Hyperspell

What do they actually do

Hyperspell provides a hosted “memory” layer for AI agents. Its API, dashboard, and SDK connect to company tools like Slack, Gmail, Notion, and Google Drive, continuously index that data into a searchable memory graph, and return structured context or LLM‑ready summaries that developers feed into their agents (site, docs).

This is a shipped developer product with a live dashboard and docs, early pilots/testimonials, support for bring‑your‑own LLMs, and continuous updates from user interactions; the company also highlights SOC 2 for enterprise readiness (site, YC profile, Forbes).

Who are their target customer(s)

• Builders of agent‑enabled products (SaaS teams and AI startups): They need agents that remember company facts and past conversations, but building connectors and a reliable memory layer in‑house is slow and costly. Hyperspell offers prebuilt connectors and an SDK so teams can plug in a memory API instead of building it themselves.
• Internal automation teams (sales ops, HR automation, internal tools): They need up‑to‑date information from Slack, Gmail, Notion, and Drive, but stitching sources together and keeping data fresh is a burden. Hyperspell continuously indexes these sources into a persistent memory graph to reduce staleness and fragmentation.
• Customer support and knowledge teams using AI assistants: They want accurate answers grounded in internal docs and past tickets, but off‑the‑shelf LLMs can hallucinate or miss context. A memory layer that returns structured context or summaries helps reduce wrong answers and speed resolution.
• Security, compliance, and IT owners at larger companies: They worry about granting AI broad access to sensitive data and need auditability and access controls. Hyperspell emphasizes enterprise controls and SOC 2 compliance to address these concerns.
• Platform/ops engineers running agent runtimes: They need a standard way to feed context into different LLMs and frameworks, but custom memory systems are brittle and expensive to scale. Hyperspell provides APIs and runtime integrations, including bring‑your‑own LLM support, so memory can be consumed uniformly across agents.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led pilots with hands‑on integrations for target dev teams and internal automation owners; scope narrowly, ship the needed connectors, and prove measurable drops in hallucination or agent failure to convert (docs, site).
• First 50: Publish SDK tutorials, templates, and reference integrations (AgentStack, Slack, Notion, Drive) to enable self‑serve; pair with targeted outreach to platform partners and developer communities and convert with short trials and a developer success playbook (docs, site).
• First 100: Stand up a mid‑market/enterprise motion with 1–2 AEs and a security/solutions lead; package SOC 2 and security artifacts, offer procurement‑friendly contracts/SLAs, and add SI/ISV partnerships to drive channel deals (site, YC profile).

What is the rough total addressable market

Top-down context:

Relevant spend pools include knowledge‑management software (~$20B in 2024) and AI application software (~$172B in 2025), with conversational AI already a multi‑billion category; Hyperspell’s near‑term TAM sits in the low single‑digit billions with room to expand if memory becomes standard (Grand View Research — KM, Gartner 2025, Grand View — Conversational AI).

Bottom-up calculation:

If roughly 10,000–30,000 mid‑market/enterprise teams adopt a hosted memory layer at an average ACV of $75k–$100k, that implies ~$0.75B–$3B near‑term TAM; if persistent memory becomes a required component for 5–20% of AI application software (~$172B in 2025), the serviceable pool expands to ~$8.6B–$34B (Gartner 2025).

Assumptions:

• Adoption by 10k–30k mid‑market/enterprise teams actively deploying agents in the next few years.
• Average ACV of $75k–$100k for a hosted, enterprise‑grade memory layer with connectors and security.
• 5–20% of AI application software spend ultimately requires a purchased (not built) persistent memory layer.

Who are some of their notable competitors

• Vectara: Hosted RAG platform with connectors and focus on grounded generation; overlaps with teams seeking an out‑of‑the‑box retrieval and summarization pipeline instead of building memory in‑house.
• Pinecone: Managed vector database widely used for retrieval‑augmented generation; many teams assemble agent memory on top of Pinecone rather than buying a dedicated memory layer.
• LlamaIndex: Developer framework for connecting data to LLMs with indexing, retrieval, and agent tooling; common DIY alternative for building memory and context pipelines.
• Glean: Enterprise search across tools like Google Drive, Slack, and Confluence; competes for internal knowledge retrieval budgets that might otherwise fund a memory layer.
• LangChain: Popular agent framework with memory and retrieval modules; teams may use LangChain components to build their own memory systems instead of adopting a hosted service.