YesPress Profile · Company File
Exhibit A: the wordmark you've seen on a chat widget without ever noticing it.
Capacity.
The St. Louis company teaching AI to answer the questions nobody wanted to answer.
The St. Louis company teaching AI to answer the questions nobody wanted to answer.
Somewhere this minute, a customer is typing a question into a chat box. They expect an answer in seconds. They will not get a person. They will get Capacity - and most of the time, they will never know the difference.
That is the quiet trick of Capacity in 2026. It is an AI support automation platform that fields chats, calls, emails, and texts for more than 20,000 organizations, from the US Army to Disney. The company reports north of 36 billion automated interactions. It crossed $60 million in annual recurring revenue last year and, less glamorously but more rarely, became profitable. And it did all of this from St. Louis, Missouri, which is not where the story of artificial intelligence is usually set.
Capacity sells a deceptively boring promise: the support queue can be smaller. Tickets that used to wait for a human can be resolved before a human sees them. The hold music can stop. For a category that has spent a decade apologizing for wait times, that is the whole game.
"Capacity is an all-in-one, AI-powered support automation platform that deflects tickets, emails, and phone calls using practical and generative AI."
Here is the inconvenient truth about enterprise knowledge: it exists. Somewhere. In a PDF, a Slack thread, a wiki nobody updates, the head of a colleague who left in 2021. The answer to almost any support question is technically available. It is just unreachable at the exact moment someone needs it.
So companies hired people to go find it. Then they hired more people to manage the people finding it. Then they bought ticketing systems to track how long the finding took. The cost of not-knowing got distributed across help desks, call centers, and the patience of customers - a tax everyone paid and nobody itemized.
Capacity's founders had spent years staring at this exact problem from the other side. Their previous company was, of all things, a question-and-answer business.
"The answer almost always exists. The work is making it findable the instant someone asks."
In 2006, David Karandish and Chris Sims built the parent company of Answers. In 2014 they sold it to a private-equity firm for roughly $960 million. Most people, having sold a company for nearly a billion dollars, would take up sailing.
They started another company instead. Founded in 2017, Capacity was a bet that the knowledge problem they had circled for a decade was about to be solvable in a new way - not by indexing the public web, but by sitting inside a single organization and connecting its scattered systems into one place that could answer questions. The timing was either prescient or lucky; the generative-AI wave that arrived a few years later made the bet look obvious in hindsight, which is the only time bets ever look obvious.
Co-Founder & CEO. Previously built and sold the parent company of Answers. Now running the company that automates the questions Answers only ever indexed.
Co-Founder. The other half of a partnership that has now started two knowledge companies and sold one of them for close to a billion dollars.
"Founders David Karandish and Chris Sims previously built the parent company of Answers and sold it in 2014 for roughly $960M."
Capacity is not one tool. It is a stack that has grown by both engineering and acquisition - the company folded in Lucy, Envision, and Linc and stitched them into a single platform. The pitch is unification: instead of a chatbot here and an analytics tool there, one system that automates, assists, analyzes, and engages.
Autonomous agents resolve questions across chat, voice, SMS, email, and web - deflecting tickets before a person is ever involved.
Real-time suggestions and coaching for human agents while they are mid-conversation. The machine whispers; the human decides.
Analytics and quality assurance that score and read interactions across every channel.
A knowledge engine connecting siloed documents, apps, and data - with 250+ pre-built integrations - so answers stay current.
"Automate, assist, analyze, engage - through one unified platform instead of four tools that don't speak to each other."
Karandish and Sims launch Capacity in St. Louis, betting on knowledge automation inside the enterprise.
An $8.4M Series A is followed by a $13.2M Series B from a Midwest network of private and angel investors.
An $11M close in October 2020 kicks off a Series C that later grows toward $38M as ARR climbs.
Fresh capital arrives just as generative AI turns "support automation" from a nice-to-have into a board-level line item.
Capacity raises again and acquires support-automation companies, deepening its agentic-AI roadmap (TechCrunch).
The company secures $92M - including a $50M debt facility from Chicago Atlantic - reports $60M ARR, and turns profitable. Kathy Ireland takes a strategic stake.
Skepticism is fair. Every software company claims to automate something. So here is what Capacity puts on the table, and the scale is the point: this is not a pilot, it is infrastructure that runs in the background of brands you use weekly.
The customer list reads like a cross-section of the economy - which is the tell. Support is a universal problem, so a platform that solves it sells everywhere.
"More than 20,000 organizations, 1.5 million users, and 36 billion automated interactions. That is not a demo. That is a utility."
Capacity is careful about how it frames the machine. The stated mission is not "replace the support team." It is to give people - and now AI agents - instant access to the knowledge and automation they need. The Agent Assist product is the clearest expression of that: the AI does not take the call, it stands behind the human taking the call and hands them the right answer.
Whether that distinction holds as the agents get better is the open question of the whole category. For now, Capacity's bet is that the most valuable place for AI in support is next to a person, not instead of one. It is a more comfortable story to tell, and it also happens to be where the technology is most reliable today.
"The goal is to help teams do their best work - the AI handles the routine so the humans handle what actually needs them."
Agentic AI - software that does not just answer but acts - is the next argument in enterprise software. Capacity has spent years building the unglamorous foundation it requires: the integrations, the knowledge plumbing, the analytics, the human-in-the-loop guardrails. The companies that win the agentic era will be the ones that already did this boring work. Capacity did it before it was fashionable.
There is also the matter of being a profitable AI company, which in 2026 remains rarer than it should be. Capacity reaching $60M ARR and turning a profit is a quiet rebuke to the idea that AI businesses have to burn cash forever to matter.
So return to that customer, still typing a question into a chat box. A decade ago the honest answer to "how long will this take?" was "longer than you'd like." Today the answer arrives before the question fully lands, assembled from a knowledge base the customer will never see, by a company most of them have never heard of. Capacity built the thing that makes the wait disappear. The reward for doing it well is that nobody notices it was ever there.