Fintech Co-pilot for API: How to Fix Major Issue with API Integrations

Photo of Krystian Bergmann

Krystian Bergmann

Nov 7, 2023 • 9 min read

GenAI can turn old fashioned API Documentation into a powerful and responsive tool for developers. Here’s why it’s essential for Fintech API services to leverage Large Language models (LLMs).

Introduction: Unveiling the Business Context with API Documentation

“Anyone who doesn’t do this will be fired” - proclaimed the final, sixth point of the API Mandate at Amazon. The memo forced all teams to design and build service interfaces so that they could be exposed to external developers.

Internal note was allegedly written by Jeff Bezos himself around 2002. Twenty years later, API platforms are by far one of the most important elements behind any tech ecosystem. Fintech, especially payment or Banking-as-a-Service providers (think Stripe, PayPal, Coinbase, Solaris or Mambu), are being the prime example of what can be achieved through powerful API platforms.

I picked a fintech API for a reason - in a landscape where time is money, API documentation stands as the bedrock that supports and guides developers through the integration processes. It serves as a repository of technical knowledge and a guide, ensuring that the implementations are streamlined, efficient, and aligned with best practices.

Or is it?

The Problem: Navigating Technical Hurdles with API

There are major issues limiting the success of these integrations - surprisingly enough, the number one obstacle is the lack of documentation (2022 State of API Report by Postman, 55% of respondents).

Even if official API documentation exists, it’s rarely being used in full. Most of the time, API platforms are not designed in a user-friendly way: there is no sandbox, guidelines are too complex or, on the contrary, important details are missing. On the other hand, developers may skip over the documentation simply because they are short on time.

When an API is poorly documented or designed, seeking assistance from technical support hardly moves the needle - developers face longer resolution times, higher costs, poor user experience; all hampering the developer's productivity and project timelines, not to mention frustration on both sides of the table.

10 Ways GenAI and LLMs Are Improving API Developer Experience

Enter GenAI, a system anchored on Language Model Learning (LLM). GenAI is meticulously trained on company documentation, emerging as an invaluable ally - an "Integration Co-Pilot" for developers. Armed with the capability to address and resolve any technical queries, GenAI can streamline the integration process, allowing developers to navigate the technical landscape with ease and precision.

GenAI acts as a beacon, guiding developers through the intricate pathways of integration, offering real-time, intelligent responses to technical inquiries, thereby drastically reducing the dependency on traditional technical support channels.

This is why LLMs are better than old-fashioned technical documentation:

1. Contextual Query Responses:
LLMs can be employed to provide contextual and precise responses to developers’ queries. The model can understand the intent behind the question and fetch or generate a response that is most aligned with the developer's current requirement, thereby enhancing the utility and usability of the documentation.

2. Dynamic Learning and Updating:
AI can learn dynamically from the existing documentation and any new updates or changes that are introduced. This ensures that the Co-Pilot always provides information that is current, accurate, and in line with the latest technical specifications and guidelines.

3. Personalized Assistance:
Leveraging LLMs allows the Co-Pilot to offer personalized guidance to developers. Based on past interactions and queries, the Co-Pilot can tailor its responses and suggestions to meet the individual needs and preferences of each developer, making the guidance more effective and user-friendly.

4. Interactive Troubleshooting:
LLMs enable the Co-Pilot to facilitate interactive troubleshooting sessions. Developers can engage in a back-and-forth dialogue with the Co-Pilot, allowing for a more comprehensive and satisfying problem-solving experience. This can assist in resolving issues more swiftly and with less reliance on external technical support.

5. Enhanced Navigation:
LLMs can assist developers in navigating the extensive documentation more efficiently. The Co-Pilot, powered by LLMs, can direct developers to the relevant sections, examples, and explanations within the documentation, saving time and effort.

6. Code Example Generation:
Utilizing LLMs, the Co-Pilot can generate code examples and snippets that are tailored to the developers' specific queries and issues. This hands-on, practical guidance can significantly enhance the developer's understanding and application of the documentation.

