Artificial intelligence is rapidly being adopted across financial services, compliance, governance, and risk management as firms seek to increase efficiency without expanding headcount. Yet while general-purpose AI tools have become widely accessible, their usefulness inside regulated enterprises remains limited. The challenge is how to make institutional knowledge available to AI in a controlled, auditable way.

At Longshore Labs, we’ve focused on this question for the past two years. Over the last 18 months, we’ve integrated leading AI engines into enterprise software built specifically for governance teams operating under regulatory constraints.

Rather than experimenting with AI as a productivity shortcut, we’ve approached the technology as an infrastructure problem, one that requires deliberate design to ensure reliability and control.

Most AI systems used today in fintech are powered by large language models (LLMs). These models are trained on vast quantities of publicly available text and are highly effective at summarising information, answering questions, and generating structured explanations. However, they do not have access to an organisation’s internal knowledge.

When an LLM is asked a question about a firm’s policies, regulatory interpretations, or historical decisions, it does not retrieve an answer from internal records. Instead, it infers a response based on patterns learned from public data. Such inference without grounding can produce responses that are incomplete, inconsistent, or simply wrong.

We don’t treat this as a limitation of AI itself. The models are capable—but they need structured, governed access to enterprise knowledge.

Most financial institutions already possess exactly the information an AI system would need to be effective. This includes internal policies and procedures, regulatory guidance and interpretations, risk frameworks and methodologies, research papers, technical memoranda, and historical case notes documenting prior decisions.

The problem is that this information is proprietary, fragmented across systems, and not prepared in a way that AI can safely use. Public AI tools cannot access it, and exposing sensitive documents to AI models raises unacceptable governance and data risk concerns.

A common assumption is that enterprises should simply train or fine-tune AI models on their internal data. In practice, this approach introduces significant challenges. Training is expensive and slow, models quickly become outdated as policies change, and auditability is reduced once knowledge is absorbed into model weights. Most critically, governance teams lose fine-grained control over which information the AI uses and when.

What regulated organisations actually need is not a model that “knows everything”, but a system/framework that allows AI to access approved knowledge only when required, for the sole purpose of answering a specific question, while leaving accountability and decision-making with humans.

Our implementation avoids model retraining. We convert enterprise documents into semantic representations, store them in specialised databases, and retrieve only the approved, relevant context for each query. That scoped context is supplied to the LLM at the time of response to ensure outputs are grounded and controlled.

This approach is built on three core components: embeddings, vector databases and retrieval-augmented generation (RAG).

An LLM can be thought of as a highly articulate consultant, capable of reasoning, explaining complex topics and communicating clearly. But also one who has never read the organisation’s internal documents.

Now imagine pairing that consultant with a smart librarian. When a question is asked, the librarian finds the most relevant internal materials and hands them to the consultant, who then responds using only the given information.

In this analogy, the document summaries are embeddings, the librarian is the vector database, and the overall interaction is retrieval-augmented generation.

Embeddings convert text into mathematical representations of meaning. They allow systems to recognise that different phrases may refer to the same underlying concept. For example, “high-risk jurisdiction”, “FATF grey-listed country”, and “sanctions-exposed geography” may use different words but describe a similar risk category.

By working at the level of meaning rather than exact wording, embeddings allow AI systems to understand what documents are about, not just what they say.

A vector database stores these meaning-based representations and enables rapid retrieval of relevant information, even when the wording of a question does not match the original source material. Unlike traditional databases, which depend on exact matches, vector databases search semantically.

Within Longshore’s platforms, this ensures that the most relevant policies, guidance or research are consistently retrieved in response to a query, providing stable and predictable results across teams.

Retrieval-augmented generation is the mechanism that brings everything together. When a user asks a question, the system interprets the intent, retrieves the most relevant approved internal documents, and supplies only that selected context to the AI model. The model then generates its response based on enterprise-approved information rather than general assumptions.

This ensures accuracy and consistency, while preserving a clear link between each response and its source material.

Sitting above this retrieval layer is the Model Context Protocol (MCP), which acts as the governance and control mechanism. The MCP defines how, when and under what rules context is provided to AI models. It ensures that only authorised data sources are accessed, that sensitive information is protected, and that the AI receives no more information than is strictly necessary for the task at hand.

The Model Context Protocol functions both as a secure courier and a rulebook, making AI access controlled and fully auditable, which is essential for strong governance and regulatory alignment.

This architecture fundamentally changes the risk profile of enterprise AI. Rather than relying on guesswork, AI operates within clearly defined knowledge boundaries. Information is versioned and controlled, responses can be traced directly back to source documents, and updates can be applied instantly without retraining models.

Crucially, human accountability remains intact. AI acts as decision support, not an autonomous authority, aligning naturally with regulatory expectations around explainability and oversight.

Without this structure, organisations often fall back on ad hoc use of public AI tools. Employees manually paste documents into chat interfaces; outcomes depend on individual prompting skill, and responses vary widely. Institutional knowledge remains fragmented, audit trails are weak, and governance relies on personal discipline rather than on system design, thereby raising both operational and regulatory risk.

 AI should not replace enterprise judgment. It should amplify it by leveraging organisational knowledge within controlled, auditable systems.

With this architecture in place, staff gain consistent access to reliable answers grounded in approved internal information. Time spent searching for documents or reconciling conflicting guidance is reduced, decision support improves, and governance remains embedded throughout the process. AI becomes a dependable assistant rather than an unpredictable tool.

Enterprise AI becomes truly valuable not when models become more powerful, but when knowledge is made accessible in a controlled, explainable, and repeatable way. By transforming documents into embeddings, organising them in vector databases, governing access through MCP, and delivering context via RAG, organisations move beyond experimentation toward engineered intelligence.

This is how AI stops guessing and starts supporting real decisions at scale. And it is the architectural philosophy that underpins Longshore Labs’ approach to enterprise AI in regulated environments, developed in close collaboration with industry stakeholders.