Aug 13, 2026 earnings call — EngineRoom deal and CXAI 2.0 deployment
Q2 revenue rose to $1.7 million, with ARR reaching $11.5 million after EngineRoom integration.
Revenue $0.0B (est $0.0B) · EPS $-5.00 (est $-3.00)
Analyst Q&A 5
strategy, products and EngineRoom
Q (Chris Wiegand): The analyst reviewed strategy and asked for product, customer and go-to-market updates, including how EngineRoom fits and how Sky will scale and deploy new modules.
A: Management said the combined company now has more than 50 customer relationships from EngineRoom, and the acquisition added about $4 million in revenue. They described product progress: Flow 2.0 is deployed, Events will be GA in September, and Beat is scheduled for Q4. They said they are moving from custom code to configuration, shifting to per-user software pricing, and plan to use EngineRoom for mid-market distribution with pre-configured connectors, resellers and marketplace partners.
AI and workplace trends
Q (Zoe Chen · Veldhoen + Company): The analyst (Zoe) presented three practical observations about AI in the workplace and asked the panel to react and discuss deployment and impact.
A: Management thanked Zoe and said her remarks set helpful context for the discussion. They noted Zoe is visiting customers and seeing real adoption patterns in the field. The panel used her observations to guide follow-up questions about trades, mid-market deployment and product fit.
"Zoe is flying around probably the world, but I'll say at least the States, going to different customers, and she's really seeing what's happening out there." — Chris Wiegand
AI for trades and small businesses
Q (Zoe Chen · Veldhoen + Company): Adam asked where AI can best turn operational and customer data from trades and home-service businesses into better decisions and outcomes.
A: The respondent said the data already exists but is scattered across systems, texts, invoices and people’s heads, and the owner often lacks the time to analyze it. The opportunity is to surface patterns the business already has—like which jobs actually make money after drive time, which estimates are wrong, and what work to take more or less of—and turn that into clear, actionable insights the team can use.
how AI deployment scales
Q (Zoe Chen): Khurram asked how deployment can scale given fast model change, many tools, and younger employees who can code—how to get a repeatable, scalable motion in enterprises and mid-market.
A: The respondent said mass deployment must not require a dedicated owner, champion or complex project plan; that approach won't scale in smaller companies. Instead, adoption spreads when an individual's use visibly saves time or delivers better results, so others copy it; mid-market firms move fast because fewer people need to agree and visible day-to-day gains drive uptake.
mid-market experience and next steps
Q (Zoe Chen): Khurram asked Adam to close with his experience in the mid-market and how he sees that segment evolving with these technologies.
A: The transcript does not include a direct, substantive answer from Adam to this closing question. Instead, the moderators moved toward closing remarks and additional Q&A, so no full Adam response is shown here.
"I think we're going to now move to -- Khurram has some closing words, and we have some more Q&A." — Chris Wiegand