7. Continuous Improvement through Feedback:
LLMs can process and learn from feedback provided by developers, allowing for continuous improvement and optimization of the Co-Pilot’s assistance and the overall documentation.

8. Natural Language Interaction:
LLMs facilitate natural language interactions, making the Co-Pilot more accessible and user-friendly. Developers can ask questions and express their queries in a natural, conversational manner, allowing for a more intuitive and engaging user experience.

9. Multi-language Support:
LLMs enable the Co-Pilot to support queries in multiple languages, making the documentation more accessible to a global audience of developers.

10. 24/7 Availability in a Multi-Location Environment:
LLMs empower the Co-Pilot to operate beyond the confines of time zones and geographical locations, ensuring that developers around the globe receive uninterrupted assistance 24/7. Regardless of where the developers are located or what time they seek assistance, the Co-Pilot, fueled by LLMs, is always available to provide real-time, invaluable guidance and support.

This perpetual availability fosters a more flexible and resilient integration environment, accommodating the diverse schedules and urgencies of developers worldwide. This makes the API documentation a reliable and constant companion in the integration journey, ensuring that immediate help is always at hand, thereby minimizing delays and maximizing productivity.

By leveraging LLMs in these ways, the API Documentation Co-Pilot can become a more powerful, responsive, and invaluable tool for developers, enhancing their ability to successfully integrate and utilize the available Fintech API services.

7-Step System For API Documentation Co-pilot

GenAI is no ordinary solution; it is powered by advanced technologies chosen to enhance its efficiency and responsiveness. The system is based on cutting-edge language model learning, ensuring that it is updated and aligned with the latest in company documentation and technical advancements.

This is how the process works:

Fintech Co-Pilot API  (1)

Step 1: Build Document Corpus

A collection of documents (or data) is compiled. This serves as the primary source of information.

Step 2: Generate Embeddings

The documents from the corpus are processed to generate embeddings. Embeddings are a form of vector representation that captures the semantic meaning of the documents.

Step 3: Vector Storage

The generated embeddings (vectors) are stored in a database or storage system.
User Query: A user submits a query or question.

Step 4: Generate Embeddings

The user's query is also processed to generate an embedding. This embedding will be used to find the most relevant information from the document corpus.

Step 5: Find Closest Matching Document Chunks

The system searches the stored vectors (from Step 3) to find the closest matching chunks or segments of documents that align with the user's query embedding. This is typically done using similarity measures.

Step 6: Retrieve Text for Top Matching Chunks

The actual text or data from the top-matching chunks is retrieved, based on the similarity search from the previous step.

Step 7: Prompt Engineering / Calling API

The system might further process the retrieved information, possibly using additional prompt engineering or by calling an external API.

Response: The system provides a response to the user based on the retrieved and possibly processed information.

GenAI is Charting the Path to Seamless API Integrations

GenAI is not just a technological solution; it is a transformative approach that redefines the integration experience for developers in the fintech landscape.

It can mitigate the challenges, simplify complexities, and allow developers to focus more on engineering puzzles instead of trying to make sense of a poorly designed or documented API.

PS. This particular challenge with API documentation came up during one of our AI Primer workshops. We facilitate them with companies to:

1. Guide beginners through AI & GenAI use cases and help them with a strategy and best-in-class solutions;
2. Inspire GenAI-advanced teams with our helicopter view on challenges in their industries and support them in prioritization.

Happy to connect on LinkedIn or discuss on a call if there’s anything we can help you with.

Photo of Krystian Bergmann

More posts by this author

Krystian Bergmann

AI Consulting Lead at Netguru
Thinking about implementing AI?  Discover the best way to introduce AI in your company with AI Primer Workshop  Sign up for AI Primer

We're Netguru!

At Netguru we specialize in designing, building, shipping and scaling beautiful, usable products with blazing-fast efficiency
Let's talk business!

Trusted by: