Enterprise AI &
RAG Systems
Connect private enterprise knowledge bases directly to LLM architectures. We build highly grounded, secure, and rapid Retrieval-Augmented Generation solutions with zero hallucination gates.
AI Core Engineering & Systems
From semantic vector databases to agentic workflows, we construct high-throughput intelligence layers for modern products.
Vector Database Architectures
Implementing production-scale similarity search models. We specialize in pgvector, Pinecone, Qdrant, and Milvus, designing HNSW and IVFFlat indices optimized for sub-50ms latency.
- High-Dimension HNSW Indexing
- pgvector Schema Orchestrations
- Metadata Filtering Pipelines
Data Ingestion & Extraction
Automated pipelines that ingest and clean enterprise documents. We extract clean text from complex tables, PDFs, Notion wikis, and Confluence pages, with semantic metadata tagging.
- Unstructured PDF Parser Nodes
- Semantic Document Partitioners
- Automatic Metadata Enrichments
Agentic AI Workflows
Designing autonomous multi-agent systems using LangChain, LlamaIndex, and CrewAI. We build self-correcting prompt chains with tool execution loops and dynamic intent routing.
- Multi-Agent Orchestrations
- Autonomous Tool Calling Bridges
- Self-Reflection Retry Pipelines
Model Optimization & Tuning
Integrating proprietary LLMs (GPT-4o, Claude 3.5) and hosting open-weight models (Llama 3, Mistral). We compile structured outputs using JSON schemas and run inference scaling.
- vLLM Self-Hosted Inference
- Structured Output Schema Enforcement
- Prompt Context Optimization
Observability & Evaluation
Continuous evaluation metrics tracking retrieval faithfulness, answer relevance, and context precision. We integrate LangSmith, Ragas, and Arize Phoenix to keep systems grounded.
- Ragas Faithfulness Benchmarking
- LangSmith Tracing Dashboards
- Cost & Token Tracking Ingress
VPC Security & Compliance
Ensuring secure enterprise-grade data isolation. We build isolated private network endpoints, set up zero-data-retention APIs, and meet SOC2, HIPAA, and GDPR compliance standards.
- AWS/GCP Private Link Endpoints
- Zero Data Retention Contracts
- PII Redaction Ingestion Gates
Local On-Premises Hosting vs Public API Integration
We architect solutions tailored to your security rules. We run private LLMs locally on AWS/GCP nodes with GPU acceleration, or link securely to global commercial endpoints with masking.
Self-Hosted Model Nodes
We deploy quantized Llama 3 or Mistral pipelines directly in your VPC. Bypassing public API endpoints entirely, your business-critical documents and user queries remain strictly on-premises under SOC2 firewall compliance.
Mobile & Web Native Models
For systems operating on mobile (iOS CoreML or Android NNAPI), we deploy hybrid architectures. Small edge models process classification tasks locally, while complex vector context requests route to server-side RAG pipelines.
Anonymized Cloud Ingress
Where hyper-capable models like GPT-4o are needed, we write ingestion proxies. PII (Personally Identifiable Information) data filters strip sensitive records before context compilation, forwarding sanitized vectors to LLM APIs.
AI Implementation Roadmap
Our audited progression path from raw data storage to grounded vector generation.
Knowledge Mapping & Audit
We inventory your organization's unstructured data assets: Notion workspaces, internal wikis, PDFs, and databases. We map user access control rules (ACLs) to ensure the AI never retrieves data a user isn't authorized to view.
- Security Access Control Plan
- Document Source Catalog
- Chunking Strategy Definition
AI & RAG Playground Sandbox
Inspect vector document clusters, configure parsing overlap boundaries, and run retrieval benchmark tests.
Semantic Chunking Inspector
Adjust text partitioning boundaries. Sliders control chunk size and chunk sentence overlap dynamically. See how paragraphs color-code into discrete vectors to feed vector database queries.
Veloxis Web Studios designs intelligence architectures that ground generative AI models. We construct ingestion channels that partition text documents, PDFs, and code repositories using recursive character chunking rules. By defining an optimized chunk
an optimized chunk size, we capture cohesive concepts. Configuring a targeted chunk overlap ensures that critical sentences spanning boundaries are not split in half, preserving semantic context across adjoining vectors. Finally, documents are indexed
documents are indexed on a pgvector instance utilizing HNSW similarity trees.
AI & RAG Frequently Asked Questions
Review details about indexing architectures, compliance, and system safety measures.
Build Intelligence Directly Into Your Workflow
Get in touch to construct a secure, low-latency agentic network or grounded RAG database built around your company records.