Costream Wants to Index Every Conversation Happening Inside Your Product

The Macro: Your Product Is Talking, and Nobody Is Listening

I have a theory about the next wave of product analytics. The current generation of tools tells you what users did. Amplitude, Mixpanel, PostHog, Heap. Click here, scroll there, drop off at step four. These tools are good. They are also incomplete. They tell you the what but not the why. They show you a funnel but not the frustration.

The missing signal is language. Every product with any kind of human interaction generates enormous volumes of conversational data. Support chats. Feedback submissions. In-app messages between users. Comments in collaborative tools. Onboarding conversations. Sales handoff notes that live in a CRM but never get connected back to the product experience. This data exists. It is rarely indexed. It is almost never queryable in a structured way.

Some companies try to solve this manually. Product managers read support tickets. Customer success teams tag conversations in Intercom or Zendesk. Developers search Slack for bug reports that users mentioned to support but never filed as issues. The process is fragmented, slow, and lossy. You catch the signals that happen to land in front of the right person at the right time. You miss everything else.

Fullstory and Hotjar added session replay, which is a step in the right direction. You can watch what a user did. But watching a session recording is linear and time-consuming. You cannot search across ten thousand sessions for the moment when someone expressed confusion about pricing. LogRocket captures front-end errors alongside session data, which helps with debugging but does not help with understanding user intent.

The gap is clear. There is no infrastructure layer that treats human dialogue inside products as a first-class data type. Something that indexes it, structures it, makes it searchable, and connects it to the behavioral data that analytics tools already capture.

The Micro: Two Brothers, Two Dropouts, One Hundred Thousand Users

Aditya Mahna and Ayush Mahna are brothers. Both attended Cornell. Both dropped out. Aditya, the CEO, previously built and scaled an AI company to over 100,000 users. Ayush built a consumer app that hit 100,000 downloads. They came through Y Combinator’s Summer 2025 batch. The team is two people.

I want to talk about what that track record actually tells you. Building a consumer product to 100K users as a college student means you understand distribution, retention, and the mechanics of getting people to use something. That is a different skill than building technically impressive software that nobody adopts. Both brothers have demonstrated they can ship things that people actually want to use. That matters for a company building infrastructure, because infrastructure products die when they are technically elegant but impossible to integrate.

Costream’s pitch is to index human dialogue flowing through digital products. The site is early. It is a clean landing page with a YC badge and a contact email. There is no public documentation, no feature list, no pricing page. This is pre-launch in every sense.

But the concept is sound. If you could query all the conversations happening inside your product the way you query an analytics database, the applications are immediate. Which features generate the most confused language? Where do users express intent to cancel? What do people say right before they convert? Which support conversations correlate with high lifetime value?

The competitive landscape here is fragmented. Intercom and Zendesk own the support conversation layer but do not expose it as a queryable data product. Gong and Chorus index sales conversations but only within the sales context. Dovetail and Grain help research teams organize user interviews. Nobody is building a horizontal layer that captures dialogue across all of these touchpoints and makes it available as structured, searchable data.

That is the opportunity. It is also the risk. Horizontal infrastructure plays are hard because they require integration with everything. You need to ingest data from support tools, chat platforms, CRMs, in-app messaging, and probably channels that do not exist yet. The technical surface area is enormous for a two-person team.

The Verdict

I think conversational data is genuinely under-indexed, and I think the market timing is right. AI has made it feasible to process unstructured language at scale in ways that were not economically viable three years ago. The NLP required to turn messy support conversations into structured, queryable data is now cheap enough that an infrastructure product can deliver value without charging enterprise prices.

The risk is scope. Indexing “human dialogue through digital products” is a massive surface area. The successful version of Costream probably starts narrow. One integration, one use case, one type of dialogue that delivers immediate value. If they try to boil the ocean on day one, they will burn through their runway before landing their first paying customer.

Thirty days, I want to see which integration they ship first and whether the early users are product teams, support teams, or something else entirely. Sixty days, whether the indexed data actually surfaces insights that existing analytics tools miss. Ninety days, the question is whether this is a standalone product or a feature that Amplitude or PostHog absorbs into their platform. If the dialogue layer delivers signal that behavioral analytics cannot, Costream has a real moat. If it is just a nice-to-have supplement, the incumbents will clone it.