AI That
Ships

Theoretical AI is a cost center. We build practical AI applications — from Copilot implementations to custom RAG agents — that solve specific business problems and get into the hands of your users.

The Challenge

The reality of enterprise AI.

Taking AI from a slick technology demonstration to a secure, enterprise-grade application running in production is an entirely different discipline.

Endless Proof of Concepts

Great ideas that never make it to production. You have Jupyter notebooks in a repository that successfully demonstrated an AI capability. But because it wasn't integrated into a user workflow or deployed securely, it hasn't generated a single dollar of business value.

The Hallucination Problem

ChatGPT doesn't know your business context. Business users tried using public language models for work, but they confidently gave the wrong answers about internal policies. Without grounding in your own operational data, AI is just a confident guesser.

Data Security Fears

You're afraid to index data because of poor internal permissions. If you point an AI search at SharePoint, how do you mathematically guarantee an intern can't ask the bot for the CEO's salary document or unreleased financial projections?

Deliverables

Moving from hype to production.

Custom RAG Agents on Azure

Retrieval-Augmented Generation systems securely grounded in your proprietary company data. We build chat interfaces that securely source knowledge from your internal documents, databases, and APIs without exposing data to public models.

Microsoft 365 Copilot Readiness

Before you turn on M365 Copilot, we audit your Entra ID and SharePoint permissions, preventing oversharing and securing sensitive corporate data.

Azure AI Studio Integration

Leveraging Azure AI Search, Azure OpenAI, and Prompt Flow to build highly scalable, enterprise-secured AI architectures natively in your cloud.

Prompt Engineering & Adoption

Technology is only half the battle. We train your teams on how to write effective prompts, build internal libraries of use cases, and drive organization-wide adoption of new AI tools.

Case Study: Enterprise IT

Deflecting 40% of Tier 1 IT Helpdesk Tickets

The Situation

A 2,000-employee company was struggling with escalating IT support costs. Wait times were averaging 4 hours for simple issues like VPN connections, password resets, and software access requests. The helpdesk team was burning out handling repetitive questions instead of complex infrastructure problems.

What We Built

  • Indexed SharePoint, Confluence, and legacy ticketing systems using Azure AI Search to create a unified knowledge base.
  • Built a custom RAG (Retrieval-Augmented Generation) chatbot accessible directly within Microsoft Teams.
  • Implemented strict Entra ID security trimming to ensure users only accessed documents they had network permissions for.

40%

Ticket deflection in Month 2

8 Seconds

Average resolution time

Don't settle for theoretical AI.

Stop building proofs of concept that never leave the staging environment. Let's architect a secure, practical AI application that drives real business value.