Engineering Intelligence.
From research to production. Building scalable, reliable, and observable AI systems that solve real-world problems.
Enterprise RAG Systems
Hybrid search, reranking, and citation-backed generation at scale.
Autonomous Agents
Self-directing agents with planning, tool usage, and multi-step reasoning.
LLM Ops & Evaluation
Model observability, latency tracking, and automated evaluation.
Multimodal Integration
Fusing text, vision, and audio models for comprehensive understanding.
Vector Architecture
Scalable vector search with Qdrant, Pinecone — sub-millisecond latency.
Fine-Tuning
Custom domain models with LoRA/QLoRA on Llama 3, Mistral.
Edge AI
Local inference with ONNX and quantized formats for privacy-first apps.
AI Agent Decision Flow
Watch an autonomous agent break down a complex task, call tools, reason, and deliver results.
Semantic Search Engine
Client-side vector similarity search — type a query and see results ranked by meaning, not keywords.
Runs in your browser. Production systems use vector databases and embedding models.
Multimodal Pipeline
Drop a document to see the processing pipeline in action
Receiving raw input
OCR / Transcription / Extraction
Converting to vector space
Storing in vector DB
Query and retrieve results
RAG Engine for Internal Knowledge Base
B2B SaaS Company · 6-week engagement
A mid-size SaaS company had over 15,000 internal documents spread across Confluence, Notion, and Google Drive. Their support team spent an average of 12 minutes per ticket just searching for relevant information.
We built a retrieval-augmented generation pipeline that ingests documents from all three sources, chunks them based on semantic boundaries, and indexes them into a Qdrant vector store. Hybrid search (dense + BM25 sparse) with cross-encoder reranking surfaces the most relevant passages.
The conversational interface embedded in their support dashboard delivers cited answers in under 200ms. Every response includes source links for verification.