AI Systems Integration
LLM workflows, model-backed internal tools, data pipelines, APIs, human review loops, and automation that fits the way the team already works.
AI Systems Engineering | ML Integration | Document Intelligence
Practical AI systems engineering for messy, high-value operations.
I help teams in logistics, finance, manufacturing, and adjacent operations design, integrate, and evaluate machine learning systems. That can mean LLM workflows, internal AI tools, model evaluation, data pipelines, automation, or document intelligence. Document understanding is a specialty, not the boundary of the work.
PhDRobust AI research with document understanding depth
10+Years across data, engineering, and applied machine learning
ProvoUtah-based and available for focused remote or local engagements
Services
Focused help for AI systems that need to work.
Evaluation and Automation Audits
A practical review of where AI can reduce manual work, where it will fail, and what data, process, and quality gates are missing.
Document Intelligence
Extraction, classification, OCR evaluation, handwriting recognition strategy, and layout-aware pipelines when documents are the hardest part of the system.
Good Fit
Useful when AI has to survive real operations.
You need more than a demo.
The useful version has to connect to existing data, tools, permissions, review steps, and business constraints.
Your data is messier than the vendor pitch.
Documents, logs, labels, databases, images, and operational edge cases decide whether automation succeeds.
You need sober AI guidance.
You want a technical partner who can separate useful AI from risky hype and define an evaluation plan before spending heavily.
Credentials
Research-backed, implementation-minded.
Start
Request an AI systems review.
Email a short description of the workflow, data sources, current tools, bottleneck, and what success would mean for the team. If documents are involved, include the document types and sample volume.
taylornarchibald@gmail.com