Real-Time AI Systems for Complex Workflows
William Foland, PhD builds dependable multimodal, vision, and LLM systems fast, from architecture through production delivery.
About
AI systems architect with a PhD in neural language understanding and 20+ years delivering production software across software, silicon, and applied machine learning.
Itinitek specializes in real-time multimodal platforms, vision analytics, and domain-specific LLM systems that streamline complex human workflows. From Harvard's AI teaching simulations to healthcare and industrial automation, the work centers on clarifying the problem, designing the architecture, and closing the execution gap quickly.
Core Expertise
Agentic AI Systems
Multi-agent orchestration for production workflows, including planner-executor-verifier loops, MCP tool use, and stateful versus stateless reasoning strategies.
- Multi-Agent
- MCP
- Verification Loops
- Retry Logic
Evaluation & Reliability
Closed-loop evaluation systems, failure-mode analysis, and iterative tuning for AI products that need to work reliably under real constraints.
- Dataset Construction
- LLM-as-Judge
- Guardrails
- System Tuning
Generative AI & LLMs
RAG pipelines, fine-tuning, prompt engineering, and model routing for domain-specific systems that improve expert workflows instead of adding brittle automation.
- RAG
- MCP Servers
- PEFT / LoRA
- Prompt Engineering
Real-Time Multimodal
Low-latency voice, video, and avatar systems built for human interaction, teaching simulations, and other applications where timing and responsiveness matter.
- WebRTC
- Voice / Speech
- Video Avatars
- Low Latency
Computer Vision
Vision pipelines for industrial and edge workloads, combining detection, OCR, parsing, and multimodal reasoning to recover usable structure from complex visual inputs.
- YOLO
- OCR
- Video Analytics
- Multimodal Reasoning
Infrastructure & Delivery
End-to-end system design spanning model serving, cloud infrastructure, and application delivery across prototypes, pilots, and production systems.
- AWS / Azure
- PyTorch
- TensorFlow
- FastAPI / NextJS
- Python / C++ / JS
Track Record
Founder & Chief Consultant
Itinitek Ltd, Golden, CO
Everyone knows chatbots. We're well past that.
- Designed an ingestion engine that combines specialized AI agents with deterministic engineering checks so extracted data is verified against physical and geometric constraints before landing in the database.
- Built the workflow to route ambiguous cases for fast human review while preserving provenance and validation history on every extracted value.
- Focused the system on the hard parts of industrial drawing extraction: cross-checking tables against drawings, resolving BOM mismatches, stitching continuations across sheets, and recovering reasoning structure from 2D isometric geometry.
- Architected the platform to improve with each project, producing structured data that is auditable, trustworthy, and usable downstream for engineering workflows.
Gen AI Data Scientist
Harvard Business School, Cambridge, MA
- Solo-architected and built a real-time AI meeting simulator where a single LLM controlled four distinct video avatars simultaneously, coordinating speech, timing, and behavior to emulate a live multi-participant discussion.
- Designed a multi-agent coordination layer where a single reasoning model controlled multiple autonomous actors with timing, state, and interaction constraints.
- Engineered a versatile simulation architecture configurable for hundreds of scenarios, enabling rapid creation of new teaching, coaching, and role-play environments without modifying core logic.
- Designed a custom WebRTC + NextJS + FastAPI interface translating model outputs into deterministic multi-avatar actions under tight latency constraints.
- Integrated institutional systems into GPT-5.1 and Claude via tool-using agents and custom MCP servers with controlled action spaces and secure execution boundaries.
- Built a closed-loop evaluation system using synthetic multi-agent dialogue to stress-test coordination, reasoning, and timing, enabling iterative refinement through failure signals.
- Delivered the platform to 900+ MBA students and demo’d it for deans, donors, and faculty as a flagship component of the school’s AI initiative.
- Owned the full lifecycle from prototype to production, including concurrency, evals, guardrails, and streaming reliability for multi-avatar operation.
Gen AI Consultant
IsoGrab.com, Houston, TX
- Architected and built a vision-LLM pipeline that ingests batches of complex isometric piping PDFs—oil & gas, water treatment, industrial facilities—and extracts high-accuracy structured data across widely varying drawing styles.
- Designed a multi-model system combining OCR, layout parsing, and LLM reasoning to recover bill of materials, elevations, insulation details, line identifiers, and component metadata with minimal manual correction.
- Separated perception stages from reasoning stages so vision, OCR, and LLM components could be tuned independently.
- Implemented automated consistency checks and iterative refinement loops that drove error rates down to levels acceptable for safety-critical engineering workflows.
- Delivered a working 4-week proof of concept that reduced diagram-to-data conversion from weeks to hours, leading the client to approve a full production build.
