You don't have client AI case studies on your site. Why should we trust your capability?

Because enterprise AI is primarily an integration and engineering challenge, and that is what we have built for 25 years. Our demos are not mockups: DANUB and BERNARD are functional agents you can interact with. The gap between a functional demo and a production deployment is architecture, governance, and integration with your specific systems. Those are problems we know how to solve.

What about hallucination and accuracy?

We use Retrieval-Augmented Generation (RAG) as the foundation of every knowledge agent we build. The agent does not rely on what the model knows in general: it retrieves information from your specific data sources and uses the model to reason over and present that information. Answers are grounded in your data. For high-stakes decisions, we design human-in-the-loop workflows.

What is MCP and do we need it?

MCP (Model Context Protocol) is the emerging standard for connecting AI agents to enterprise systems: databases, APIs, internal tools, and data sources. If you want an AI agent that knows about your specific business data, MCP is how you expose that data to the AI in a structured and governed way. We design and build MCP servers. Whether you need it depends on what your AI agent needs to know and where that data lives.

We want to start with AI but do not know where to begin.

The most useful first step is identifying a concrete use case: a specific task your team does repeatedly that requires gathering information, synthesizing it, and producing an output. Market research, product recommendations, customer briefing preparation, document classification, compliance checking. We scope these with you, assess the data and integration requirements, and propose a pilot. We do not recommend broad AI transformation without a specific starting point.

AI Integration Services

AI that connects to your systems. Not AI that replaces them.

Most AI implementations fail not because the technology is wrong, but because it was disconnected from the systems, data, and workflows where the real work happens. Create IT builds AI agents that integrate with what you already have: your SharePoint knowledge base, your CRM, your ERP, your product catalogue, your sales data. We bring 25 years of enterprise integration experience to AI, so the agents we build actually work in production.

Integration-first, not AI-first

There is a pattern in failed enterprise AI projects: a capable model, poorly connected to the right data. The model does not know your products, your business rules, or your customers, because nobody built the integration layer that would tell it. Garbage in, garbage out, scaled to production speed. The AI does not know your products, your business rules, your customers, or your internal processes. It gives generic answers. Teams stop using it. The project is shelved.

We approach AI differently. Before we build any agent, we map the data sources the agent needs to be useful: product catalogues, pricing rules, customer history, competitive intelligence, internal documents, operational systems. Then we architect the integration layer that connects the AI to those sources reliably and securely. The model is the last decision, not the first.

This is systems integration applied to AI. It is what we know how to do.

Our current focus: AI agents

We are concentrating our AI work on agentic systems: AI that does not just answer questions, but takes a task, reasons across multiple data sources, and produces a structured output your team can act on. A market intelligence report. A product recommendation with supporting documentation. A sales brief tailored to a specific customer's purchasing history. Work that currently takes hours, automated to minutes.

AI agent types

Knowledge and product advisors

AI agents that help staff or customers navigate complex product or service catalogues. The agent takes a need or a question, asks clarifying questions, consults product specifications and business rules, and returns a recommendation with supporting documentation. Deployable on intranet, customer-facing website, or in-store devices.

Market and competitive intelligence agents

Agents that continuously monitor specified sources (company websites, news, LinkedIn, industry publications, RSS feeds, APIs) and generate structured intelligence reports: executive summaries, full analyses, competitive comparisons. Output to Word, PowerPoint, and Copilot. Configurable by theme, subject, geography, and competitor set.

Commercial and sales assistants

Agents that prepare sales teams before client interactions: account summaries, purchasing history analysis, relevant promotional offers, suggested sales scripts based on marketing materials. Available as text or voice-based interaction, and as mobile push notifications aligned to a salesperson's schedule and route.

Custom agents for specific business processes

If your use case does not fit a standard pattern, we scope and build custom agents: document processing, internal knowledge retrieval, compliance checking, workflow automation with AI decision steps. The architecture depends on the data, the use case, and the integration requirements.

MCP development

We design and build MCP (Model Context Protocol) servers that connect AI systems to your internal data sources, APIs, and tools. MCP is becoming the standard protocol for AI-to-enterprise-system integration. We build the connectors that make your systems available to AI agents securely and reliably.

Built by us, ready to demonstrate

Before taking AI to clients, we built for ourselves. Our R&D team developed three AI agent concepts grounded in real enterprise use cases.

Demo available

DANUB: Product Advisor Agent

A product advisor agent that helps staff and customers find the right product for their need. The agent receives a specific question or requirement, asks context questions to refine the request, consults product catalogues, specification sheets, and business rules stored in SharePoint, and returns a recommendation with useful information, accessories, and downloadable documentation.

