How Reo calculates Technology Usage Confidence

Overview

Reo already tells you when a target account uses a technology you're tracking. That's useful on its own, and it isn't changing.

What's new is a rating that goes with every match: High, Medium, or Low.

What does Technology Usage Confidence (TUC) measure?

• the propensity that a company is actually using a given technology right now

• the depth of that usage, how embedded the technology is in how they actually build

Together, they tell you not just whether a company uses a technology, but how seriously to treat that match before you reach out.

Every score comes with the reasoning behind it, the specific signals and findings that led to it, so you always see why Reo landed on High, Medium, or Low.

It comes from looking at:

• whether a company's engineers actually have the skill for a technology

• what they're building on GitHub

• who they've hired recently

• how actively they're hiring right now

This article explains how that rating gets built, where you'll see it and how to read it.

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How the score gets built

4 key signals

The following are fed into every source:

• Job intelligence: how much the company is hiring for the technology, how recent those postings are, and whether hiring is picking up, holding steady or slowing down

• Developer skills: how many of a company's engineers list the technology as a skill - this is measured against what's typical for a company that size

• GitHub activity: how active the company’s engineers are in the technology's open source community

• Recent hires: whether the company has brought someone on with that skill, and how recently

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How it's calibrated

Every score is also calibrated to the company and the technology, unlike other players in this space that measure against one flat bar.

Here's how we operationalize it:

Company size

• 2 engineers listing a tool as a skill means something completely different depending on where those 2 engineers sit.

• At a 5-person engineering team, 2 engineers skilled in a tool is nearly half the team, which says a lot about how central that tool is to how they build.

• At a 500-person engineering org, the same 2 engineers could just be 2 people who picked up the skill somewhere else and never touched it again after joining.

Reo compares each company against others of a similar size, so the 5-person startup gets credit for what 2 skilled engineers actually represents there, and the 500-person company isn't rated as a heavy adopter off a signal that thin.

Technology type

What counts as strong hiring depends on how common the technology is in the first place.

Example 1: Same number of job postings, but different technologies

Company	Job postings this quarter	What it means
React	5	• Barely registers • Thousands of companies post React roles every month
Temporal	5	• Deliberate bet, so it’s a clear tell • Almost nobody hires for a specialized tool like this at that volume

Reo checks each company's hiring against what's typical for that specific technology. We don’t go by a fixed number for every technology. So, a company going deep on a rare tool isn't scored as Low just because its raw numbers look small next to a company hiring for something as common as React.

Example 2: Same technology, differing developer activity on GitHub

Job postings aren't the only thing that decides a score. Even for the same technology, developer activity can matter more. Here's what that looks like:

Company	Job postings this quarter	Engineers active on GitHub	Score
PostMan	5	10	Medium
CircleCI	10	50	High

PostMan posted twice as many jobs as CircleCI. But it has five times as many contributions to the technology's GitHub repos which is what led to the High confidence rating.

Example 3: A large number of postings doesn't mean much if none of them are recent

Here's what that looks like:

Company	Total job postings	Most recent posting	Score
PagerDuty	20	18 months ago	Medium
Datadog	6	3 weeks ago	High

PagerDuty has more than 3 times as many postings as Datadog. But its most recent one is 1.5 years old. Datadog has far fewer postings, but they're active right now. Recent hiring counts for more than a large pile of old listings, so Datadog scores higher.

That's the point of calibration - a score means the same thing no matter where it comes from:

• We compare each company against others of a similar size to measure developer skill

• We check each company's hiring against what's typical for that specific technology

• We weigh hiring along with how active a company's engineers are on GitHub

• Similarly, we weigh job postings, along with hoe recent they are

You never have to mentally discount a score because of who or what it's attached to. We do that for you through how we calculate TUC.

