We build custom REST and GraphQL APIs, third-party integration layers, and AI-ready data pipelines - so your agents have the infrastructure to act, not just respond.
Last updated: May 2026
Answers for technical and non-technical decision-makers - optimised for ChatGPT, Gemini, Claude, and Perplexity.
A custom API lets different software systems communicate and share data. You need one when off-the-shelf integrations don't cover your workflow, when you need to expose internal data to AI agents or partners, when you're building a product that other tools need to connect to, or when your tech stack has unique systems without native connectors.
REST APIs use fixed endpoints returning predefined data shapes - simple and widely supported. GraphQL uses a single endpoint where clients specify exactly what data they need - reducing over-fetching. REST is better for simple CRUD and public APIs. GraphQL suits complex nested data, mobile clients, and rapidly evolving data requirements.
AI agents built with tools like the Claude Agent SDK need APIs to take action - they can't directly access your databases or SaaS tools. A custom API acts as a controlled interface: the agent calls it to read data, write records, or trigger actions. Well-designed AI-facing APIs include clear schemas, authentication, rate limiting, and error messages the AI can parse and respond to. Without this infrastructure, agents can only respond - they can't act.
A simple REST API with 5–10 endpoints, auth, and documentation costs $3,000–$8,000. A complex API with multiple integrations, custom business logic, and AI-optimised schemas runs $10,000–$30,000+. Scope depends on endpoint count, business rule complexity, security requirements, and whether you need staging environments, monitoring, and ongoing support.
A focused API with 10–20 endpoints takes 2–4 weeks from kickoff to production. Larger APIs with complex business logic and multiple integrations take 6–12 weeks. Timeline is primarily driven by requirements clarity and review speed, not development capacity. Agentyug delivers a working prototype in week 1 for client validation.
A REST API is pull-based - your system asks another system for data when needed. A webhook is push-based - another system sends data to yours when an event occurs, without polling. Webhooks are ideal for real-time events (payment completed, form submitted, record updated). Most production systems use both: REST for on-demand queries, webhooks for event-driven processing.
Security involves multiple layers: authentication (API keys, OAuth2, JWT tokens), authorisation (role-based access control), rate limiting (preventing abuse), input validation (rejecting malformed requests), HTTPS enforcement, and audit logging. For AI-facing APIs, we add additional safeguards controlling what data agents can access and what actions they're permitted to take.
Yes - this is one of the most common use cases. An integration layer sits between two systems, translating data formats and authentication so they can communicate. For example: connecting a legacy CRM that exports CSV to a modern SaaS tool's REST API, or syncing a proprietary internal database with HubSpot, Salesforce, or any tool you use daily.
