AI Lab & Applied AI

RAG Systems and Agent Networks

We equip AI with corporate context and action capabilities. Autonomous agents that solve real problems.

• Advanced RAG (Retrieval-Augmented Generation) architectures
• Multi-role agent orchestration with LangChain/LlamaIndex
• Secure integration with databases, APIs, and internal systems
• Implementation of strict guardrails and output validation
• LLM Observability: traceability of prompts, costs, and latency

Business Impact: Intelligent automation of complex workflows, decisions based on private data, and absolute mitigation of hallucinations.

Fine-Tuning and Local Models

We train open-source models with your specific knowledge. Maximum privacy, lower latency, and zero dependency on external APIs.

• Curation, cleaning, and preparation of synthetic/real datasets
• Efficient fine-tuning (LoRA/QLoRA) of base models (Llama, Mistral)
• Systematic quality evaluation and custom benchmarks
• Quantization and optimization for high-performance inference
• LLM deployment on your own infrastructure (On-Premise/VPC)

Business Impact: Total data sovereignty, highly specialized models in your domain, and drastic reduction of inference costs at scale.

Ultra-Low Latency Voice AI

Conversational voice assistants indistinguishable from humans. Native telephony integration for call center automation.

• Bidirectional streaming architecture (Speech-to-Text/Text-to-Speech)
• Direct integration with SIP trunks and PBX platforms
• Advanced interruption handling (barge-in) and sub-500ms latency
• Multi-tenant architecture for B2B deployments at scale
• Sentiment analytics, transcription, and insight extraction

Business Impact: Automation of repetitive interactions, predictable concurrent capacity, and measurable conversations that improve with real data.

LLMOps, Evaluation, and Governance

We create the control system an AI solution needs to evolve without losing quality, traceability, or budget discipline.

• Versioning for prompts, models, datasets, and configurations
• Automated and human evaluations tied to business criteria
• Observability across quality, latency, tokens, cost, and errors
• Guardrails, red teaming, and sensitive data protection
• Progressive deployments, rollback, and model comparison

Business Impact: Controlled model changes, measurable quality by version, and evidence-based evolution decisions.

AI Integrations and MCP

We connect models and agents to the tools where work already happens, applying permissions and auditability to every action.

• MCP servers and connectors for APIs or internal systems
• Integrations with CRM, support, documentation, and operations
• Permissions by tool, action, user, and organization
• Human approval for sensitive operations
• Complete traceability across calls, decisions, and outcomes

Business Impact: AI integrated into real workflows, able to act within clear boundaries without creating another operational black box.