Q&A with Christian Lunoe, Head of Sales at Orion by Gravity

Christian Lunoe has joined Orion by Gravity as the company’s new Head of Sales. Based in Boulder, Colorado and founded in 2024 by former Looker and Google Cloud leaders Lucas Thelosen and Drew Gillson, Orion by Gravity is a collaborative analytics platform that surfaces what changed in a business before anyone asks, and lets teams ask follow-up questions in plain language without waiting on the data team. The company recently raised more than $10 million led by Next Frontier Capital, and its customers include Dave, Excess Telecom, Category Partners, and DMi Partners.

Lunoe has spent close to fifteen years in data and analytics. He began as a market research analyst at Comscore, working with consumer and payments data for Visa, Mastercard, and American Express. He joined Looker before Google's acquisition of the company in 2019, and stayed on at Google Cloud. He then spent close to five years in sales leadership at Level AI. A Wharton School economics graduate, Lunoe now leads sales at Orion by Gravity.

What is Orion by Gravity?

Orion by Gravity is a collaborative analytics platform that discovers important business changes and explains them in plain language, before anyone has to ask. It watches what is happening across the business, flags what moved and why, and sends a short briefing to the people who need it. When someone wants to dig in, they ask follow-up questions, and their teammates build on the same shared context.

It connects to the systems a company already runs, including Snowflake, Google BigQuery, Databricks, Amazon Redshift, Looker, Microsoft Power BI, and dbt. People work with it in the Orion platform, its briefings arrive in Slack and email, and it is reachable through MCP in tools like Claude. The problem it takes on is not access to data. It is awareness. McKinsey estimates the average knowledge worker loses close to a full workday every week just looking for information.

You've worked at Comscore, Looker, Level AI, and now Gravity. What's the thread that connects them?

The data almost always exists. The hard part is getting it to the person who needs it, and I have seen that from four seats. At Comscore my job was to tell a story with the data, but I could not touch the data myself. I waited on a SQL-savvy colleague for every pull, and it was never quite enough, because the first cut always raised a follow-up. Racing a presentation deadline, I would lose hours waiting to ask the next question, and eventually the fatigue set in and I just stopped asking. There were findings I never chased because each one meant another favor from someone already slammed. That is not a data problem. That is curiosity dying in a queue, and it kept me from doing my best work.

Looker was the first real fix. A semantic layer meant a company could define a metric once and everyone could trust it. I joined before Google's acquisition of Looker in 2019 and stayed on at Google Cloud. At Level AI I watched a company reach the same conclusion Lucas and Drew already had: business users want immediate answers to their own questions. Level AI's version, AI workers for conversational data, worked, but only inside the contact center. Orion points that idea at the whole business. When my old Looker colleagues showed me what they were building, it lined up with everything I had lived.

So how did Gravity come about?

It came out of a wall Lucas Thelosen and Drew Gillson kept running into for years, and the two of them founded the company in 2024. I worked with both at Looker. Lucas ran professional services there and later led product for Google Cloud's data and AI business. Drew has spent more than thirty years building enterprise data systems, from SAP, Greenplum, and VMware to Looker and Google Cloud. So between them they had spent their whole careers helping the biggest companies in the world get their data in order, and they kept seeing the same thing: even after all that investment, most people inside those companies still could not get an answer on their own. The way Lucas puts it, business intelligence has never actually been intelligent. It has just been an expensive way to answer questions you already knew to ask. Orion is what they built to change that.

Why is now the right time for this?

Three things arrived at once. Business users started expecting to type a question and get an answer, the way they do with ChatGPT. The warehouse became the center of company data, with Snowflake, Databricks, and Google BigQuery holding more of it than ever. And large language models are good enough to trust, as long as you ground them in real definitions. Investors are reading it the same way. Gravity raised more than $10 million led by Next Frontier Capital, and the backers include operators like Bryan Leach of Ibotta and Godard Abel of G2, people who have lived this problem from the inside.

For decades, BI tools promised to democratize data. Why has that been so hard to deliver?

Because the industry kept treating a people-and-process problem as a technology problem. I think in terms of people, process, and technology. The technology got great. The people part, that business users do not think in SQL, and the process part, the request-and-wait loop through the data team, never got fixed. So a new tool would land on the old process, and adoption would stall. It still is stalled. At a typical company only a quarter to a third of employees use the BI tools they pay for, versus about 80 percent of executives.

Here is the opinion that gets me in trouble: dashboards have become overrated. We spent twenty years teaching people to read dashboards and write SQL when we should have been teaching software to understand people. A dashboard waits for you to come look. Most of the time, the thing that matters is the change you were not watching for.

