Good documentation has always been valuable. In the age of AI, it is a requirement.
AI coding tools need documentation to understand how a platform works, what APIs it exposes, what configuration options are available, and how different parts of the system fit together. The better the documentation, the better the AI.
That matters for insurers because AI is quickly moving from novelty to infrastructure. It is starting to help insurers with configuration, workflow automation, integrations, and product development. But AI systems can only work effectively when the software they interact with is understandable without human explanation and consistent enough to rely on over time.
- Needs no further explanation
- Promotes self-sufficient engineers
- Empowers AI effortlessly
- Needs heavy explanation
- Frustrates engineers
- Blocks AI
Why AI Needs Strong Documentation
AI coding tools and agents work by retrieving context from available sources. Documentation that is consistent, structured, comprehensive, and AI-accessible can be discovered, indexed, and incorporated into those workflows.
In one sense, AI needs the same things human developers need. It benefits from stable terminology, comprehensive coverage, a single source of truth, and documentation that explains not just what exists, but how the pieces fit together.
But AI is still different from a human developer who can ask a colleague, email the vendor, or rely on training when documentation is incomplete or contradictory. When information is missing, outdated, fragmented across versions, or buried in support artifacts; the quality of the AI’s output drops.
That is why documentation quality directly affects AI quality. If the platform is hard for engineers to understand, it will be even harder for AI agents.
Insurance Struggles With Documentation
In principle, AI does not care whether documentation is public or private. In practice, however, hidden documentation is rarely (if ever) good.
Publishing documentation creates pressure that private documentation does not. It invites scrutiny. It ties documentation quality to company reputation. It forces the product to be explainable without a company representative in the room first. It prevents vendors from exaggerating or outright misrepresenting product capabilities.
Why Most Insurance Technology Vendors Have Poor Documentation
1. The platform has too many versions, too many one-off implementations, or too much customer-specific customization to support a shared set of documentation.
If different customers are effectively using different software, then a single source of truth becomes difficult to maintain. Public documentation reveals to customers which features other customers are getting and how unevenly the product evolves across implementations.
2. The system is so incoherent that they depend on human training to smooth over confusing design.
In those environments, documentation does not truly stand on its own. The real product knowledge lives in implementation teams, support threads, and accumulated tribal knowledge. That may be frustrating for engineers, but it’s a total blocker for AI.
This is why public documentation is such a strong signal. A vendor with comprehensive public documentation is telling you their platform is consistent enough to explain non-interactively, stable enough to rely on over time, and open enough that customers do not need the vendor in the room for every decision.
The Top 25 Public Software Companies Have Public Documentation
This is the best practice. There is no debate. See for yourself…
The Socotra Approach Documentation
At Socotra, we have long viewed documentation as part of the product, not an afterthought.
That means building software that is clean enough to document intuitively. It means going beyond API references, to describing key concepts and including “getting started” guides. And it means deploying and maintaining modern documentation frameworks.
That perspective informed our recent investment in a new public documentation platform and AI-powered documentation experience.
Socotra recently launched a new documentation site built on Fumadocs, a modern documentation framework designed for developer-centric platforms. The new platform improves discoverability, navigation, machine accessibility, and content portability. It also introduces first-class support for LLM consumption: every page is accessible as raw Markdown through a .md URL, and a structured llms.txt index enables AI agents to discover and navigate content efficiently.
These technologies have ultimately made it more efficient for AI to ingest and navigate our documentation.
Documentation that Chats With You
Socotra has also launched Socotra Assistant, an AI-powered documentation assistant fluent in Socotra documentation.
Technical documentation often contains information users need only occasionally. The challenge is not always the existence of documentation, but finding it when needed.
Socotra Assistant provides a conversational interface across all knowledge of the Socotra platform. Instead of navigating multiple pages or refining search queries, users can ask questions directly, in any language, and receive responses backed by the underlying documentation.
Typical questions users might ask Socotra Assistant
How do I build a product?
Explain data extensions and custom data types.
How do I set up a rating table?
What event types are available?
Explain how to update billing settings for an account, policy, or quote.
How do I configure a coverage term?
Agentic Configuration Needs Documentation
We have written previously about Socotra Agentic Configuration and demonstrated how AI can build a working insurance product in hours instead of weeks. In a recent hands-on workshop, insurance technology leaders used Agentic Configuration to build insurance products complete with rating logic, underwriting rules, and operational workflows—all in less than three hours.
That kind of AI-assisted development is only possible because Socotra provides AI with the context it needs. Socotra’s products, data model, APIs, plugins, and workflows are structured and comprehensively documented. AI is not guessing how the platform works; it’s building against a well-defined system with a single source of truth.
Without that foundation, Agentic Configuration becomes much more difficult. AI cannot reliably configure a platform when documentation is incomplete or implementation differs from customer to customer.
Therefore, Agentic Configuration is not just a feature. It is the result of years of investment in modern platform architecture, comprehensive documentation, and an elegant data model that both humans and AI can understand.
Documentation Is Core Product
For years, insurers could treat documentation as a secondary issue. If the product were powerful enough and the implementation team were strong enough, weak documentation could be tolerated.
That is no longer true.
AI agents need context. They need consistency. They need structure. They need documentation that is accurate, accessible, and efficient to consume.
That is why strong documentation is now a strategic software requirement for insurers. It affects how quickly developers can build, how safely AI can operate, how easily business users can find answers, and whether agentic product development is possible at all.
Public documentation is not the whole story. But it is often the visible evidence of something deeper: a coherent product, a scalable architecture, and a vendor willing to publicly declare what its product does and doesn’t do.
That foundation is what enables Socotra Assistant. It is what makes token-efficient documentation possible. And it is what powers Socotra Agentic Configuration.
