Custom AI Integration

Copilot Studio is not always the right tool, and the cost of pretending it is shows up later, in production.

Copilot Studio is the right tool for fast, Microsoft embedded AI deployments. It is not always the right tool. When it is not, custom agents or AI solutions that need accurate source citation, cost sensitive high volume deployments, or architectures Microsoft's stack does not support well, we build with Anthropic, OpenAI, or a custom LLM's and  retrieval pipeline instead. 

We evaluate model and platform choice honestly for each use case, then build the integration: API connections, retrieval augmented generation pipelines, and custom backend architecture where a low code platform would fall short. 

How we help 

  • Model evaluation: Custom LLM API versus Copilot Studio versus Azure AI Foundry, assessed against your actual cost, accuracy, and compliance requirements, not vendor defaults. 
  • RAG architecture: custom retrieval pipelines using vector search and content indexing for public facing AI that needs to cite real, accurate sources rather than hallucinate them. 
  • API integration: direct integration with Anthropic, OpenAI, or other model providers into your existing systems. 
  • Model layer migration: replacing an underperforming model behind an existing AI product without rebuilding the whole platform. 

Technology 

  • Anthropic API 
  • OpenAI API 
  • Cloudflare Workers 
  • Vectorize 
  • Firecrawl 
  • Mistral 
  • LangChain style orchestration patterns 
  • vector databases for retrieval 

 

Why it matters 

 

We have seen that Copilot Studio or other low-code platform are not always the best fit for AI use cases where answers must be grounded in accurate source material and source references need to be reliable. In those situations, a custom RAG architecture can give more control over how content is collected, indexed, retrieved, and presented to the user.
 
Retrieval augmented generation only works if the retrieval half is actually good. A model citing the wrong source confidently is worse than one that admits it does not know. That is why we focus on the indexing and retrieval layer, content chunking, embedding quality, and relevance scoring, instead of treating the model alone as the solution.

 

 

Frequently asked questions

Usually because the use case calls for a different balance of accuracy, cost, flexibility, integration depth, or control over the underlying architecture. We evaluate the options honestly against the actual requirements rather than defaulting to one platform or vendor.
Retrieval augmented generation grounds AI responses in selected source material instead of relying on the model’s general knowledge alone. It matters whenever answers need to be traceable, up to date, or based on a specific knowledge base, whether the solution is internal, customer-facing, or embedded in another workflow.
Yes. Model layer migration, swapping the AI engine behind an existing product without rebuilding everything around it, is exactly the kind of work we have done, including moving a RAG platform from Mistral to a different model.
Not necessarily. The right cost model depends on usage volume, model choice, hosting architecture, maintenance needs, and how much control the solution requires. We compare the realistic options before recommending whether to use a low-code platform, direct model APIs, or a custom architecture.