- Engineered the pipeline for robustness across large document sets, non-standard symbol libraries, and heterogeneous drawing conventions.
Chief GenAI Data Scientist
BigRio LLC, Cambridge, MA
- Architected and built an LLM-driven engine embedded directly into clinical workflows (no UI), where GPT-4 analyzed patient data and generated draft care plans, reducing practitioner documentation time and improving consistency.
- Designed the ingestion and reasoning pipeline to handle heterogeneous clinical data—structured fields, free-text notes, historical records—while enforcing domain constraints and alignment with clinical guidelines.
- Implemented guardrails, validation rules, and safety filters to keep outputs reliable in a regulated healthcare setting.
- Delivered a backend service that became part of daily practitioner workflow, accelerating documentation and reducing administrative burden.
- Built an Azure-based RAG PoC for clinical decision support, delivered from concept to pilotable prototype in 8 weeks.
Founder & Chief Consultant
Itinitek Ltd, Golden, CO
- Fine-tuned LLaMA for the legal domain in 3 weeks and built a RAG pipeline that handled 500+ page contract sets.
- Built an AWS pipeline combining LLMs and SQL for real-time options analysis; the proof of concept helped drive a client funding round.
Chief Scientist
Lilac Cloud Inc, Cupertino, CA
- Architected and designed vision-AI pipelines for edge computing, combining Dockerized GPU services with CUDA-accelerated model execution to support real-time video analytics in bandwidth- and latency-constrained environments.
- Built custom FFmpeg filters and Libav extensions to run inference inside the video pipeline, enabling object recognition, event detection, and metadata insertion without interrupting frame flow.
- Implemented GAN-based imperceptible video watermarking and other security features to protect high-value media across distributed streaming environments.
- Delivered low-latency frame processing systems for live sporting events and other real-time workloads, maintaining consistent throughput on varied edge hardware.
- Optimized GPU utilization, memory transfers, and batching strategies to achieve stable real-time performance.
Cofounder & Chief Scientist
Bolt Analytics Corp, Santa Clara, CA
- Architected time-series anomaly detection for financial services using CNN, RNN, and transformer approaches.
- Led an offshore team building automated diabetic retinopathy detection from retina scans using TensorFlow.
Research Scientist
CU Computational Language and Education Research, Boulder, CO
- Developed recurrent NLP models for automated speech recognition and dialog analysis in K-12 STEM education.
- Built system to help teachers reflect on and improve instructional practices through AI-powered feedback.
Expert Technical Consultant
Dovel & Luner, LLP, Santa Monica, CA
- Served as an expert witness in semiconductor patent litigation.
- Contributed technical analysis that supported successful client outcomes.
Founder & Independent Developer
Itinitek Ltd, Golden, CO
- Architected, developed, and marketed six GPS, graphics, and skiing applications for iPhone (Objective-C).
- Worked concurrently with MS and PhD coursework at University of Colorado.
Senior Director, Optical Products
Marvell Semiconductor Inc, Santa Clara, CA
- Led a 150-engineer division developing full SoC solutions spanning ARM core, servo, read channel, and DSP on one chip.
- Managed the complete product lifecycle from architecture through silicon to production.
- Drove multiple generations of mixed-signal optical storage controllers.
Research & Education
Education
PhD in Computer Science
University of Colorado, Boulder, CO (December 2017)
Dissertation: Natural Language Understanding: Deep Learning for Abstract Meaning Representation
Deep learning models similar to those that power ChatGPT and modern LLMs.
MS Computer Science
University of Colorado, Boulder
BS Electrical Engineering
University of Colorado, Boulder
Selected Publications
- Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks, ACL Conference, 2017. State-of-the-art system for extracting semantic meaning graphs from text, improving prior results by 5%+.
- CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks, NAACL Conference, 2016. Competition entry demonstrating a novel neural approach to semantic parsing.
- Dependency-Based Semantic Role Labeling using Convolutional Neural Networks, NAACL Conference, 2015. CNN-based method for extracting semantic roles with state-of-the-art performance.
Foundational Engineering
Deep roots in semiconductor design and complex system architecture provide a unique advantage in optimizing modern AI workloads.
Before pioneering GenAI solutions, I spent years architecting mixed-signal integrated circuits and read channels for data storage. This foundational experience in low-level signal processing, error correction, and hardware constraints directly informs my approach to building efficient, scalable, and robust AI systems today. Understanding the "metal" makes the software faster.
U.S. Patents
19 U.S. patents spanning signal processing, read channel design, mixed-signal integrated circuits, and optical recording systems. Full patent list available on request.
Ready to Accelerate Your AI Strategy?
Available for consulting and development projects.