Hyper

Competitive Intelligence & Product Roadmap

Shared memory layer for AI-first company context.

Company Overview

Hyper is a company-memory platform that ingests workplace context and feeds it into existing AI tools. Serving AI-first startup teams across engineering, product, founder, and GTM workflows.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

May 18, 2026

Product Surface Signal

Confidence:

Medium

New Intel: Hyper is turning company knowledge into a cross-tool agent memory layer. If it becomes trusted across Slack, Notion, GitHub, Claude Code, and Cursor, it competes for agent infrastructure budget before incumbents close the gap.

What They're Building

The company's public product roadmap & what they're committed to building.

Company Memory Layer

Hyper stores and retrieves structured knowledge for AI agents so company context can persist across tools.

Workplace Source Ingestion

The product pulls context from Notion, Slack, email, GitHub PRs, calendars, conversations, Claude Code, LinkedIn DMs, and Cursor sessions.

Agentic Knowledge Cleanup

Hyper uses agents to synthesize, clean, deduplicate, and keep company knowledge current.

Context Injection

The company aims to feed relevant memory into existing AI tools on each chat turn without requiring manual tool calls.

Founder and Key Execs

Shalin Shah

Co-Founder (former autonomy lead at Matic Robots and UC Berkeley EE/CS)

Kanyes Thaker

Co-Founder (former FDE at Snorkel AI, research lead at Matic Robots, and UC Berkeley CS and mathematics)

Founder Force Multiplier

Shalin Shah and Kanyes Thaker pair autonomy engineering with Snorkel AI field experience. That mix fits a product that has to model messy company state and sell into technical teams.

Funding History

2026 | Founded
2026 | YC Spring 2026
2026 | $125k seed investment listed from Y Combinator

Competitors

Memory Store:

Builds shared memory for team agents across tools including Claude, ChatGPT, Cursor, Slack, Raycast, Linear, Granola, Fathom, and OpenCode.

Glean:

Enterprise search and knowledge access incumbent with a broader buyer motion and more mature enterprise surface.

Guru:

Knowledge-management platform focused on verified internal knowledge, with less visible emphasis on automatic agent memory.

Notion AI:

Captures company context inside Notion, while Hyper is positioned as a cross-tool memory layer.

Hyper

's Moat:

Candidate moat is workflow switching costs: if Hyper becomes the permissioned memory layer across AI tools, replacing it means rebuilding connectors and accumulated context.

How They're Leveraging AI

Cross-tool company memory retrieval

Hyper uses AI to retrieve and inject company context into existing AI tools for AI-first teams. The product appears designed to make tools like Claude Code and Cursor aware of prior company decisions, docs, messages, and customer context.

Conversation-to-memory capture

Hyper's earlier product surface captured real-world conversations, produced transcripts, summaries, to-dos, and made those conversations searchable through a voice query interface. That looks like the seed of its broader company-memory system.

Agentic knowledge cleanup and deduplication

Hyper uses agents to synthesize, clean, deduplicate, and keep company knowledge current across messy workplace inputs. This is the less glamorous layer, but it determines whether the memory system is trusted or ignored.

AI Use Overview:

Hyper appears to use schema-constrained extraction and retrieval over company systems, with the edge in real-time context selection rather than model ownership.

Agent Infrastructure

Tasklet

Runs cloud AI agents for recurring knowledge-work workflows.

Firebase-grade infra founders are attacking workflow automation as LLMs start to operate software, with early ARR and USV as both customer and investor.

Decagon

AI agents that autonomously handle enterprise customer support across chat, email, voice, and SMS.

Agentic AI for customer support is one of the few GenAI categories with clean ROI math, and Decagon has locked in the blue-chip logo sweep that compounds into category default status.

Manus

Autonomous AI agent platform that executes multi-step business workflows end-to-end

A model-agnostic orchestration layer with real browser and shell execution captured viral enterprise demand faster than any US agent startup, a thesis validated by Meta's $2B acquisition.

Bubble Lab

Gives ops teams an AI super-employee in Slack that automates cross-platform workflows.

Zapier and Make require users to think in workflows. Bubble Lab's Pearl lives in Slack and runs multi-step operations (Jira, Stripe, Notion) from plain English, which means ops teams adopt it without learning a new tool.

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