AI is undergoing a pivotal shift—from static chatbots that respond to queries, to autonomous agents capable of perceiving context, making decisions, and executing complex workflows with minimal human input. For builders, this evolution brings a fundamental question: How do we architect systems that are not just AI-enabled, but AI-native—purpose-built to automate, adapt, and accelerate business outcomes at scale?

At Reap, our mission is to create global financial infrastructure that’s fast, inclusive, and powered by stablecoins. We’re building tools that enable money to move smarter across borders—and now, we’re exploring how AI can help us build faster, serve better, and unlock entirely new capabilities.

Why AI, why now?

The AI ecosystem has matured rapidly. Scalable large language models (LLMs), frameworks like LangChain and LangGraph, and advances in memory, tool use, and orchestration now make it possible to go beyond simple conversation. Today’s AI systems can reason, act, and learn dynamically—much like human collaborators.

We don’t view AI as a surface-level feature or a plug-in. At Reap, we see it as a as a new way of architecting intelligent workflows. From cognitive architectures that mimic decision-making to agent frameworks that dynamically interface with APIs and data, we’re exploring how intelligence can be embedded into the foundations of our workflows.

For example, even with the most well-written documentation and how-to tutorial videos, users often still ask 'what should I do next? This persistent friction highlights a broader challenge in customer experience: information alone does not guarantee clarity or confidence.

AI can bridge that gap—not just by surfacing knowledge, but by contextualizing it and, in some cases, acting on it.

AI in action: A smarter documentation assistant with RAG

One of the most immediate applications of AI in any complex system is knowledge retrieval. Whether for customers or internal teams, getting accurate answers quickly is crucial. Traditional documentation, while valuable, often fails to deliver information in a frictionless way. This is where Retrieval-Augmented Generation (RAG) can bridge the gap and bring context aware information.

The following simple Langflow workflow demonstrates a bot processing our public ReadMe documentation, automatically crawls knowledge sources, and embeds the content into a vector database.

The workflow begins by using a web crawler node to automatically fetch content from ReadMe pages. The retrieved data - typically in HTML or Markdown format is then cleaned and parsed to extract meaningful text.

Next, the textual data is chunked into manageable segments (e.g. 500 - 1000 tokens), optionally with overlap, to preserve semantic context across boundaries. These chunks are then passed to an embedding model such as OpenAI’s text-embedding-ada-002, which converts them into high-dimensional vector representations. The resulting vectors are stored in a vector database like AstraDB.

Card Issuing Documentation: https://reap.readme.io/docs/getting-started

Product knowledge now becomes accessible to a RAG system. When a user submits a query, the system first encodes it using OpenAI embeddings and performs a semantic vector search against a pre-indexed knowledge base stored in a vector database such as AstraDB. 

The most relevant document chunks are retrieved based on cosine similarity.

These chunks are then fed as part of the context window to the LLM, typically along with the user's query, using a technique called prompt engineering. This enables the model to generate factually grounded, context-aware responses that reference up-to-date, domain-specific documentation. Additionally, this architecture helps mitigate hallucinations and ensures that the generated responses remain aligned with internal knowledge and policies.

Results

• Faster, more accurate answers: Users receive precise responses grounded in real documentation, reducing confusion and support dependency

• Scalable knowledge access: Automatically keeps AI answers up to date as documents evolve

The following example shows how FX markup is grounded to product documentation

Key takeaway for developers

RAG-powered assistants can drastically reduce friction in knowledge-heavy industries. The key is not just retrieval, but integrating feedback loops and precision-tuned prompts to ensure reliability. Any business with complex documentation can benefit from this approach.

The future of AI in engineering & operations

AI is no longer an experiment - it is a foundational layer in modern system architecture. The next wave of AI adoption will focus on

• Expanding AI-driven automation beyond customer support and into core operational processes.

• Enhancing security, governance, and monitoring to ensure reliability in production environments.

• Leveraging multi-modal AI capabilities to handle structured financial data and process automation.

For engineers and product builders, the question is no longer whether AI should be integrated, but how to design AI-native architectures that are scalable, reliable, and seamlessly embedded into existing systems.