The Top Enterprise AI Startups in YC S26
The enterprise AI companies in Y Combinator's Summer 2026 batch are automating work that large organizations currently buy as a service or staff with headcount: contact centers, market research, engineering interviews, security patching, and lobbying. Several are already running inside production systems at large companies and reporting revenue. Here are the top enterprise AI startups from the batch, in no particular order.
OpenTag — The AI coworker that lives in Slack
OpenTag (tryopentag.com) puts an agent inside Slack and Teams that teams tag the way they would tag a colleague. Invite it into a channel, hand it a task, and the result comes back in the thread with its sources attached. The pitch is that the harness should be model agnostic, so when a stronger or cheaper model ships, the coworker improves without the customer doing anything.
The agent runs in a sandbox with its own machine and browser, proposes automations for work it sees repeated, and maintains a wiki describing how the company actually operates, which the team and other agents can both read. Permissions follow the channels it has been invited into. Founders Tony Kam, Shelden Shi, and Wilson Nguyen worked together at Lilac Labs (YC S24) and met at Berkeley; the company launched earlier in the batch as Gini and says it is live with 10 teams.
Dialogus — Infrastructure for enterprise voice agents
Dialogus (dialoguslabs.com) is building what it calls an AI-native contact center. The company's argument, laid out in its launch post, is that demoing a voice agent is easy and shipping one is not: production agents have to reach phone systems, CRMs, internal databases, messaging channels, escalation paths, monitoring, and compliance review before they handle a single real customer.
Its platform handles that plumbing so agents can complete workflows end to end, gathering information, checking it against internal systems, updating records, sending follow-ups, and handing edge cases to a person. Dialogus says it is running voice operations for Fortune 500 enterprises and handling thousands of calls in production. It was founded by Rodrigo Terán, Hans Ibarra, and Juberth Rodriguez, with prior engineering work at Google and Meta.
Studio — The simulation engine for market behavior
Studio (trystudio.ai) builds live models of a company's market so teams can test a launch, a price change, or a campaign before committing to it. Rather than generating synthetic survey respondents, it constructs interconnected audience cohorts from behavioral data, combining a company's own sales and campaign records with outside signals like social conversation and market trends, then models how those groups influence each other over time.
Customers get forecasts for revenue, conversion, adoption, and sentiment, along with a breakdown of which cohorts are driving a result and the evidence behind it. Forecasts are compared against real outcomes afterward and the model is recalibrated. The company says its predictions have matched live KPI outcomes with better than 95% accuracy in tests with consumer brands including Danone and Mila. Nikita Mullangi built decision-making simulation software at MIT, and Cameron Malloy built predictive models in private equity and agent-based simulations at Berkeley.
Litmus — Evals for humans
Litmus (litmushiring.com) generates technical assessments from a company's own repositories, tickets, and job descriptions, then lets candidates complete them asynchronously in their normal environment with the tools they actually use, AI included. Submissions run in a sandbox so graders score executed code, and evaluation covers process and tool fluency alongside the final output. The framing is that model evals have a method worth borrowing for people: build a realistic environment and inspect the trajectory, not just the answer.
The company positions itself between LeetCode-style screens, which it argues test the wrong thing, and full work trials, which do not scale. It reports more than 3,000 assessments evaluated, and says it has built hiring systems for Neo Scholars and its portfolio, Mercor, and Composio. Shaivi Rau is CEO, previously in early-stage startups and venture capital, and Elena Zhao is CTO, previously a software engineer at Two Sigma and Meta.
Palisade — AI that finds and fixes OS vulnerabilities across fleets
Palisade (palisade-ai.com) works a layer below most security tooling. It monitors syscalls, eBPF traces, process and memory events, and network packets to catch operating-system flaws such as privilege escalation, remote code execution, and memory corruption, which are difficult to see from the network, cloud, or application layer. Once a problem is identified, the fix is pushed as a kernel rule across every endpoint, compressing a rollout that would otherwise take weeks.
The company's launch post lists real CVEs it already handles, including the netfilter use-after-free in the Linux kernel tracked as CVE-2024-1086 and the sudo chroot privilege escalation tracked as CVE-2025-32463. The target customer is any organization running large endpoint fleets, GPU clusters or cloud servers included, where one kernel bug multiplies across every machine. Founder Prince left Caltech at 18 to start the company and had been doing professional red-team and blue-team work since 16.
Locke — The first AI government affairs firm
Locke (locke.inc) is a services firm with agents underneath it. Its AI handles legislative analysis, stakeholder tracking, and drafting of contract proposals, while in-house lobbyists own the path from initial research through the first meeting to a funded contract. The company's case is that traditional government affairs is fragmented across separate firms for the Hill, the agencies, and procurement, with no single party accountable for the result.
Locke says it passed a $500,000 annual revenue run-rate six weeks after a quiet launch, with clients in defense, healthcare, and govtech. Founder Parth Badhwar held policy roles at the White House and Deloitte before leading go-to-market at a startup selling into government offices, and the broader team comes from Congress, the Pentagon, and Deloitte.
Every company here is selling into a budget line that already exists, which is the shortest path an enterprise AI startup can take to revenue in its first year.