What makes Orion different from other AI analytics tools?

Most AI analytics tools wait for a question and answer it about a single database. Orion is different in a handful of specific ways. It is proactive, surfacing what changed before you ask instead of sitting idle until you query it. It is collaborative, so a whole team works from the same context rather than each person going solo. It is warehouse-agnostic, working across Snowflake, BigQuery, Databricks, Redshift, and the rest of the stack, so it can explain a change that spans your CRM, product, and finance data. It does not require a semantic layer: if your definitions live in LookML or dbt it uses them, and if they do not, it builds its own. It is governed and permission-aware, and it shows the query behind every answer. It reaches you through MCP inside tools you already use, like Claude. And it runs as embedded analytics, so a company can put Orion inside its own product and sell it. Any one of these exists somewhere. Putting them together is the point, and it is what a warehouse-native assistant in a box cannot match.

How does Orion keep its answers trustworthy?

The fair knock on AI and data is that it makes things up, so Orion is built to show its work. It answers from your governed definitions, respects who is allowed to see what, and shows the steps and the query behind an answer, so a skeptical analyst can check it. It is SOC 2 Type 2 compliant, and under the hood it runs as a set of specialized agents rather than one model doing everything, which helps it stay reliable.

It also keeps what we call an ambient-learning knowledge base, the caveats and definitions that usually live in one veteran analyst's head. It grows as people use it and updates as definitions change, so the reasoning behind an answer does not walk out the door when that person leaves.

Orion works inside tools like Claude through MCP. Why does that matter?

Because people should not have to go to the data. It should come to them, where they already work. Orion connects through MCP, the open standard for linking AI assistants to real systems, so someone can reach it from inside Claude or Slack. The Claude part matters more than it sounds. A lot of business users now think and write inside a general assistant, but that assistant does not know your company's numbers, and if you ask it anyway, it guesses. Through MCP, the answer comes back from your real definitions instead. Orion becomes the data layer behind the assistant people already use.

Where is Orion being used?

Two ways. Internally, a company points Orion at its own stack so teams that never get enough of the data team's time can serve themselves, and leaders get a briefing on what changed before they ask. Sales is the one I know best: a rep or a leader can see why pipeline slipped or which segments are converting, without waiting days for a report. Early customers have cut recurring and ad hoc analytics work by up to 85 percent and turned briefings that used to take days into minutes.

The one I find more interesting is embedded analytics. Category Partners does not just use analytics, it sells it. It is opening a new revenue channel by white-labeling Orion inside its own product, so its customers get an analyst of their own and Category Partners earns recurring revenue for it. Data monetization has been a promise for years. Category Partners is doing it.

There is a lot of fear that AI will replace analysts. Where do you land?

Analysts do not get replaced. They get their time back. What they tell us they notice first is that they stop answering the same question forty times a week. The repetitive pulls go to Orion, and the work that needs a human stays with them.

Adoption tracks ownership. When an analyst shapes how Orion answers and corrects it when it is wrong, and then the whole company relies on that work, it stops feeling like a threat. The data team ends up defining how the business understands itself, which is the job most of them wanted anyway.

What's the bigger shift you're betting on?

Companies do not have a data problem anymore. They have an attention problem. The data is already collected and already paid for. What is missing is noticing what matters in time to act on it. The companies that win the next decade will not be the ones with the most data. They will be the ones that make it easy for every employee, and every customer, to use the data they already have. Closing that gap, continuously and on its own, is the opportunity, and it is what convinced me to join Gravity.

About Orion by Gravity: Orion by Gravity is a collaborative analytics platform based in Boulder, Colorado, founded in 2024 by former Looker and Google Cloud leaders Lucas Thelosen and Drew Gillson. It launched Orion in 2024. Orion investigates what is changing across a company’s data and delivers plain-language briefings, in the Orion platform, in Slack and email, and through MCP in tools like Claude, for both internal teams and the customers they serve. It connects to Snowflake, Google BigQuery, Databricks, Looker, and dbt, and is SOC 2 Type 2 compliant. Gravity has raised more than $10 million, including a $7 million round led by Next Frontier Capital, with Foundry Group, K5 Global, and Denver Ventures, and angels including Bryan Leach (Ibotta), Godard Abel (G2), Nick Caldwell (Peloton), Ed Hallen (Klaviyo), and Tom Eggemeier (Zendesk). Learn more or book a demo at ByGravity.com.