Real systems built for real work.

We build the internal intelligence infrastructure that turns fragmented operational data into faster, better decisions, deployed inside your environment, and yours completely when we're done.

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We work with these platforms
PI HistoriansWellViewEnverusSCADA SnowflakeDatabricksSpotfire
Our mission

To empower organizations to fulfill their mission by expanding what is technologically possible.

Most companies aren't short on AI ideas, they're short on AI that survives contact with real operations. Helix finds the use cases worth doing, fixes the data underneath them, builds the system, and stays until your team runs it every day. Strategy to production, one partner.

We build, not just advise

We assess, design, build, integrate, and support.

Data-first delivery

We fix the root causes that kill most AI projects before building any systems.

Vendor-agnostic

The right model and platform for your problem, never a forced technology stack.

Pilot to production

Start with one focused problem. Prove ROI in weeks. Scale what works.

Industry · Energy

Where the data is hardest and the downtime is most expensive.

We have deep experience within natural gas, building real-time models that predicted and prevented drilling failures. The work below is field-tested, not theoretical.

  • Anomaly detection and failure prediction, rod pump, compressor, and separator issues caught before they cut production.
  • Artificial lift optimization, dyno card analysis and gas lift injection tuning.
  • Spill prevention and HSE analytics, tank-level monitoring, predictive flagging, leading indicators.
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Core domain expertise
Upstreamproduction optimization · artificial lift
Midstreamgathering & compression · pipeline integrity
Background20+ yrs within the industry
Platformswell-versed across SCADA, Historians, Enverus, WellView, Snowflake, Spotfire
Core domain expertise
Cardiovascular & metabolic disease
Vascular & mitochondrial biology
Aging & chronic disease
Translational & preclinical
Industry · Life sciences

Discovery, design, and the software that keeps both reproducible.

Most firms sell target discovery, molecule design, and research software as three separate purchases. We run them as one loop, which is why the handoffs between them don't lose information.

  • Computational biology and target discovery, single-cell, spatial, and multi-omic analysis ending in a ranked target list.
  • AI-enabled protein design, binders generated, variants prioritized, developability assessed in silico.
  • Scientific AI and software, evidence graphs, human-in-the-loop review, provenance on every output.
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How we do it

Is your data ready for AI?

Every engagement climbs the same five layers, each one built before the one above it. Skipping a layer is why most AI projects fail, not model quality. It's why the foundation gets built first, and why what we hand off holds up in a decision and in an audit.

When we're done, the system runs in your environment, documented, with your team trained on it.

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05 AI enablement
04 Data intelligence
03 Data governance
02 Data infrastructure
01 Data capture

Posts from the team.

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The companies that win the next decade will not be the ones that used the most AI. They will be the ones that built the best internal intelligence systems, and did it early enough to compound the advantage. In 2026, that is still available. That is why we build now.

Miles MansCEO & Co-Founder
The next step

We'd rather talk about your data than sell you anything.

A thirty minute collaboration that starts with you telling us what you're trying to answer, and we'll tell you honestly how we can help.

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