors and audit friction.
Explore our secure development practices in Confidential Computing for AI Workloads.
Bridge knowledge gaps and mitigate risk when migrating or maintaining outdated systems (COBOL, mainframe). A model trained on legacy code and documentation acts as an expert assistant, helping engineers understand and refactor complex, poorly documented logic.
Automate infrastructure-as-code (IaC) generation, CI/CD pipeline troubleshooting, and cloud cost optimization scripts. Models understand your Terraform, Kubernetes, and internal tooling patterns to generate reliable, secure automation.
Protect your core algorithmic advantage while accelerating development. Train a model exclusively on your code to create a competitive moat—your AI assistant understands nuances generic tools miss, without exposing sensitive logic to third-party APIs.
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 answers about training AI on your private code to build intelligent coding assistants.
Our engagement follows a structured 4-phase methodology: 1) Discovery & Data Assessment (1 week) where we analyze your codebase architecture and define success metrics. 2) Secure Data Pipeline Setup (1-2 weeks) where we establish encrypted data ingestion from your repositories (GitHub, GitLab, Bitbucket). 3) Model Training & Tuning (2-3 weeks) where we train a model (like CodeLlama or a custom SLM) on your code, implementing techniques like LoRA for efficiency. 4) Integration & Deployment (1-2 weeks) where we deploy the model via API or integrate it into your IDE (VS Code, JetBrains). We deliver a final model, integration code, and performance benchmarks. Learn more about our Domain-Specific Language Model (DSLM) Training approach.
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.
