MODEL LAYER
Foundation reasoning & multimodal intelligence powering structured workflows.
GPT-4oClaude 3GeminiLlamaHuggingFace
Used in multi-agent orchestration, structured RAG, and enterprise automation.
1.5yr+
Years of hands-on experience
Tech Stack
Languages & Runtimes
FastAPIREST APIs
Vector DBs & RAG
ChromaDBRAG PipelinesKnowledge Management SystemsDocument Chunking & Embeddings
Storage
Document Stores
"Great AI isn't about automation alone — it's about designing intelligence that understands people."
— Hansika
Intelligence Architecture
API & INTEGRATION LAYER
Provider abstraction and multi-model switching via unified interfaces.
OpenAI APIAnthropic APIGemini API
Client → API Gateway → Model Provider
DATA & RETRIEVAL LAYER
Contextual grounding using vector search and structured retrieval pipelines.
ChromaDBRAG PipelinesKnowledge Management SystemsDocument Chunking & EmbeddingsDocument Stores
User Query→Embed→Vector Search→Context Injection→Model
AGENT ORCHESTRATION
Stateful, multi-step reasoning systems with graph-based execution.
LangGraphCustom Agent Systems
Planner→Tools→Memory→Executor→Output
BACKEND & SECURITY
Authenticated, access-controlled, production-grade APIs.
JWT AuthenticationAPI Access Control
RBACRate LimitingSecure Token Handling
PERCEPTION & DEEP LEARNING
Vision pipelines and domain-specific model training.
Deep LearningPyMuPDF (PDF reading / thumbnails)Pillow (image processing)pypdf
CNNMultimodal ProcessingEdge Inference
DEPLOYMENT & MONITORING
Scalable deployment with logging, monitoring and CI/CD.
LoggingCI/CDAPI Monitoring
Latency · Error Rate · Throughput · Cost
CLOUD INFRASTRUCTURE
Elastic compute across cloud and serverless environments.
AWSVercel
Selected Projects
1 / 5
AgenticAI Data Labeling Platform
Problem
Data labeling is the bottleneck of modern AI.
Solution
The system uses a Hub-and-Spoke agent architecture.
Impact
Reduced TTM (Time to Market) by 4 months; Surpassed human-crowdsourced accuracy
Click to view details and links
More projects
DhyanaAI – Mental Health Chatbot
Python
Implemented sentiment analysis with spaCy/scikit-learn, React.js frontend, and Flask-based backend. Collected and analyzed user input to classify emotions (happy, sad, angry, stressed). Applied data preprocessing and feature engineering to train ML models for emotion prediction. Generated insights from user interaction data to track mood patterns over time.
Tech
PythonNLPSentiment AnalysisspaCyscikit-learnReact.jsFlask
Deep Dive Case Studies
Narratives of engineering journeys, from architectural decisions to deployment challenges.
Recent Blogs
View AllNo blog posts yet.