AI ThatActually Ships.To Production.
Theoretical AI is a cost center. We architect and deploy custom RAG agents, Copilot implementations, and enterprise ML models that move from notebook to live environment.
Why AI projects fail in the real world.
Data Security Flaw
The model summarizes the CEO's salary document for an intern. It ignored SharePoint permissions entirely. Public AI tools have no concept of your internal access controls.
Hallucination Risk
Without RAG grounding in your actual data, the model confidently answers questions about company policy — with completely fabricated information. No one notices until damage is done.
Integration Failure
The AI sits in a standalone web app nobody checks. It's not where your teams work. It generates zero business value because it was never embedded into existing workflows.
What's Included
Four pillars of enterprise AI engineering
Custom RAG Agents
Retrieval-Augmented Generation grounded in your proprietary data. Chat interfaces that source from internal documents without exposing data to public models.
M365 Copilot Prep
We audit Entra ID and SharePoint permissions before enabling Copilot, preventing oversharing and securing sensitive data from unauthorized prompts.
Azure AI Studio
Azure AI Search, Azure OpenAI, and Prompt Flow combined into scalable, enterprise-secured AI architectures deployed inside your private cloud.
Adoption & Prompting
We train your teams on effective prompts, build internal use case libraries, and drive the organizational change that makes AI actually stick.
How we ship AI
to production.
Secure the Foundation
Before any AI model touches your data, we ensure strict access controls and permissions are in place. Entra ID security trimming, sensitivity labels, governance — configured first, not as an afterthought. We don't index what shouldn't be seen.
Ground in Reality
We connect models to your actual enterprise data using Azure AI Search and RAG architecture. SharePoint, Confluence, legacy systems — semantically indexed and queried. This mathematically eliminates hallucinations on topics covered by your knowledge base.
Ship to Production
We move past Jupyter notebooks. Scalable Azure Container Apps, Teams integrations, Prompt Flow monitoring, CI/CD pipelines — all documented and handed over. You get a live product, not a proof of concept that sits in staging forever.
Enterprise IT
Helpdesk AI.
How we built it
Indexed SharePoint, Confluence, and legacy ticketing systems to create a unified, secure knowledge base inside Azure AI Search.
Built a custom RAG chatbot deployed natively inside Microsoft Teams — accessible where employees already work, requiring zero behavior change.
Implemented strict Entra ID security trimming so each user's context is mathematically limited to documents they're already permitted to read.
Set up Azure Monitor dashboards and Prompt Flow tracing so the IT team owns the system — no ongoing consultant dependency.