Lionfish Technology Advisors has a competitive intelligence tool called the Aquarium. Working with our Advisors, it allows us to analyze a market and all the players in it. Recently we were doing some analysis on the AI Development/Platform market and realized something of note.
Everywhere you turn, someone wants to build a large language model (LLM) and as such, they want to buy one of the GenAI tools that are household names. The impulse is understandable. Models are tangible, impressive, and fun to demo. Yet the organizations that quietly turn AI into durable value aren’t collecting models like trading cards. They are building platforms.
A model can win you a headline. A platform can win you a line of business.
Let’s define platform. A platform is the repeatable backbone that carries ideas from whiteboard to production with safety, speed, and control. It is less glamorous than a shiny model announcement, but it is the difference between a one-off stunt and a sustainable capability. A good platform does five things well: manages data responsibly, abstracts model choices, orchestrates complex workflows, enforces safety and governance, and operates features in production with clear economics. Start with data, because that is where most grand ambitions quietly wobble.
Enterprise information is scattered and uneven in quality. If you cannot fetch the right facts at the right time with the right controls, your answers will be eloquent and wrong. The platform’s data layer handles pipelines, indexing, and retrieval so that applications speak with the organization’s voice, not a random sampling of last year’s PDFs. It also
encodes policies that keep private things private, not as an afterthought but as a first principle.
Above data sits model access. Today’s best choice for reasoning might be different from tomorrow’s. Costs shift. Latency targets tighten. New fine-tuned variants appear that are fantastic at one niche. An effective platform treats models like interchangeable components. You can call a generalist for deep thought, a compact model for routine flows, and a specialized tool for domain tasks. That flexibility empowers teams to choose
based on outcome, not hype.
Then comes orchestration, the unglamorous hero. Real features rarely rely on a single prompt. They retrieve context, reason, call tools or internal APIs, validate intermediate steps, and then compose an answer. Without orchestration, you have clever demos. With it, you have systems that keep working under real-world stress.
Safety and governance form the platform’s conscience. You want your applications to be helpful and harmless, and you want to prove it. That means input filtering, redaction where appropriate, policy checks, and evaluation suites that measure not just accuracy but behavior. It means audit trails that can tell a straight story when a regulator or customer asks a hard question.
Governance that lives inside the platform saves you from decorating each new feature with bespoke controls.
Finally, operations and experience bring the results back to earth. Can you trace how a response was formed? Can you monitor cost and quality by use case, not just in aggregate? Can you run A/B tests on prompts or agent flows without holding your breath? If the answer is yes, you are ready to iterate with confidence.
If the answer is no, you are still in the demo phase, even if your app has a thousand users. A reasonable objection is that all this sounds heavy. Isn’t the charm of modern AI how quickly one can prototype?
The point of a platform is not to slow you down; it is to make speed repeatable and safe. The trick is to start thin and grow by demand. Begin with an opinionated golden path for two or three patterns you know you will need. Retrieval-augmented question answering for internal knowledge. Document synthesis for research and client deliverables. Agentic task execution when you need tools to do the work, not just talk about it.
Each path should include templates, tests, and dashboards soteams get useful defaults without reinventing the wheel. There is also the matter of cost. Models are not free, and enthusiasm can inflate invoices. A platform helps you spend like a strategist. A simple model router can send light tasks to efficient models and reserve the heavy hitters for the moments that justify them. Observability closes the loop.
When you can see cost per outcome and quality per dollar, you stop arguing in the abstract and start tuning with facts. Culture matters as much as code. Treat the platform like a product with customers, roadmaps, and service levels.
Give it an owner who says no to features that do not belong and yes to the paved paths that make adoption easy. Bring risk and compliance in early, not to slow things down but to help you design controls once instead of ten times. Create a rotating red team that pressure-tests prompts and flows.
Keep a living library of attacks and oddities and run it as part of continuous evaluation. Make the platform your shared memory as well as your shared machinery. What about building your own model? Some organizations will benefit from training or fine-tuning, especially where proprietary data or niche tasks make a difference.
The platform makes that happen.
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Scott Nelson, Advisor
Lionfish Tech Advisors, Inc.
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Title background image sources: AI and public domain.