COMMERCIAL EVOLUTION
Your Architecture & Standards, Coded into AI
We build private, fine-tuned developer platforms that move your security rules and coding patterns from "static documentation" into the "model weights" of a secure, internal coding assistant.
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THE ENTERPRISE CHALLENGE
Generic Copilots Create Technical Debt
Off-the-shelf coding assistants (like standard GitHub Copilot) are trained on the public internet. They know generic Python and Java syntax perfectly, but they are oblivious to your internal reality.
For regulated enterprises, this creates critical friction:
01
Architectural Drift
Generic models suggest libraries you don't use or patterns that violate your security governance.
02
Fragmented Knowledge
Your "source of truth" isn't just in code—it is scattered across SharePoint, Confluence, Enterprise Architecture (EA) PDFs, and legacy commit messages.
03
The Context Trap
Developers waste hours writing massive prompts to explain internal rules, or worse—they spend hours refactoring AI-generated code to meet compliance.
04
Data Sovereignty
Regulated industries cannot risk sending proprietary code to public model APIs.
The Problem
From Documentation Chaos to Principal-Level Precision
An engineering organization (with more than a few hundred developers) struggles with consistency. Junior engineers wasted hours searching for internal documentation, and "generic" AI tools are rejected by the CISO due to data leakage risks. They needed an AI that didn't just know how to code, but how to code like a Principal Architect at their firm.
THE SOLUTION
The "Private Brain" for Enterprise Engineering
A Hybrid Agentic Platform that combines RAG (Retrieval-Augmented Generation) with Model Fine-Tuning.
02
Unified RAG Layer
A vectorization pipeline that ingests non-code context (SharePoint architecture docs, EA PDFs, GitHub repositories) to provide the model with "Active Facts" about the environment.
01
The "Private Brain" (Fine-Tuning)
Instead of just prompting a generic model, we fine-tuned an enterprise-grade open model (e.g., Qwen) on the client's high-quality internal codebases. This moved "Rules" from the prompt into the model's actual weights. The AI instinctively follows the client's naming conventions and security patterns.
03
Custom VSCode Extension
A bespoke IDE extension delivers this intelligence directly in the developer's workflow, without data ever leaving the client's secure tenant.
04
Stateless Polish
A hybrid architecture where a secure, stateless reasoning model is used only for final syntax cleanup, ensuring no proprietary context is retained externally.
THE OUTCOMES
The Results
→
30–50%
VELOCITY INCREASE
Developers generate compliant boilerplate and functional code significantly faster.
→
Autonomy
AUTOMATED GOVERNANCE
The model suggests approved libraries and patterns by default, reducing code review cycles.
→
New Rates
HIGHER ACCEPTANCE
Developers accepted ~32% more AI suggestions compared to generic tools because the code was already architecturally compliant.
→
Standards
ZERO TRUST SECURITY
No raw documents leave the tenant; embeddings and inference occur entirely within the client's controlled infrastructure.
THE SOLUTION
The "Private Brain" for Enterprise Engineering
A Hybrid Agentic Platform that combines RAG (Retrieval-Augmented Generation) with Model Fine-Tuning.