It’s Governance First, AI Second at Squash
The service desk automation startup believes dependable guardrails are essential to giving MSPs the confidence they need to unleash AI’s fullest powers.
Atera’s guaranteed 50% automated ticket resolution rate within 90 days of implementation is essentially just a starting point. Robin can potentially close as many as 92% of tickets eventually if you grant it permission to address all the many ticket types it’s capable of addressing.
“There are customers that do that and there are others that limit it, and they’re at 75% or 60%,” says Gil Pekelman, Atera’s CEO.
Hard to know without guessing why users who limit Robin’s actions are doing so, but uneasiness about the many things that can go wrong when AI acts alone probably has something to do with it, suggesting that there’s a direct relationship between how much MSPs trust their AI automation tool and how much productivity benefit they get from it.
Which brings us to Squash, a very young service desk automation startup founded by veterans of Silicon Valley high-flyers Rippling and Harvey that’s betting big AI can deliver a lot more for MSPs than it is today provided those MSPs are comfortable allowing it to.
“We are at a point with the technology where the tech is not the limitation any longer,” says CEO and co-founder Sandeep Uppaluri (pictured). “The limitations that we see are basically MSPs’ willingness to lean in, adopt, and make the most of the tech.”
The product Squash built in response to that analysis stands apart, the company believes, for two reasons, starting with the breadth and depth of its automation abilities.
“A lot of the other players out there in the market today are mostly focused on ticket intake, maybe triage and dispatch of tickets,” Uppaluri says. “We integrate very broadly into your entire tech stack, and we will take actions on your behalf to help you resolve those tickets.”
More importantly, he continues, AI governance is a core design pillar rather than afterthought at Squash.
“We built out the governance controls before we built out any of the AI stuff, basically because we think it’s super important,” Uppaluri says. “The better your governance controls, the more it allows you to unlock the power of the models, the further you can push them, and the more you can get out of them.”
Squash uses a containerized environment within the tool to enforce governance. Incoming tickets go straight to that isolated space, where the system automatically applies the user’s guardrails. The system helps users slowly define those policies over time on a ticket type by ticket type basis.
“A lot of our product is designed around how we can enable a gradual rollout,” Uppaluri says. “It’s that trust building that is the crux of getting value out of AI. It’s not tech.”
Squash users come in every variety, the company says, but tilt a bit toward the kind of MSP running OpenClaw in the lab because they like living on the leading edge.
“Very quickly they start to realize that you need more governance controls,” Uppaluri says.
That Squash takes such controls seriously isn’t the only thing distinctive about it, however, he adds. The company’s unusually flexible pricing scheme sets it apart as well, not just from other AI startups but from nearly everyone else as well. Want per endpoint pricing? Squash is happy to provide it. Prefer something outcome-based? Squash has you covered there too.
“We try to flex the model to make sure that it’s working for you,” Uppaluri says. “The goal is to make it feel as bespoke as possible.” And to ensure that the fees partners pay and productivity gains they collect net out to about a 20% increase in service margins, he adds.
Consistently. Like Atera, Squash has opted not to saddle partners with the challenges of earning predictable profits while paying unpredictable token-based rates.
“MSPs shouldn’t be worrying about what model to use, how much it costs, how the price has changed, and how to orchestrate these different things,” Upplauri says. “That’s the responsibility of a vendor.”