Miako is an open-source, production-grade AI backend designed to bridge the gap between rigid, frustrating chatbots and fluid, human-like conversation. Built on an async-native FastAPI architecture, Miako is engineered to handle thousands of concurrent users with sub-second latency, providing a seamless and intelligent customer service experience.
Our vision is to redefine automated customer service by creating an AI that is not only fast and scalable but also contextually aware, culturally fluent, and fundamentally reliable. We believe that the future of BPO automation lies in deterministic, goal-oriented systems that understand users on their own terms—from complex "Taglish" code-switching to the nuances of Gen-Z slang.
Miako is being built with a few uncompromising principles in mind:
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Performance Above All: To deliver a superior user experience, every response must be fast. By leveraging Groq's LPU and a highly optimized async workflow, Miako aims for sub-second responses, eliminating the awkward pauses that plague traditional AI assistants.
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Deterministic Workflow over Agentic Freedom: While AI agents are powerful, they can be unpredictable. Miako operates on a deterministic CrewAI flow. This means every user query follows a logical, pre-defined path of analysis (Language → Intent → Action). This ensures reliability, prevents unexpected behavior, and makes the system safe for real-world business logic.
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Cost-Efficiency by Design: We architect Miako to be powerful yet affordable. Instead of relying on a single, massive reasoning model for every task, we use smaller, faster, and more cost-effective models for specialized jobs like language identification and intent classification. The right tool for the right job.
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The Safety Net: Proactive Clarification: We are architecting a crucial fallback mechanism. When the workflow is uncertain or lacks context, it won't guess and risk an error (like deleting a database). Instead, it will trigger a specialized "clarification" LLM. This LLM will act like a curious child, asking the user simple, targeted questions to remove ambiguity before proceeding. This makes the system safer, smarter, and more collaborative.
To understand how Miako handles each message, here is a step-by-step breakdown of her internal decision-making process.
The first step is to triage the incoming message, checking its language and ensuring it's safe to process.
Once the language is understood, Miako's core brain trust determines what the user actually wants to do.
Finally, Miako gathers all context from short-term memory (and long-term RAG if needed) to craft a coherent, intelligent, and personalized response.
- Asynchronous-First Architecture: Built with FastAPI to handle high I/O and thousands of simultaneous connections without blocking.
- CrewAI Flow Orchestration: A deterministic, multi-step workflow ensures predictable and reliable query processing.
- Advanced RAG with Hybrid Search: Combines dense vector search (Cohere) and sparse keyword search (BM25) via Zilliz Cloud for unparalleled retrieval accuracy.
- Custom Concurrency Control: A bespoke locking mechanism (
LockManager) guarantees strict user-state isolation, preventing "context bleeding" and race conditions. - "Glass Box" Retrieval Security: Granular control over the context fed to the LLM prevents prompt injection and data leakage.
- Multi-Language & Slang Understanding: Specifically designed to translate and comprehend code-switching (Taglish) and modern vernacular.