Mason Hendry — AI Solutions Specialist with full-stack development capabilities, and founder of Hyperform Labs. I take real-world workflows and rebuild them as production systems: real data, real users, measurable gains.
Shipped engagements with measured outcomes. Stacks and metrics reflect work actually delivered.
Led the full replacement of a 30-year legacy collections system for a law firm — requirements, data architecture, migration, and org-wide rollout. Designed relational models and RPC-based workflows over deeply nested entity relationships, with row-level security and role-based access for multi-user deployment.
Built a RAG-based support agent for an industrial instrumentation manufacturer: product support, knowledge-base management, and auto-generated RFQ drafts for inside sales. Used Tesseract OCR and NLP to ingest technical documentation into a Qdrant vector store, with retrieval orchestrated in Python and Flowise. Prototyped in LangChain, migrated to Voiceflow for production reliability.
Dual-sided patient/physician dashboard for a 350+ patient clinic — lab-result visualization, AI-assisted physician plan generation, and automated patient communication. Built to accelerate clinical workflow and patient education.
Designed and deployed 20+ generative-AI automations across marketing clients — including RAG systems and agentic workflows, prompt libraries, a per-client meme-generation system, and AI-integrated reporting — with documented adoption playbooks and SOPs.
A working tool, not a slide. This is the kind of diagnostic I run at the start of an engagement — answer five questions and get a scored readiness profile with prioritized next steps.
A documentation-first deployment process built around two loops — validate before scaling, and keep the system improving after launch. The same arc whether the client is a law firm or a marketing agency.
Map the actual workflows, systems, and data with stakeholders. Find where time leaks and where AI gives real leverage versus theater.
Assess the current state of processes, workflows, systems, and data — then identify the core problems worth solving.
Design data models, security, and the smallest system that solves the real problem.
Get a fast, initial test run into real hands to validate the solution's assumptions and gather feedback before committing to scale.
Refine against what real users actually did, not what we assumed they'd do. Loop until the core assumptions hold.
↻ loops back to Pilot
Move beyond the trial scope to production — with a real rollout plan, auth, access control, and migrations. The unglamorous parts that make it stick.
Put observability in place so we know we're moving the right needle, not just shipping. Measured by usage and outcomes, not by launch.
Build the loops that keep RAG systems, AI agents, and workflows from going stale — so the system improves over time instead of decaying.
Playbooks, SOPs, and workshops so the team actually adopts it.
↻ feeds the continuous-improvement loop
Integrate prompts and LLMs directly into current (or new) SOPs to enhance existing workflows with minimal lift — the fastest path to a measurable win.
Document- and context-aware LLMs with tailored system prompts for specific workflows. "Simple" because they reason and respond but don't yet take external actions like a full agent.
Agents embedded in a department or workflow that can access documents, send emails, run analysis, and take real actions — scoped to a specific operational job.
Agents trained on an organization's database or knowledge base that answer questions, pull documents, and take grounded actions with cited sources. Built and run in parallel with the agent tiers above.