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AI Product Builder
I build AI prototypes, automation workflows, and service concepts to test whether ideas actually work in real business environments.
My background is in operations, business planning, and data analysis. While working across those roles, I became increasingly drawn to the gap between ambiguous business needs and technical reality, and started building systems myself to close it.
Today, I focus on turning vague requirements into testable PoCs, structured experiments, and clear Go / Drop decisions grounded in real-world constraints.

"Memory is not a log. Memory is compacted meaning."

I am not a core AI researcher working on low-level optimization or deriving algorithms from first principles. My strength is execution, structured experimentation, and product-minded technical thinking.
When I encounter a business bottleneck, I combine existing models, APIs, and algorithms to test whether an idea is actually feasible under real constraints. I build to uncover structural limits, and I use evidence to decide whether to iterate, pivot, scale, or stop.

• Build to Validate: I use prototypes and workflows to test whether an idea can survive real business constraints.
• Production-Aware Thinking: I treat AI systems as services that must work with cost, stability, and operational friction in mind.
• Data-Driven Decisions: If a system does not prove its value through measurable results, I document the failure and move on.
• Learn from Limits: Failed experiments, trade-offs, and dead ends are often the most useful inputs for better system design.

• AI Proof-of-Concepts (PoCs)
• Automation Workflows
• LLM Application Prototyping
• Data Pipelines
• Technical Feasibility Validation
• Product-Oriented Experiment Design

• 🧠 Agent Memory System
  MCP-based long-term memory architecture.
  Architecture: Separated the State of Truth (MongoDB) from the semantic retrieval layer (ChromaDB) to improve consistency and memory compaction.

• 🎯 Automated Brand Logo Extraction
  Zero-shot logo extraction pipeline combining Grounding DINO, SAM, and custom post-processing.
  Focus: Built a robust pipeline that operates without supervised training data.

I value the lessons learned from failed experiments as much as successful deployments. Below are projects where I tested architectural ideas, examined feasibility, and made data-driven decisions.

• 🧪 Q-PSA (Project Killed)
  Tested discrete perturbation for quantized LLMs to estimate layer importance.
  Decision: Killed the project after experiments showed it was ~1300x slower than the baseline and failed pruning validation.

• 🗺️ Circle-WFC (Architectural Pivot)
  Attempted to replace A* pathfinding with a geometry-guided Wave Function Collapse (WFC).
  Insight: Found the structural limit of local consistency in global pathfinding, then reframed the value of the concept into an efficient search space reducer.

• ⚡ HW-WFC v2.9 (Feasibility Validated)
  Constraint-driven AI compiler scheduling R&D.
  Result: Matched Exact DP's optimum, validating algorithmic feasibility, but concluded the research after identifying hardware-backed cost-model calibration as the real production bottleneck.

• 🎓 ADIGA College Admission Data Pipeline
  Extracting and normalizing complex HTML data across 200+ institutions.
  Result so far: Reduced schema violation rates under noisy HTML inputs using hallucination-controlled LLM workflows.

For longer write-ups on troubleshooting, architectural decisions, trade-offs, and project context:
👉 Selected Project Details (PROJECTS.md)

Email: kdtyohan@gmail.com
LinkedIn: entangelk