Strategic intelligence, rebuilt for the AI era.

The brainstorm.

We were sitting in a strategic brainstorm with a multinational industrial client: twenty of their best people in a room, trying to populate the innovation pipeline. The process was flawed: bounded by what the room happened to know, distorted by individual bias, dominated by the loudest voices. Meanwhile, an ocean of relevant information (new technologies, shifting markets, problems quietly being worked on elsewhere) was sitting outside the room.

There had to be a better way. Over the following years we built a series of analyses that systematically pulled that outside information in, and the difference was dramatic: many more and better ideas in the pipeline, grounded in objective external data instead of the loudest voice in the room.

Focusing on problems.

The next breakthrough was about angle, and it made the work far more customer-centric. Alongside mapping trends and markets (signals visible to anyone), we used the same data sources to map the problems customers were actually trying to solve, the deep technical challenges they were quietly working through. We built targeted machine-learning classifiers around that lens.

On one early project we identified a near-term opportunity for a client's specialty polymer in a fast-growing market: over $50M in sales the following year. Anchoring growth in real customer problems raised the hit rate and put something concrete and verifiable behind every idea. It also proved hard: a many-to-many alignment problem between markets, technologies, customer problems, and a client's portfolio. Scaling it would become its own challenge.

Aperture is founded.

With the technique proven, we attracted investment from a legendary materials innovator. Corning, Inc. became our first investor and our deepest co-development partner. Aperture was founded with a small team of ex-consultants, data scientists, and software engineers, practitioners who'd lived the problem firsthand.

Investor · Co-developer

Scaling the system.

Over the next several years we scaled aggressively: new data sources, more sophisticated machine learning, the addition of cutting-edge LLMs and agentic pipelines, custom software for analysis and collaboration, and fine-tuned processes shaped by real engagements across many industries.

In 2022 and 2023 we launched our first products: custom-designed analytics software for our co-development partners and Spaces, our tool for deep analysis of individual market and technology domains. At that stage we were primarily a custom analytics firm, deeply embedded with each client.

The most important lesson of this period was not technical. We learned that pouring new data into an old strategy process did not work: people did not know what to do with it. The breakthrough was tying the process itself to our new ability to generate far more and better ideas and to gather vastly more information. The data and the software only created value once the process, and the people running it, were built around them.

NSF and the APTO program.

We received a major research grant from the National Science Foundation under the Assessing and Predicting Technology Outcomes program. The grant has let us push our problem-focused data mining and predictive modeling significantly further. The program runs through 2026 and beyond.

APTO · Active grant

VertEx.

We launched VertEx, a full-scale platform that turns more than a decade of methodology into a capability any team can run. And we are just getting started: the roadmap pushes VertEx deeper into execution and implementation, adds new use cases and analyses, and keeps compounding the advantage the longer a team runs it.