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How to read High, Medium, and Low

Rating	What it means	Recommended course of action
High	Strong, corroborated evidence, current	• Move straight to outreach • Pull the specific detail behind the score - a job post, a hire, a team, from the evidence panel • Use it to personalize your outreach instead of just saying ‘I noticed you use this’
Medium	A real signal, but thinner or older	• Include it in this week's list, but don't lead with the tech match alone • Pair it with another reason to reach out, and check whether the trend is accelerating before you decide how urgent it is
Low	Weak or stale evidence	• Don't lead with this technology yet • If the account is a fit for other reasons, keep it in your pipeline and revisit once the evidence changes, rather than writing it off

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3 places you'll find this within Reo

1

Agents

1. Navigate to Agents from the left menu

2. From the chat box, ask a question that includes a technology, for example: How deeply is Snowflake embedded at this account?

Sample prompts to try:

• How deeply embedded is LangChain in NVIDIA?

• Is Databricks actively hiring for Snowflake engineers right now?

• How does Stripe's use of Kubernetes compare to six months ago?

• Does Figma have any GitHub activity around React?

• What's the evidence behind Airbnb's Confidence score on Postgres?

1. The answer comes back with a Confidence rating attached, not just a binary response

1. Expand the reasoning to see it broken down by signal, what each one found and how it factored into the score

2

Company Audiences

1. Navigate to Audiences from the left menu > select Company Audiences tab

2. We now have a new column - Matched Tech

1. Every company in your results shows a Confidence rating for each technology it matches

1. Click a rating to know more about Insights and Findings: adoption momentum, recent hiring mentions, latest activity, and the date the score was last calculated

1. When you're creating a new Company Audience, you can add the technologies you're looking for. Layer on any other filters you need, like industry, headcount, or hiring intent

2. Reo returns every company that matches, and the Matched Tech column shows a Confidence rating next to each technology, not just whether it matched

3

Account Overview

1. Navigate to Accounts from the left menu > open any account

1. Under the Overview tab, on the left, you will find the Technology Stack section

2. You will find the tech and the corresponding TUC under 'Preferred Technologies‘ and 'Other Technologies’

Refer to our documentation on how you can configure your ICP & Preferred Technologies.

1. Click it learn more about Insights and Findings: adoption momentum, recent hiring mentions, latest activity, and the date the score was last calculated

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A few things worth knowing

1. This is NOT an account-level score

   1. This is scored per account, per technology. Not once across an account's entire stack

   • An account can be High on Kubernetes and Low on Snowflake. Don't read it as one grade for the whole account

2. A team doesn't need to be actively hiring to score well

   1. If the skills are already in the building and someone with that background joined recently, that shows up too, even in a quarter with zero relevant job posts

   2. A mature team that stopped hiring because it's fully staffed still looks like what it is: a strong match

3. Scores update on a quarterly basis

   1. The timestamp next to the score tells you exactly when it last ran

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FAQs

How is this different from Preferred Technologies?

They solve different problems. Preferred Technologies is your filter, it tells you whether a company matches a technology you care about at all. TUC is what you check once you already have that match, it tells you whether it's worth acting on this week. Most teams use Preferred Technologies to build the list and TUC to work it.

How is this different from the Tech Maturity agent?

Nothing changes about Tech Maturity from your side, it still works the way it always has, asking Reo to find companies deep into a stack. What's different is what's answering that question underneath. TUC is the engine behind that answer now, which is also why you'll start seeing the same rating in search, account pages, and other agents.

Do you have this for every technology?

Pretty much, yes. If there's any public signal at all, a job posting, a GitHub repo, an engineer listing it as a skill, we can score it. Coverage gets thinner for very niche technologies or very small companies, because there's less public data to work with in the first place. A strong hiring signal alone can still carry a score to High even when everything else is thin.

Why does this account show Low when I know they use the technology heavily?

Low could probably mean the public evidence hasn't caught up yet, not that we think the company doesn't use the technology. Maybe their hiring for that skill happened outside what's currently visible, or their engineers just don't list it publicly. Pull up the evidence panel and you'll usually see exactly why.

Does a Low score mean I should skip the account?

Not necessarily. If you already have a reason to think the account's a fit, a Low score isn't a reason to drop it, it's a reason to double check before you lead with the tech angle.

How often does this update?

Scores refresh on a quarterly basis rather than in real time, so there's always a short lag between something changing and the score catching up. The timestamp next to every score tells you exactly when it was last calculated, so you clearly know how current it is.