The Shift: AI needs to move beyond single-model thinking

A common pattern has emerged in AI adoption. Many organizations begin with a single API approach, assuming that a foundation model - whether from OpenAI, Anthropic, or a Cloud Service Provider (CSP) - can handle all their needs.

It works at first. Quick integrations, immediate results. But soon, limitations surface

• A single model lacks adaptability. It cannot adjust to different modalities (speech, text, vision) with equal accuracy.
• Retrieval of relevant knowledge is inconsistent. Enterprises need models that understand context, not just generate responses.
• Costs escalate quickly. A proprietary API means businesses pay per call, without the flexibility to optimize inference costs.

This is where Compound AI become essential. Instead of relying on a single model to handle everything, enterprises are now orchestrating multiple specialized AI models to work together.

• Speech-to-text models handle voice interactions. Vision models extract insights from images and video.
• Embedding-based retrieval models structure and enrich knowledge. 
• LLMs reason over retrieved context instead of generating blindly.
• This shift is happening across industries, and we’re already seeing it in enterprise AI deployments today.

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Case study: European insurance brokerage is using Compound AI to drive sales productivity

A European insurance brokerage sought an AI-driven system for its sales teams - one that could capture insights from high-stakes B2B sales calls and make them retrievable for future reference.

The problem was clear

• Their meetings involved complex negotiations, financial structuring, and regulatory discussions that were critical for follow-ups.
• Traditional speech-to-text models provided transcripts but lacked contextual depth.
• A single LLM query couldn’t accurately surface insights when sales reps needed to reference past discussions.

 A Compound AI System was the solution

• Whisper V3 captures and transcribes speech from meetings. 
• QwenVL 2 extracts slide content and visual elements.
• An embedding model with Milvus enriches and stores meeting data for retrieval. 
• Hybrid search retrieves insights using both keyword-based and semantic search. 
• Llama 3.1 8B provides natural language responses based on retrieved information.

Now, when a sales rep asks, "What were the CFO’s concerns about risk coverage in our last meeting?" the system doesn’t just return a transcript. It surfaces the most relevant sections of the conversation, along with any referenced slides, giving sales teams precise access to critical insights.
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Why is Compound AI hard to scale? Why does Pipeshift exist?

At first glance, this approach may seem straightforward - integrate a few AI models, and the problem is solved. In reality, however, designing a robust, production-ready Compound AI System is complex.

AI orchestration is a non-trivial challenge. Deploying multiple models isn’t just an engineering task. It requires MLOps expertise to ensure scalability, efficiency, and low latency. 

The open-source tradeoff: flexibility vs. complexity. CSPs make it easy to consume AI via simple APIs, but at a cost - limited control, hidden expenses, and rigid architectures. Open-source AI allows greater flexibility, but enterprises must navigate infrastructure choices, fine-tuning strategies, and retrieval optimizations.

Each stage of AI adoption has distinct challenges. The complexity isn’t just in deployment - it begins from the moment AI is introduced into an organization.

• POC Design - Defining the right architecture from the start.
• Evaluating the POC - Understanding beyond just accuracy metrics - retrieval precision, cost efficiency, and usability.
• Production Deployment - Managing compute resources, observability, and API efficiency at scale.
• Scaling AI in Production - Handling multi-modal workflows, inference cost optimization, and governance requirements.

This is where Pipeshift plays a critical role.

We help teams think through AI implementation holistically - from selecting the right model architectures in the POC stage to ensuring cost-efficient, scalable deployment across the enterprise.

The hard realization about AI implementation

The biggest challenge in enterprise AI isn’t choosing the right model - it’s designing the right system. We’ve seen companies rush into AI adoption, only to face roadblocks later. Some test single API models, only to realize retrieval is insufficient. Others deploy AI in production, only to struggle with latency, cost, and governance issues.

The key takeaway?

AI isn’t a one-size-fits-all model - it’s an interconnected system that needs to be designed, optimized, and orchestrated. And that thinking needs to start early. We’ve worked with enterprises that realized this too late - teams that had to re-architect their AI workflows from scratch after running into scalability issues. 

Pipeshift was built to help companies avoid these pitfalls. Whether it’s architecting a proof-of-concept, evaluating performance, or scaling AI across global teams, we work with organizations to build AI systems that are robust, efficient, and future-proof.

Thinking about Compound AI for your business? Let’s discuss your AI strategy.