AI-copilot mobile interface for delivery vans

Built a Bluetooth-connected mobile app that fuses AI and manual control, enabling van operators to manage robotic systems in real time—handling both continuous sensor streams and bursty high-volume data, all within a secure local network.

AI-first robotics architecture

Designed a modular, parallel-processing pipeline that routes in-van data to specialized AI models for perception (e.g., dimension/text recognition), motion planning, damage detection, packing optimization, and fleet diagnostics—deployable on both local hardware and cloud.

Context-aware AI-guided delivery dialog

Delivered an adaptive UI component that provides real-time, AI-generated guidance to delivery personnel, optimizing package retrieval and handoff with dynamic instruction flows.

Hybrid cloud-local platform with full ops support

Engineered the system for dual deployment (in-van and cloud), with secure processing and resilient offline support—now in pilot phase for rollout with major global delivery fleets.

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AI-powered cold-chain monitoring & alerts

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Deployed a real-time IoT data pipeline that ingests terabytes of temperature, location, and handling events, then uses event-driven AI agents to flag anomalies and push instant alerts—helping prevent the $30 B annual loss of temperature-sensitive goods.

Adaptive temperature-control module

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Designed an on-device AI algorithm that continuously tunes set-points in response to ambient conditions, removing the need for human intervention and ensuring medicines and vaccines stay within safe ranges in the field.

AI-generated compliance auditing

Built a policy-based alert engine and automated, AI-driven audit report generator that produces secure, end-to-end traceability logs—dramatically cutting manual data-gathering time for regulatory reviews.

Secure, scalable IoT + mobile platform

Implemented encrypted network tunnels, Bluetooth/Wi-Fi auto-reconnect mobile apps, and fault-tolerant cloud infrastructure to process tens of terabytes per day, with Plank providing ongoing maintenance and uptime assurance.

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AI-powered procurement research copilot

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Built a domain-specific AI agent system fine-tuned on battery chemistries, vendor catalogs, and robotics specs—enabling procurement teams to explore, compare, and source equipment with precision.

Autonomous vendor workflow automation

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Replaced manual procurement steps (email, fax, RFPs) with a multi-agent system that autonomously handles vendor inquiries, negotiations, and evaluations—learning from prior transactions to improve over time.

Scalable, intelligent data ingestion engine

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Engineered a smart crawling and indexing pipeline to fetch and deduplicate tens of terabytes of equipment data, ensuring a continuously updated knowledge base with minimal manual upkeep.

Dynamic taxonomy generation via topic modeling

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Developed an AI system that classifies equipment and SKUs using semantic embeddings and unsupervised topic modeling—creating a unified schema across diverse product types without hand-labeled categories.

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Multi-agent AI insights engine for financial data

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Built ~10 interoperating graph-based AI agents that automatically ingest, categorize, and analyze transactions from all connected financial institutions, surfacing tailored insights such as spending trends, budgeting nudges, and savings opportunities.

AI-embeddings user memory for personalization

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Developed a persistent vector-based memory system that captures financial behavior and user interactions, enabling agents to infer preferences, habits, and life context—powering deeply personalized financial advice.

AI-curated financial news feed

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Delivered an embeddings-powered indexing and ranking engine that classifies trending news and matches it to individual users' financial interests and holdings—creating a personalized Bloomberg-like dashboard.

Scalable, secure, real-time infrastructure

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Designed a high-throughput system with dynamic autoscaling, secure hybrid deployment (cloud + on-prem), and real-time inference—paired with ongoing operational support and regulatory-grade data protection.

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Real-time AI video-creation engine

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Built a multi-agent graph system that assembles MLS photos, clips, audio, and text into polished listing videos in < 5 s on GPU clusters, then auto-publishes to agents’ social channels.

Smart data ingestion pipeline

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Developed an AI crawler that pulls images and metadata from agent sites, MLS, Zillow, and Redfin, cleans and deduplicates them, and unifies everything into a common schema for seamless editing.

Dynamic, brandable template system

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Delivered an AI-driven template framework letting agents swap fonts, backdrops, marketing copy, and property details to produce highly personalized videos that match their brand style.

Scalable, secure multi-tenant platform & ops

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Handles ~1 M listings with isolated databases for each brokerage, plus ongoing maintenance—already live with Sotheby’s agents.

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AI-powered site selection engine

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Built a multi-dimensional regression tree model trained on US census, cellular, and ordering data to score millions of intersections and recommend optimal store locations—used by major retail and restaurant chains nationwide.

High-performance geospatial visualization

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Delivered an SSE-based web map UI that streams server-rendered tiles and uses an AI model to reduce rendering load by intelligently narrowing the set of relevant intersections based on user input.

Dynamic, scalable modeling infrastructure

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Engineered a concurrent data processing pipeline capable of re-running nationwide analyses in real time as users tweak parameters, handling massive datasets across geography, demographics, and sales history.

Auto-optimized multi-model framework

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Built a model orchestration system using LLM-judging to auto-select the best fit across regression, random forest, and graph-based models—also supporting ensembling for more accurate recommendations.

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Company-wide engineering knowledge search

Built a RAG pipeline that ingests GitHub code, Linear tickets, Slack threads, specs, and more into Pinecone + Postgres, then powers an AI chatbot that answers how-to, debugging, and code-review queries—scaling to 100 TB/day with 1,000 + elastic workers and thousands of live webhooks.

Dynamic chat UI toolkit

‍Delivered a Next.js library that streams LLM responses and auto-renders the right component—plain text, code blocks, flowcharts, architecture diagrams, or performance charts—giving engineers rich, context-aware answers in real time.

Docs-to-roadmap multi-agent system

Converted product requirements into draft design docs and populated Linear boards with ticket hierarchies, then checked pull requests for spec compliance—automating the planning loop from PRD to code.

Secure, self-hostable platform & ops

Packaged the stack as Terraform for one-click AWS deployment, met SOC-2 & HIPAA via least-access controls and Vanta monitoring, and provided ongoing maintenance across multiple enterprise installations.

Secure, open-source AITP SDK & reference agent

Delivered Typescript + Python SDKs that implement the new AITP protocol and a demo e-commerce agent that lets users shop Amazon directly from any MCP-compatible chat UI—built with clean architecture, 100 % test coverage, and penetration-tested for production-grade security.

Terraform-based agent infrastructure automation

Delivered infrastructure-as-code scripts to provision and auto-scale a microservice backend on AWS, including support for Trusted Execution Environments (TEEs)—a complex requirement rarely supported in cloud-native stacks.

AI agent code-generation factory

Created a multi-agent generator that ingests the AITP spec and outputs a fully functional, self-deploying agent codebase, automatically detecting and fixing type, compile, and runtime errors.

Ongoing open-source support & security ops

Handled end-to-end maintenance, community triage, and vulnerability patching for Near’s open-source projects, ensuring long-term sustainability and developer trust.