Autonomous AI Agents & Enterprise Machine Learning

Deploy enterprise-grade autonomous AI agents, fine-tuned LLMs, RAG knowledge bases, and custom machine learning pipelines nationwide across all 50 US states.

Core Capabilities & Engineering Deliverables

• Multi-Agent Workflow Orchestration

  Autonomous AI agent networks utilizing LangGraph, CrewAI, and AutoGen that plan, execute, and self-correct multi-step enterprise workflows.

• Enterprise RAG & Vector Search

  Zero-data-leakage semantic search over internal knowledge bases using Pinecone, Qdrant, and pgvector with strict tenant isolation.

• Custom LLM Fine-Tuning & Prompts

  Domain-adapted model tuning with open-source (Llama 3, Mistral) and proprietary LLMs (OpenAI, Anthropic Claude, Google Gemini).

• Real-Time Conversational Voice AI

  Sub-second latency voice pipelines with ElevenLabs, Deepgram, and Twilio for automated customer support and qualification.

• Enterprise AI Governance & Safety

  PII redaction, hallucination evaluation, automated content safety filters, and audit trail logging.

Commercial Delivery Standards & Assurance

• Custom Autonomous Multi-Agent Workflows
• Private Enterprise Vector Database Integration
• Sub-Second Response Latency Optimization
• SOC2 / HIPAA / GDPR Compliance Considerations & Guardrails
• Full Model Weights, Prompts & Pipeline IP Handover

Frequently Asked Questions

How does Blunt Soft ensure our proprietary company data remains private when using AI?

We implement zero-data-retention API policies, on-premise or private cloud vector databases (Qdrant/pgvector), and self-hosted open-source models (Llama 3) that never expose sensitive business intelligence to public model training.

What is the difference between simple chatbots and autonomous AI agents?

Autonomous AI agents don't just chat—they execute actions. They browse live data, interact with databases, trigger APIs, run code, and autonomously collaborate across multi-agent workflows to complete complex business tasks without human intervention.

What frameworks do you use for AI agent and RAG engineering?

We engineer solutions using Python, LangChain, LangGraph, LlamaIndex, CrewAI, Pinecone, Qdrant, Hugging Face, and API orchestrations across OpenAI, Claude, and Gemini.