Deployable across three channels from the same agent: intranet (for staff), in-store tablet, and B2B website.

Architecture: Microsoft Copilot, SharePoint (knowledge base), business rules integration.

Demo available

BERNARD: Market and Competitive Intelligence Agent

A market and competitive intelligence agent that monitors a defined set of sources and generates structured reports on request or on schedule. Sources include competitor websites, public websites, news sites, RSS feeds, subscription services, LinkedIn profiles and company pages, and APIs. Data is indexed into SharePoint and surfaced via Copilot.

The agent generates an executive summary and a full report, filterable by context, theme, subject, and topic. Export to Word and PowerPoint. Configurable for individual or team delivery.

Architecture: Microsoft Copilot, SharePoint, web crawling and ingestion layer, Companies Systems integration.

In development

DEVALIS: Sales Assistant Agent

A sales assistant that prepares sales representatives before client visits. The agent draws from financial data, promotional campaign data, marketing materials, and company systems to generate a client-specific brief: current account status, purchasing habits, relevant promotional offers, and a suggested sales script.

Available in text (V1), real-time voice conversation (V2), and as a mobile push notification with informational audio aligned to the salesperson's schedule and route (V3).

Architecture: Microsoft Copilot, financial and promotional data systems, marketing materials integration, mobile application.

What organizations are asking for

Requests come from organizations in various industries. They share a common starting point: existing systems with valuable data, and an interest in making that data useful to AI without replacing the systems that contain it.

AI agent and chatbot development:

Custom-built conversational agents connected to internal knowledge bases and operational systems

MCP server development:

Building the integration layer between AI systems and existing enterprise data sources and APIs

AI bot auditing:

Assessment of existing AI implementations for accuracy, governance, bias, and production reliability

Data lake architecture for AI:

Designing the data foundation that AI systems need to be useful: structured, clean, accessible, and governed

How we Build

Microsoft Copilot and Azure AI

Our AI agent architecture centres on Microsoft Copilot and Azure AI services. This is consistent with our Microsoft Solutions Partner status and the Microsoft ecosystem depth we have built over 25 years. For organizations already in Microsoft 365, our agents integrate naturally with the tools your teams already use.

RAG (Retrieval-Augmented Generation)

We ground AI outputs in your data, not in the model's general training. RAG architecture connects the AI to your specific knowledge sources (SharePoint, databases, document repositories, APIs) so responses are accurate, current, and relevant to your business, not to the world in general. This is also the primary mitigation for hallucination risk.

MCP (Model Context Protocol)

MCP is the emerging standard for connecting AI agents to enterprise systems. We build MCP servers that expose your internal data sources, tools, and APIs to AI agents in a structured, secure, and auditable way.

.NET and Azure integration layer

The integration between AI and enterprise systems is software engineering, not just AI configuration. We build the connectors, data pipelines, and API layers in .NET and Azure, using the same engineering standards we apply to all our integration work: monitored, documented, and production-grade.

What we bring that most AI vendors do not

25 years of enterprise integration experience

The hardest part of enterprise AI is not the model. It is connecting the model to the right data, in the right format, from the right systems, with the right governance. This is systems integration. It is what we have done for 25 years across retail, financial services, hospitality, and healthcare. We are applying that experience directly to AI.

We built before we sold

We did not launch an AI practice on the basis of a slide deck. We built our own agents first: DANUB, BERNARD, and DEVALIS are internal R&D investments, not client-facing promises. Building them taught us where AI agents actually break: poor data quality, ambiguous instructions, and the gap between what a demo does and what a production system needs to handle. You can see them working before you commission anything.

Microsoft ecosystem depth

Our AI agents are built on Microsoft Copilot and Azure AI services, integrated with SharePoint, Active Directory, and the Microsoft 365 tools your teams already use. For organizations in the Microsoft ecosystem, this means AI that extends what you have, not a parallel stack to manage.

A dedicated R&D team

We have a dedicated data and AI engineering team focused exclusively on building and evolving our AI agent capability. This is not a side practice or a capability claimed on the basis of one project. It is an active investment.

More case studies

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Transforming Q-Commerce: A Portuguese Retailer's Instant Delivery App

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Create IT Achieves Microsoft Solutions Partner Status in Azure Digital & App Innovation

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May 6, 2025

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Tour Tailors boosts efficiency with Kanban Zone and Create IT

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Frequently asked questions

Let's look at your options together.

If you want to understand what AI agents can do in your business context, the fastest way is to see one working. We can show you in a 30-minute session and discuss whether it makes sense for your organization.

Talk to our team

AI Integration