Practical AI & RAG Solutions for Enterprise Data
We engineer domain-specific AI solutions that augment human decision-making and extract structured value from unstructured enterprise data. Grounded in your authenticated knowledge bases with zero hallucinations.
What We Build
Custom-engineered digital solutions tailored to high-scale production requirements and complex enterprise workflows.
- Enterprise Retrieval-Augmented Generation (RAG) Systems
- Intelligent Semantic & Hybrid Search Engines
- Multi-Modal Document Analysis & OCR Pipelines
- Custom AI Assistants for Internal Knowledge Bases
- Automated Data Extraction & Classification Pipelines
Key Capabilities & Standards
Rigorous engineering benchmarks, strict concurrency validation, and automated compliance pipelines integrated into every release.
- Dense & Sparse Hybrid Vector Search (BM25 + Embeddings)
- Contextual Chunking & Metadata Filtering
- Strict Grounding & Hallucination Prevention Guardrails
- Fine-Tuned Specialized Embeddings & Re-Ranking Models
- Enterprise Security, Encryption & Access-Controlled Retrieval
Our Practical AI Solutions & RAG Systems
A structured, milestone-driven delivery framework ensuring predictable velocity, code quality, and verifiable production outcomes.
Document Ingestion
Extract text from PDFs, wikis, and database records.
Phase Deliverables Matrix
- Ingestion PipelineVerified
- Parser SpecificationsVerified
Our Development Approach
Data Audit & Ingestion Architecture
Analyze enterprise knowledge bases, file formats, permissions, and chunking strategies.
Embedding & Vector Pipeline Implementation
Build automated ingestion pipelines that chunk, embed, and index enterprise documents.
Evaluation & Hallucination Benchmarks
Test question-answering accuracy against verified test datasets with automated precision scoring.
Secure Production Integration
Deploy access-controlled API endpoints and interactive chat/search user interfaces.
Real-World Use Cases & Outcomes
Internal Engineering & Policy Knowledge Assistant
Indexed 10,000+ internal Notion docs, GitHub wikis, and API specs into a unified RAG system.
Reduced internal developer question triage by 70% with instant cited answers.
Intelligent Medical Record Summary Tool
Engineered secure semantic search over unstructured clinical notes and patient records.
Reduced physician chart review time from 20 minutes to under 2 minutes per patient.
Frequently Asked Questions
How does AIVON prevent AI from giving incorrect or fabricated information?
We use RAG architectures where the LLM is restricted to answering ONLY using the retrieved context snippets. If the required information is missing from your documentation, the system responds that it does not know rather than guessing.
Can we run vector search inside our existing PostgreSQL database?
Yes! Using the pgvector extension, we can add high-performance semantic search directly inside your existing PostgreSQL database without adding extra vector database infrastructure.
Grounded Artificial Intelligence
We don't build generic AI gimmicks. We engineer deterministic, citation-backed intelligence systems that turn messy corporate documentation into an instant, searchable knowledge engine.
Technology Stack
Why AIVON
- Zero hallucinations: Every answer provides clickable citations to the exact source paragraph.
- Enterprise data privacy: Strict VPC deployment with zero third-party model training.
- Hybrid search: Combines keyword exact matching with semantic intent for 100% recall.
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