My Approach
Build only what provides measurable value
Prototype fast, validate early, scale only when proven
Favor simple architectures over over-engineering
Prioritize reliability and maintainability
Optimize for cost/performance balance
• Business goals
• Pain points
• Data availability
• Technical feasibility
How I Solve AI Problems
1. Discovery & Understanding
2. Solution Design
• Architecture planning
• Tool & model selection
• Risk assessment
• Cost/performance trade-offs
3. Execution
• Rapid prototyping
• Iterative development
• Real-world testing
• Deployment
• OpenAI, Anthropic, Claude
• HuggingFace Transformers
• LangChain
• Custom LLM fine-tuning
Capabilities
LLM & NLP
Vision & Deep Learning
• PyTorch
• YOLO-based pipelines
• OpenCV
• TensorRT / ONNX Runtime
Infrastructure & Deployment
• Docker, FastAPI
• AWS
• NVIDIA Jetson/Orin stack
FAQs
-
Most companies don’t need custom training. Off-the-shelf LLMs and lightweight integrations deliver 80–90% of the value at a fraction of the cost and complexity. Custom models only make sense when your data or workflow is truly unique.
-
Any team that spends time on writing, analysis, reporting, research, planning, or repetitive operational tasks can benefit immediately. AI isn’t industry-specific, it’s workflow-specific.
-
AI replaces tasks, not people. The goal is to remove low-value work so your team can focus on creativity, judgment, strategy, and problem-solving; the things humans do best.
-
AI delivers the highest return when it removes repetitive work, accelerates decision-making, or improves a workflow your team already uses every day. The biggest wins come from practical automation, not moonshot ideas.
-
Starting with ambitious ideas instead of real problems. Successful AI adoption begins with understanding your processes, not chasing trends.