> For the complete documentation index, see [llms.txt](https://docs.reo.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.reo.dev/agents/tech-projects.md). # Tech Projects > **A Tech Project is a major initiative a company is building or investing in** to solve a business problem. It's time-bound, involves significant engineering bandwidth, and t**ells you exactly what that company is prioritizing right now.** Some of the most common ones look like this: * Optimizing AI/GPU spend * Building an AI-native product * General cost optimization * Cloud-to-on-prem migration * Migrating to a modern data platform, like a new warehouse or lakehouse * Pursuing a compliance certification (SOC 2, HIPAA, FedRAMP) to unlock bigger deals * Replatforming a legacy system instead of continuing to patch around it Once a tech project is underway, it changes how that company actually operates, and that change is visible, if you know where to look. ***For example:*** * Optimizing GPU/AI spend is a popular tech project * If a company is genuinely working on this, it shows up in how much they're actually spending on GPU infrastructure * That specific, checkable read on GPU spend is what we call a Tech Indicator {% hint style="success" %} Tech Project is the initiative, while Tech Indicator is the signal that tells you it's happening {% endhint %} > A Tech Indicator can help you uncover more than one tech project. It **tells you something specific about how a company actually builds or invests in its technology.** It goes beyond the company's tech stack to show you the operating reality behind it. That can look like: * Serving AI inference at real scale, not a small pilot * Spending heavily on GPUs, the kind of cost that shows up in the budget * Running on-prem instead of in the cloud * Operating in a highly regulated industry, like healthcare, finance, or defense * Managing data at petabyte or exabyte scale, far beyond what most standard tools were built for * Running on a single cloud provider instead of going multi-cloud **None of this shows up in a standard tech stack lookup, or in firmographic data like funding and headcount.** But every one of these changes how you'd sell to that company, price for it, or prioritize it in your territory. {% hint style="info" icon="lightbulb" %} ***That's exactly where tech indicators will help you:*** \ \ To expand what you can know about an account beyond its individual technologies, and surface the bigger technical initiatives already happening inside your target accounts {% endhint %} Here are the following Tech Indicators that Reo scores for every account in your target list or segment: {% content-ref url="/pages/Ttl4inkGc9JJJjFwoy8Z" %} [GPU Compute Intensity](/agents/tech-projects/gpu-compute-intensity.md) {% endcontent-ref %} {% content-ref url="/pages/iQ7QQKlJAqwqYKh4DWnV" %} [AI Nativeness](/agents/tech-projects/ai-nativeness.md) {% endcontent-ref %} *** ### **How Reo builds a Tech Indicator** **Reo uses multiple specialized agents to pull evidence from different sources, validate the signals, and reason toward a single, explainable conclusion.** This means the signal is: * **Multi-source**, and not dependent on one dataset * **Reasoned**, as agents interpret the evidence instead of simply matching keywords * **Explainable**, so you can understand why a company received a particular result For every company, **Reo brings together signals across the following 5 sources** to reason about these indicators and explain the evidence behind them. {% stepper %} {% step %} ### **Product** What the company has actually built, according to its own website, product pages, and engineering blogs {% endstep %} {% step %} ### **Hiring** Not just whether a role exists, but how specific it is. For example - ‘CUDA kernel optimization engineer’ (CUDA is NVIDIA's GPU programming platform) says far more than ‘ML Engineer’ with AI mentioned once in a long list of requirements {% endstep %} {% step %} ### **LinkedIn** How many current employees actually sit in AI/ML roles, and whether that number is growing. This feeds the scale of GPU-focused engineering investment, and also how much of the company's workforce is actually built around AI {% endstep %} {% step %} ### **GitHub** Whether the company's own engineers are active on the repositories that power GPU and AI workloads. A developer’s activity (like forks, commits, etc) on GitHub repositories is a solid signal {% endstep %} {% step %} ### **Firmographics** Funding and industry. Capital determines whether a company can sustain real GPU spend, and some industries (autonomous vehicles, genomics, video AI) simply demand more compute than others {% endstep %} {% endstepper %} *** ### Worth knowing * Today, **this works as an enrichment, not discovery:** you'll need an existing list or segment to run it against * **A Low tier with low confidence isn't a disqualified account** * It just means Reo doesn't have much public data on that company yet, maybe they keep a low profile, run a contractor-only team, or spend through someone else's infrastructure that leaves no trace * Worth a quick manual check before you write an account like that off * **The same logic applies to any single score that looks off to you** * Reo runs the same framework on every account and shows its reasoning for each one * So, check the evidence before you decide not to trust it --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.reo.dev/agents/tech-projects.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.