Build intelligent conversations with multi-provider LLM access. OpenAI GPT-4, Google Gemini, Anthropic Claude through one unified API. Streaming responses, function calling, context memory for voice and chat agents.
Trusted by businesses worldwide
LLM Providers
Single unified API
TTFT
With Gemini 2.5
Context Window
With Gemini models
Uptime
Multi-provider reliability
From user input to intelligent response
User message or transcribed speech
LLM understands intent & context
Function calls & data retrieval
Streaming response with actions
Everything for intelligent conversations
GPT-4, Gemini, Claude access
Token-by-token responses
Connect to your APIs
Managed conversation history
100+ languages supported
Auto-failover to backup
Versioned system prompts
Cost & performance tracking
Choose the right LLM for your use case
| Feature | GPT-4o | Gemini 2.0 | Claude 3 | GPT-4o-mini |
|---|---|---|---|---|
| Response Quality | Excellent | Very Good | Excellent | Good |
| Latency (TTFT) | ~500ms | ~200ms | ~400ms | ~200ms |
| Hindi Quality | Very Good | Excellent | Good | Good |
| Price/1M tokens | $5.00 | $0.075 | $3.00 | $0.15 |
| Context Window | 128K | 1M | 200K | 128K |
| Function Calling | Yes | Yes | Yes | Yes |
Power any conversational experience
Voice Agents
AI phone assistants
IVR Modernization
Replace rigid menus
Voice Commerce
Shopping by voice
Voice Search
Natural language queries
Customer Support
24/7 AI support bots
Sales Assistants
Lead qualification
Internal Copilots
Employee AI assistants
Knowledge Bases
RAG-powered Q&A
OpenAI-compatible API with multi-provider support
// Conversational AI example (Node.js)
import { EdesyAI } from '@edesy/ai';
const ai = new EdesyAI({ apiKey: 'your-api-key' });
// Chat completion with function calling
const response = await ai.chat({
provider: 'gemini', // or 'openai', 'claude', 'azure'
model: 'gemini-2.0-flash',
messages: [
{ role: 'system', content: 'You are a helpful order status assistant.' },
{ role: 'user', content: 'Where is my order #12345?' }
],
functions: [
{
name: 'get_order_status',
description: 'Get order status from database',
parameters: { order_id: { type: 'string' } }
}
],
stream: true // Enable streaming
});
// Handle streaming response
for await (const chunk of response) {
if (chunk.function_call) {
// Execute function and continue conversation
const result = await executeFunction(chunk.function_call);
} else {
process.stdout.write(chunk.content);
}
}From signup to intelligent conversations
Sign up and get credentials
Select LLM based on needs
Set system prompt & functions
Deploy with auto-scaling
Pay per token. Use any provider without separate accounts.
Everything about Conversational AI API
A conversational AI API provides access to large language models (LLMs) for building intelligent chatbots and voice agents. It enables natural language understanding, context-aware responses, multi-turn conversations, and integration with business systems through function calling. Our API unifies access to multiple LLM providers.
We support 5+ LLM providers: OpenAI (GPT-4, GPT-4o, GPT-4o-mini), Google Gemini (2.0 Flash, 2.5 Flash-Lite, Gemini Pro), Anthropic Claude (Claude 3 Opus, Sonnet, Haiku), and Azure OpenAI. You can switch providers per-conversation or use fallback chains for reliability.
For voice agents, latency is critical. We recommend: Gemini 2.5 Flash-Lite (fastest, ~100ms), GPT-4o-mini (fast, good reasoning), or Claude 3 Haiku (fast, excellent instruction following). For complex reasoning, use GPT-4o or Claude 3 Opus but expect higher latency.
Function calling allows the LLM to invoke external APIs and business logic during conversation. For example, a voice agent can check order status, book appointments, or query databases. You define available functions, and the LLM decides when to call them based on user intent.
Our API manages conversation history automatically. You can configure memory window (last N messages), summarization (compress long conversations), and persistent storage (Redis/database). For voice agents, we optimize for token efficiency while maintaining context.
Streaming delivers the LLM response token-by-token instead of waiting for completion. This reduces perceived latency - users see/hear responses immediately. Essential for voice agents where the TTS can start speaking as soon as the first tokens arrive.
All supported LLMs handle multilingual conversations natively. For Indian languages, Gemini performs best with Hindi, Tamil, Telugu. GPT-4 provides excellent quality across languages. You can set preferred language per-conversation and enable auto-detection.
Cost depends on conversation length and model. Typical 5-minute voice call (~1,000 tokens): GPT-4o-mini ~$0.0015, Gemini Flash ~$0.0005, Claude Haiku ~$0.001. We provide detailed usage analytics and cost allocation per-conversation and per-agent.
Yes, several providers support fine-tuning: OpenAI (GPT-4o-mini, GPT-4o), Azure OpenAI (custom deployments). For most use cases, prompt engineering with good system prompts achieves similar results without fine-tuning costs. We provide prompt optimization guidance.
We implement: automatic fallback to backup providers, retry logic with exponential backoff, request caching for identical prompts, and circuit breakers for failing providers. Enterprise plans include 99.9% SLA with credits for downtime.
Get your API key and start building with GPT-4, Gemini, and Claude.
Every business is unique. Let's discuss your specific needs and create a pricing plan that works for you.
Custom pricing based on your needs
No hidden fees or surprises
Flexible payment options
Volume discounts available
Free consultation & demo
30-day money-back guarantee
Our team will get back to you within 24 hours with a personalized pricing proposal
Or reach out directly:
Trusted by businesses worldwide