This document discusses challenges with API integration and proposes augmented approaches using AI. It notes that API integration takes a long time on average of 700 days due to difficulties understanding documentation, requirements, and ecosystems. Common obstacles include domain modeling, use cases, documentation quality, and access issues. The document advocates improving documentation to explain business and product aspects beyond technical references. It envisions next-gen integration using AI like NLP to help analyze APIs and generate integration code on demand. This could enhance documentation with interactive capabilities and help applications autonomously discover and connect APIs.
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Zdeněk “Z” Němec discusses how AI can enhance API analysis and documentation
1. Zdeněk “Z” Němec, February ‘23
API Documentation & AI
Augmented API docs & API integration
superface.ai
2. Founder & CTO of Superface.ai
• Pioneered the API-
fi
rst approach
at Apiary.io → Oracle
• Founder of API consulting Good
API
• Helped several Fortune 100
companies with their API
strategy and execution
• Founder and CTO of Superface.ai
Zdeněk “Z” Němec
superface.ai
4. APIs are hefty burden
Accelerated by global pandemic, the need for API integrations grew in 80,4% of companies
700 days
Average time needed by a SaaS app to build API integrations
superface.ai
8. Superface research
Top 6 obstacles when integrating APIs
1. Understanding the API domain model and language
2. Answering the question “Can this API ful
fi
ll my use-case?”
3. The need to understand the entire ecosystem and documentation of API
vendor in order to integrate even a small functionality
4. Ambiguity of my own product requirements
5. Quality of documentation, outdated API specs
6. Access hurdles, certi
fi
cation, API provider terms
superface.ai
9. What are we analyzing?
Implemenation
Product
Business
Business rules,
commercials,
limits, regulations,
certi
fi
cation, SLAs
How to connect,
API calls, JSONs,
API Spec
what API o
ff
ers, what capabilities, what use-cases it carters to
superface.ai
10. API documentation today
Technical by the (wrong) default
• Most often, API docs focus on the technical
aspects
• The least important one, and the one that
changes the most often
• API docs are not focusing enough on the
product’s capabilities and domain
• Business side is usually under-documented
Implemenation
Product
Business
superface.ai
13. Minimizing the analysis today?
The tools we have today
• Improving the API documentation
• Document the business and product aspects in addition to technical reference
• SDKs, cURL, Postman collections, examples
• Standardization & harmonization
• Uni
fi
ed APIs
• “APIs in front of APIs”
• Nylas, Hyperswitch, Metapack, Merge
superface.ai
15. Next-gen API analysis
• Analysis will be always needed (for non-commodities)
NLP-based
augmented human operator
Employing LLM/AI to assist
in the proces of analyzing
and integrating API
NLP – Natural Language Processing
LLM - Large Language Model
Self-integrating apps
machine-to-machine communication
Enabling applications to
autonomously discover & connect
APIs
superface.ai