We build AI that ships.
That is the whole philosophy.
HelixAI LLC is a small, engineer-led practice building AI systems for companies that need them to actually work in production. Not pilots that die in QBRs. Not proof-of-concept demos that look good on a slide deck. Real systems that ship, get used, and hold up under pressure.
The industry is full of firms that sell strategy, workshops, readiness assessments, and frameworks. Those things have their place. But what most companies actually need is an engineer who can sit down, understand the problem, and build the thing. That is what we do.
Software Engineer
AI Systems Architect
The founder
HelixAI was founded by David Smith, a software engineer with over a decade of hands-on experience. David has worked across the stack in Python, JavaScript, TypeScript, and Java, building production systems in enterprise environments where the work has to actually run, not just demo well.
His range covers the full lifecycle: application code, test automation at scale, AI pipeline architecture, multi-agent system design, and consumer-facing product development. The pattern across all of it is the same. Take an idea. Build it end-to-end. Ship it.
What David has built
Five projects spanning original AI research, enterprise automation, and productized services.
ADAI (Artificial Discovery AI)
Original AI research, not API integration. ADAI is a dual-agent architecture designed to catch overconfident or incorrect AI outputs before they reach production. Agent Alpha runs locally on Apple Silicon via MLX. Agent Omega runs in the cloud on Together AI. The two agents communicate through a Redis pub/sub layer, with a Sentinel model trained on 500+ hand-authored examples drawn from historical paradigm shifts in science and technology. The idea: train a model to recognize the pattern of an expert who was wrong and did not know it yet. When one agent delivers an answer, the other evaluates it for overconfidence, logical gaps, and blind spots before the output is trusted. The architecture is a working prototype of what the industry is now calling agentic critic systems, built before most firms were publicly using the term.
QA Automation with AI at Enterprise Scale
Built a full QA automation system using AI assistance that compresses test case generation, execution, and regression analysis from a half-day of manual work into roughly 10 minutes. The system integrates with existing Playwright and Cypress test infrastructure. The compression ratio matters: when a QA team can run in minutes what used to take hours, release velocity changes. This is the kind of work AI consulting should produce but rarely does.
Three-Layer Content Generation Pipeline
Built an automated content generation pipeline using a three-layer architecture: a drafter model generates first-pass content, a voice rewriter trained on brand-specific examples adapts tone, and a critic model evaluates output against quality gates before publishing. Voice match scores progressed from 0.0 to 0.86 across iteration cycles during development. The pipeline runs end-to-end without manual hand-offs. The architectural pattern (drafter plus rewriter plus critic with hard quality gates) is the same pattern now powering parts of the ACO pipeline.
Agentic Commerce Optimization (ACO) Pipeline
A productized service that makes ecommerce products findable in AI-driven shopping surfaces (ChatGPT, Perplexity, Google AI). The pipeline uses a six-dimension signal architecture, a six-gate validation process, and feed submission to open protocols including ACP and UCP. Internal testing on a single product moved agent match rate from 0% to 100% across 50 diverse query patterns.
Focus-First ADHD Productivity App
A task and focus application designed specifically for users with ADHD. Most productivity tools are built for neurotypical workflows and quietly penalize the users who most need help. This app was built the other direction: starting from the actual cognitive patterns of ADHD (executive function challenges, time blindness, context-switching cost) and building the interface around those realities instead of against them. The project reflects the same engineering discipline applied to user experience: understand the real problem first, then build the right tool for it.
Why HelixAI exists
Most AI consulting engagements fail. The industry data is clear. 80% of enterprise AI projects never reach production (RAND). 95% of GenAI pilots at scale fail to deliver measurable ROI (MIT NANDA, 2025). 42% of companies abandoned their AI initiatives in 2025 (Deloitte).
The reason is rarely the model. It is almost always the implementation. Teams get sold on vision decks and buy strategy consulting when what they needed was someone who could actually build. HelixAI exists to fill that gap. We do not sell decks. We ship systems.
RAND Corporation
MIT NANDA, 2025
Deloitte
How we work differently
We build with the failure modes in mind.
We have read the postmortems. Klarna. IBM Watson and MD Anderson. Volkswagen Cariad. McDonald's drive-through AI. Every one of those failures has a named pattern. We build to avoid those patterns, not to pretend they do not apply.
We stay ahead of the curve because we have to.
AI is a moving target. The models change every few months. The protocols change every few weeks. We are small by design, which means we can pivot the stack when a better approach emerges. We do not play catch-up with the technology because we do not have the legacy overhead that forces slower teams to.
We put clients ahead of the game, not alongside it.
When you work with HelixAI, the goal is not to match what your competitors are doing. It is to be the company your competitors are going to be trying to match in 18 months. That requires real engineering, not trend-chasing.
We reject shortcuts.
Shortcuts in AI are expensive. They look cheap at the start and become production outages at the end. We build things properly the first time, with validation gates, graceful degradation, and real observability. That discipline is why our systems still work when other teams' pilots quietly get unplugged.
What we offer
HelixAI runs two offerings. Both are engineer-led. Both are built for clients who care about production outcomes, not pilot demos.
Agentic Commerce Optimization (ACO)
A service for merchants on Shopify, WooCommerce, and BigCommerce. It makes products findable in ChatGPT, Perplexity, Google AI, and other agent-mediated discovery surfaces. Built on a six-dimension signal architecture with six-gate validation. Founding Partner pricing during early access.
Learn about ACO →AI Implementation Consulting
Engineer-led, fixed-scope project work and monthly retainer engagements. For companies whose AI needs go beyond product discovery: custom multi-agent systems, AI integration across existing infrastructure, agent pipeline development, and systems that have to hold up in production. Starting at $5,000 per project. By application only. Three active engagements per quarter.
Request a consulting conversation →Where we work from
HelixAI LLC is registered in Utah and built from Mount Pleasant. The work is remote-first and delivered globally.
Want to talk?
If your AI project is stuck, if your pilot is not making it to production, or if you have an idea that needs an engineer who will actually build it, we are open to a conversation.
david@helixai.media