CubeAPM is a full-stack observability platform built for engineering teams at high-growth companies. It processes over a billion requests a month and helps teams at companies like BharatPe, Delhivery, Shiprocket, and Mamaearth monitor, debug, and scale faster, without the cost and compliance tradeoffs of traditional SaaS APM tools.
Their buyers are CTOs, VP Engineering, platform leads, and DevOps heads at fintech, logistics, e-commerce, and SaaS companies. Technical buyers who are selective, hard to reach, and quick to ignore anything that feels generic.
Getting their attention meant showing up where they actually are: LinkedIn first, WhatsApp to close.
The challenge: generic outreach doesn't work on technical buyers
Before toflow.ai, the team was building prospect lists manually. Searching LinkedIn, noting down names, copying everything into a spreadsheet. Then writing individual messages and sending them out with no real system for following up.
"We knew exactly which companies we wanted to go after. The problem wasn't the list. It was how long it took just to get from that list to an actual outreach going out."
Vineet Chirania, Co-founder at CubeAPM
Reaching engineering leaders made it harder. CTOs and DevOps heads spend time on LinkedIn. They check connection requests from people who clearly know their space, and they respond to messages that reference something specific about their stack or scale. But the team had no coordinated LinkedIn outreach and no way to follow up at the right moment. In India, WhatsApp is where conversations with technical buyers actually close, and that channel was entirely missing from their outreach.
Why toflow.ai changed how the team prospects
Vineet already used Claude daily for writing and research. When he saw that toflow.ai connected directly to Claude, the decision was straightforward.
"I already use Claude every day, and now it just works inside toflow too. I asked it to find fintech founders in India and kick off a LinkedIn sequence. It was done in under two minutes. That used to take half a day."
Vineet Chirania, Co-founder at CubeAPM
Three things made toflow.ai the right fit.
LinkedIn as the primary channel for technical buyers. Engineering leaders spend time on LinkedIn. They check connection requests, they read InMails from people who clearly know their space, and they respond to messages that reference something specific about their company's stack or scale. toflow.ai's LinkedIn automation runs connection requests and follow-up messages with context pulled from account research, so every touch looks like it came from someone who did their homework.
WhatsApp as the channel that closes the loop. In India, WhatsApp is where conversations move fast. A CTO who has not replied to a LinkedIn message will often respond to a well-timed WhatsApp message. toflow.ai includes WhatsApp natively, not as a separate tool. When a prospect goes quiet after the LinkedIn sequence, a WhatsApp follow-up goes out automatically as part of the same sequence.
Prospecting through Claude. toflow.ai connects directly to Claude, so Vineet can describe the target in plain English and have a sequence running in minutes. No filters to configure, no manual steps between identifying who to reach and actually reaching them.
How CubeAPM runs outreach today
Building the prospect list
The team uses toflow.ai's Chrome extension to pull leads from LinkedIn Sales Navigator. Targets are filtered by job title (CTO, VP Engineering, Head of DevOps, Platform Lead), company size, and industry. Contacts land in toflow.ai directly.
For faster prospecting, Vineet types the target profile into Claude: "Find me engineering heads at fintech companies in India with 200 to 1000 employees." Claude searches, qualifies contacts, enriches their details, and enrolls them in a sequence. What previously took a morning now takes a few minutes.
toflow.ai's account research surfaces context before any message goes out: the tech stack the company is likely running, their scale, whether they have mentioned monitoring or infrastructure challenges publicly. That context shapes the message for each prospect rather than starting from a blank template.
The outreach sequence
LinkedIn opens every conversation. WhatsApp is where it closes.
Day 1. A LinkedIn connection request written around the company's engineering context. For a fintech processing payments at scale, the note references latency and compliance overhead. For a logistics platform running real-time tracking, it is uptime and observability at volume. Specific enough to read like research, short enough to fit the format. Engineering buyers notice when someone clearly understands their stack, and that recognition is what gets the request accepted.
Day 4. A LinkedIn message to prospects who have connected, going deeper on the specific challenge their company faces. The context from the connection request carries forward. This is not a cold follow-up. It is a continuation of a conversation the prospect already opted into, with more substance behind it.
Day 8. A WhatsApp message for anyone who has not yet replied. In India, this is often where the response comes. A CTO who has not responded to two LinkedIn touches will frequently respond to a well-timed WhatsApp message from someone they have already connected with. The prior LinkedIn touch is what makes the WhatsApp message feel like a continuation rather than a cold approach. WhatsApp is where the conversation moves from outreach to a booked demo.
Keeping conversations alive
"The follow-up was always where things fell apart. Someone replies once, shows interest, and then life moves on. You forget to follow up and the conversation dies."
Vineet Chirania, Co-founder at CubeAPM
toflow.ai's follow-up agent monitors every active thread across LinkedIn and WhatsApp. When a prospect shows interest and the conversation goes quiet, the agent sends a follow-up timed to the engagement signal rather than a fixed calendar date. No lead cools because no one remembered to follow up.
Managing replies without the overhead
As reply volume grew across LinkedIn and WhatsApp, keeping track of every thread became its own task. toflow.ai's inbox manager handles this. Replies from both channels land in one feed, the agent reads the thread, drafts a response, and categorises the intent. The team steps in only for conversations that need a human.
Results
Demo calls doubled. Qualified demo calls booked per month went up 2x after switching to toflow.ai. The combination of accurate contact details, coordinated LinkedIn sequences closing on WhatsApp, and consistent follow-ups is what moved the number.
12% average reply rate. Engineering buyers are harder to reach than most. A 12% reply rate reflects how much the channel strategy matters. LinkedIn builds the first impression and earns the connection. WhatsApp is where that connection turns into a reply.
10x faster to launch a sequence. What used to take half a day, building a list, finding contact details, writing messages, setting up outreach, now takes minutes. Vineet types a target description into Claude and the sequence is running before he finishes his next task.
"The time we used to spend on setup is now going into the actual conversations. The first call is already warmer because the outreach was specific."
Vineet Chirania, Co-founder at CubeAPM
What's next for CubeAPM
The team is running more targeted LinkedIn sequences by company stage, tailoring the angle for early-stage startups scaling fast versus enterprises dealing with legacy monitoring costs.
"Every segment has a different problem with observability. The more specific we can get with the outreach, the better the conversations we are walking into."
Vineet Chirania, Co-founder at CubeAPM