AI & Machine Learning Engineering
Custom LLM fine-tuning, RAG vector pipelines, semantic search engines, and autonomous AI agents integrated into your production stack for sub-120ms response times.
Sub-120ms Retrieval & Guardrail Protection
Battle-Tested AI Architecture
Custom RAG Pipelines & Vector Search Engines
Connect your internal documentation and databases to real-time Qdrant & Pinecone vector stores, delivering context-aware LLM answers in milliseconds.
Domain-Specific LLMs
Fine-tuning Llama 3 and Mistral models on enterprise data for domain mastery and cost efficiency.
AI Agent Architectures
LangChain agentic workflows executing multi-step reasoning, tool usage, and database actions.
Private Vector Storage & Data Guardrails
Self-hosted vector infrastructure with strict PII filtering, prompt injection defense, and SOC2 compliance.
How We Build Autonomous AI Engines
Data Structuring & Vector Indexing
Cleaning, chunking, and generating high-dimensional embeddings for enterprise datasets.
RAG Pipeline & Guardrail Setup
Building hybrid keyword-semantic search and safety guardrails to prevent hallucinations.
Fine-Tuning & Model Evaluation
Fine-tuning custom open-weights models and evaluating against domain benchmarks.
Production API & SLA Deployment
Deploying high-throughput FastAPI containers with 24/7 SLA monitoring.
What You Receive With Senbix AI Engineering
Custom RAG Pipelines
Hybrid dense-sparse retrieval combining Qdrant vector search with BM25 keyword matching.
Autonomous AI Agents
Multi-agent frameworks equipped with custom API tools, memory systems, and execution loops.
Vector DB Architecture
High-throughput vector indexing with Qdrant, Pinecone, and Pgvector for instant lookup.
Prompt Injection Defense
Rigorous input sanitization, PII redacting, and guardrails to ensure safe LLM outputs.
Open-Source LLM Hosting
Deploying Llama 3, Mistral, and DeepSeek on dedicated GPU cloud infrastructure.
24/7 Model Monitoring
Continuous tracking of retrieval accuracy, token costs, latency, and drift.
Enterprise AI Knowledge Base Transformation
We deployed a Qdrant RAG pipeline across 500k+ internal legal & technical documents, empowering support teams to retrieve answers in sub-120ms.
Building Production-Grade AI & RAG Vector Systems
Deploying generative AI into production requires far more than basic API wrapper calls. Enterprises demand strict data privacy, sub-120ms vector retrieval, and robust guardrails to eliminate model hallucinations. At Senbix, our AI engineering team builds hybrid RAG (Retrieval-Augmented Generation) architectures using Qdrant and Pinecone vector stores, combining dense vector embeddings with sparse keyword search for pinpoint context retrieval.
Whether fine-tuning domain-specific Llama 3 models or deploying autonomous LangChain multi-agent workflows, we ensure self-hosted containerized execution with complete SOC2 compliance and zero data leaking to third-party providers.
AI Engineering FAQs
Common AI technical questions answered by our machine learning architects.
How do you prevent AI model hallucinations?
We implement strict hybrid RAG vector verification, prompt guardrails, and citation tracing so responses are 100% grounded in your verified corporate data.
Is our proprietary data safe when using your AI systems?
Yes. We deploy self-hosted vector databases and open-weights models (Llama 3, Mistral) within your private AWS/Vercel cloud with zero external data sharing.
What vector databases do you support?
We specialize in Qdrant, Pinecone, Pgvector, and Weaviate for sub-100ms similarity vector search.
Can you build custom autonomous AI agents?
Yes. Using LangChain and LlamaIndex agentic loops, we build AI agents capable of querying databases, executing custom APIs, and automating workflow tasks.
Let's Build Your Next Digital Breakthrough
Direct engineering collaboration from day one. Tell us about your goals and receive a detailed technical roadmap & proposal within 24 hours.