Chapter 1 — Overview
Introduction
OpenBrain is a B2B platform for building language-specific voice digital assistants (VoiceBots) on top of existing telephony infrastructure — combining NLU/NLP pipelines, knowledge representation, speech recognition and speech synthesis in one framework.
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Overview of the framework
The framework integrates advanced natural language understanding and processing models and pipelines, knowledge representation mechanisms, and models for speech recognition (ASR) and speech synthesis (TTS) to support intelligent voice assistants tailored to different languages and industries.
By providing specialised workflows and tools, OpenBrain accelerates the development of digital voice assistants and streamlines their adaptation to new languages. Its AI models are designed to learn, adapt and repurpose natural language text and speech representations for speech recognition, speech synthesis and semantic analysis. A key differentiator is support for continuous offline and online learning: system maintainers refine and enhance models through interfaces built for ongoing improvement.
Legacy Interactive Voice Response (IVR) systems can be transformed into AI-powered VoiceBots without replacing existing PBX infrastructure. Organisations retain their phone numbers, routing logic and telephony environment, while rigid menu trees evolve into speech-driven conversational agents capable of understanding intent, resolving issues autonomously and delivering services.
BlockFlow
BlockFlow is the block-based scenario builder at the core of the platform. It orchestrates LLM-driven agents under structured human supervision, so conversational behaviour stays transparent, predictable and aligned with business objectives. Each block carries embedded intelligence — enhanced by an LLM or enriched with retrieved knowledge — to accomplish its specific function. See Designer Editor .
Key features
Thirteen capabilities define what the platform provides out of the box.
1. Integration with call centres and IVRs
OpenBrain integrates with existing PBX infrastructure, replacing rigid menu-driven call flows and static prompts with dynamic, context-aware speech interactions. Callers speak freely in their own words while the system understands intent and responds in real time — without replacing the telephony environment.
2. High-performance optimised engine
The processing engine is engineered for efficiency across CPU and GPU environments and delivers high throughput for concurrent voice or text interactions. On a standard server with a single NVIDIA A10 GPU, OpenBrain reliably handles more than ten concurrent calls.
3. Block-based development of use cases
Domain-specific scenarios are created through graphical block-based units with flexible routing between blocks. Blocks fall into four categories:
- Input nodes — capture and interpret user input.
- Output nodes — deliver information or responses to the user.
- Processing nodes — analyse, transform or interpret user-provided information.
- Routing nodes — manage logical flow and orchestration between blocks.
4. Voice communication — custom-built STT and TTS
The ASR engine is optimised for precision, multilingual support, accent adaptability and configurable performance in challenging acoustic environments. The TTS system produces fluid, context-aware and brand-customisable speech with natural prosody and tone.
5. Integration with Large Language Models
Both open-source and proprietary LLMs are supported, including on-premise models tailored to particular languages and domains. Built-in procedures cover fine-tuning and customisation, and the framework integrates with multiple virtual environments for inference, serving and scalability. See LLM Management Registry .
6. Knowledge base integration
Retrieval-Augmented Generation strategies — GraphRAG, VectorRAG and HybridRAG — combine structured knowledge graphs with high-dimensional semantic search. The platform also supports the Model Context Protocol (MCP) for standardised, governed integration between LLMs and external tools or enterprise systems. Page-level indexing and long-document processing allow pinpoint answers inside large repositories of policies, technical manuals and documentation.
7. Barge-in support
Users can interrupt or redirect ongoing system responses in real time. The system detects the interruption, processes the new input immediately and adapts the dialogue flow without unnecessary latency or repetition.
8. Automatic outbound calls
Outbound campaigns and their conversational scenarios are designed in BlockFlow, enabling automated notifications, reminders, surveys, appointment confirmations and service updates through natural dialogue.
9. Domain-adaptive AI with human-in-the-loop operation
An iterative improvement cycle enhances the LLM, STT and TTS components over time, while domain experts, administrators and QA teams review and correct AI output:
- STT adapts to domain terminology, regional dialects and accents; reviewers correct recognition errors in the transcription editor, and those corrections refine future model performance.
- TTS improves through phonetic management tools that adjust pronunciation rules, accentuation and phoneme-level corrections without full retraining. Brand voice customisation is supported through voice cloning pipelines.
- LLMs align with organisational knowledge, policies and workflows as administrators identify conversational gaps and refine prompts, tools or knowledge sources.
10. Call supervision, KPIs and transcript management
Every voice interaction becomes structured, searchable conversation intelligence. Calls are recorded, classified and transcribed in real time or post-call, enriched with timestamps, speaker separation, intent detection and contextual tags. This enables conversational traceability and rapid debugging, quality assurance and agent supervision, regulatory compliance and audit readiness, and supervised fine-tuning from real-world data.
11. Metrics visualisation and conversational analytics
The analytics environment covers call volume and interaction trends, speech system performance metrics (STT and TTS accuracy, NLU response generation speed, latency, quality indicators) and customisable reports and business KPIs such as resolution and conversation success rates. See Analytics Dashboard .
12. Intelligent health monitoring and operational resilience
- Continuous monitoring of containerised services — resource usage, uptime and failure states.
- Automatic service recovery through automated restart of crashed or unresponsive services.
- Manual service control for individual services or the whole system.
- Real-time logs and debugging via an integrated log preview.
13. Scalable, cloud-native and on-premise architecture
A modular, distributed microservices architecture scales horizontally, and can be deployed in Kubernetes-orchestrated environments for automated container management, dynamic scaling, load balancing and self-healing. Full on-premise deployment on dedicated servers or GPU workstations is supported for organisations with strict data governance, privacy or latency requirements.
Partners
OpenBrain is partnered with:
