AI That Works Smarter
In any business, knowledge is power. But for many middle market companies, where resources are stretched and institutional memory often lives in outdated systems or with just a few key people, that power often remains untapped.
The real competitive edge of a company is not just its software or customer base. It is the depth of collective experience that lives within the organization. This includes the lessons learned across teams, the processes that succeeded or failed, and the operational insight gained through years of execution. Too often, that insight is scattered across emails, folders, or locked in the heads of veteran employees, fragmented and difficult to access.
Recent advances in artificial intelligence are beginning to change that. Large language models, or LLMs, now have the ability to analyze, synthesize, and communicate information at near-human levels. On their own, they are powerful. But when paired with a company’s proprietary knowledge using a Knowledge-Grounded Assistant, they become even more valuable - a system for turning historical knowledge into a living, usable resource.
Complementing this is the growing popularity of LLM aggregation, a shift especially relevant for middle market companies. Rather than committing to a single model or building bespoke integrations, LLM aggregators serve as the connective tissue between your internal systems and a growing number of external AI models. They allow your business to work through a single access point while remaining flexible and adaptable. You are not exposed to vendor lock-in or disruptions, and as models improve or new capabilities emerge, you can adopt them immediately without overhauling your infrastructure.
At Turning Point, we have seen firsthand how combining these technologies can streamline work and unlock hidden value. We developed our own knowledge-grounded assistant, ZenoIQ. With this tool, we have been able to create unique agents to support different parts of our business, but the most transformative has been our “Everything Turning Point” retrieval agent. This tool brings together years of training materials, internal documentation, articles, presentations, and task-specific resources into a single, searchable platform. We refined the output to make it immediately usable and value-added. It has become the starting point for nearly every new client engagement.
Rather than spending hours digging through legacy folders or asking around for examples, our team can instantly surface relevant insights, drawing from a constantly growing dataset that reflects how we actually work. Each week, we continue to add new material to the system and incorporate thoughtful user feedback to improve accuracy and usefulness. As a result, the agent continues to evolve with us. It is not static content. It is a living, learning system that is becoming more valuable over time.
This use case reflects a broader opportunity for middle market companies to put their knowledge to work. Many are still not seeing meaningful ROI from their AI investments, often because those efforts are limited to research support or writing assistance. By creating a knowledge-grounded assistant, combining it with an LLM aggregator, and tailoring outputs to specific needs and tasks, companies can begin realizing real, measurable value. Start by curating what already exists within your business – internal documents, processes, reports, presentations – and feed that into a well-structured AI retrieval pipeline. Pair with an LLM aggregator, and you gain the ability to dynamically choose the best model for any given task based on speed, accuracy, or cost.
What we have learned through building our own internal agents is that usefulness grows with consistency. The more reliable and well-curated the data, and the more rigorously the feedback loop is managed, the more valuable the system becomes. It does not take long for a knowledge-grounded assistant to shift from being a novelty to being an indispensable part of daily operations.
The goal is to support your team with tools that make their work easier and more impactful. With the right intelligence layer in place, employees can move faster, ask better questions, and spend less time searching for answers and more time applying insights. In our experience, that is where the real return shows up, in accelerated onboarding, improved client delivery, and stronger internal alignment. You aren’t replacing people, you’re equipping them with sharper tools.
For middle market companies that have built up years of knowledge but struggle to put it to work, now is the time to consider how AI tools like a knowledge-grounded assistant and aggregation can support smarter operations. The ability to build systems that learn with your business, support your people, and strengthen execution is more accessible than ever.
If your organization is sitting on a decade of valuable experience, the question is not whether AI can help. The question is how much longer you can afford not to put that knowledge to work.
*More information about available LLMs and LLM aggregators. This list has not been vetted and is meant to be informative, not exhaustive.
The current landscape of leading LLMs that can be integrated into an aggregator includes, but is not limited to, OpenAI’s GPT-5 and GPT 4o, Anthropic’s Claude Opus 4.1 and Claude Sonnet 4.5, Google’s Gemini models, Meta’s LLaMA family, Cohere’s Command R Plus, Mistral’s Mixtral and 7B models, and other open-source models such as Falcon, XGen, and MosaicML. Each of these models brings different strengths in reasoning, summarization, coding, and conversational ability. Aggregators make it possible to match the model to the task, rather than force every task through the same engine.
There are also several robust platforms available today that support LLM aggregation. Some of the more mature solutions include LangChain and LlamaIndex, both of which are open frameworks that can be customized for specific enterprise needs. More turnkey commercial platforms include tools such as Cerebrium, Fireworks AI, Unstructured.io, and Portkey, each offering varying levels of model orchestration, routing, cost optimization, and governance. These are examples of aggregators that act as the control layer that lets companies deploy, monitor, and scale AI usage across functions.
