Airweave

What do they actually do

Airweave is an open‑source, developer‑facing service that turns data from apps, document stores, and databases into a single searchable knowledge layer for AI agents. Teams can use a hosted cloud or run it self‑hosted, connect sources like Google Drive, Slack, GitHub, Postgres, and Stripe, and Airweave ingests and indexes that data into “collections” for search (site/docs docs repo).

Developers then query one search endpoint (REST or Model Context Protocol/MCP) to fetch relevant context or an answer, instead of wiring many per‑app APIs. The system supports semantic and hybrid search with recency/temporal relevance, and offers Python/TypeScript SDKs. It can be deployed on‑prem with SSO/RBAC and multi‑tenant controls for sensitive data (docs MCP enterprise/self‑host pricing).

Who are their target customer(s)

• Developer teams building agent‑powered products: They don’t want to build and maintain custom ingestion + search pipelines for each data source, and agents end up with incomplete or stale context. Airweave provides a single searchable endpoint across many connectors (docs repo).
• Customer support and operations teams using assistants: Customer history lives across ticketing, email, chat, and docs, leading to wrong or low‑quality answers. Prebuilt connectors surface the right context from multiple apps for accurate responses (docs/connectors).
• Legal, contracts, and knowledge‑management teams: Finding precedent or clauses across shared drives is slow and risky; they need strict access controls and the option to run on‑prem or behind corporate auth (product/enterprise docs).
• Engineering managers and internal tooling teams: Answers require stitching code, PRs, tickets, and docs that aren’t searchable together. A single retrieval layer across GitHub/Jira/docs reduces context switching (GitHub connector example).
• IT, security, and compliance teams at larger orgs: They need to avoid vendor lock‑in and meet data residency, SSO/RBAC, and audit requirements. Self‑host and enterprise features address these needs (self‑host product).

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

• First 10: Convert active open‑source users and contributors into paid pilots with concierge onboarding and a custom connector if needed, highlighting the hosted or self‑host paths (repo connectors/docs).
• First 50: Run technical workshops, publish step‑by‑step connector tutorials and reference apps, and partner with agent/framework communities so Airweave becomes the default retrieval layer; emphasize fast hosted setup and on‑prem for sensitive teams (product/docs SDKs/connectors).
• First 100: Build a low‑touch free‑to‑paid funnel with clear limits and upgrade prompts, add 1–2 sales engineers for vertical outreach (support, legal, internal tools), and offer short on‑prem/SSO trials with priced enterprise plans backed by case studies and public pricing (pricing self‑host/enterprise).

What is the rough total addressable market

Top-down context:

Airweave sits across enterprise search and vector/semantic search infrastructure. Reports estimate enterprise search at about $4.8B in 2023 and growing, and vector databases around $2.0–$2.2B in 2024, implying a conservative ~$7B+ adjacent market today (Enterprise search Vector DB). Including AI knowledge‑management/semantic search platforms lifts the broader pool into the low‑to‑mid tens of billions over the next several years (AI in KM).

Bottom-up calculation:

As a developer‑led retrieval layer, a practical near‑term SAM could be 50k–150k teams adopting agent/RAG workflows globally, each paying ~$5k–$10k/year for hosted or supported self‑host, implying roughly $0.25B–$1.5B in serviceable spend. This expands as more teams move from experiments to production and require enterprise features.

Assumptions:

• Adoption: 50k–150k teams worldwide implement agent/RAG in the next 3–5 years (developer, support, legal, internal tools).
• Pricing/ARPA: blended ~$5k–$10k/year per team based on current public pricing and likely enterprise uplifts (pricing).
• A share of users will self‑host or use open‑source stacks, reducing hosted spend but increasing demand for enterprise features (SSO/RBAC, audit).

Who are some of their notable competitors

• LlamaIndex: Open‑source framework with many data connectors (LlamaHub) and indexing tools for building ingestion/retrieval pipelines; overlaps with Airweave on the developer ingestion + retrieval layer (docs GitHub).
• LangChain: Popular agent/framework toolkit with document loaders, retrievers, and vector store integrations; teams can assemble RAG/agent flows themselves instead of using a single hosted retrieval layer (docs).
• Weaviate: Open‑source vector database with modules for vectorization, reranking, and ingestion; appeals to buyers who want a vectors‑first datastore with built‑in RAG features rather than a connector+API service (modules).
• Vectara: Hosted semantic search/RAG platform exposing search and summarization APIs (indexing + query + reranking/generation); competes for managed, production search and grounded‑answer use cases (API).
• Glean: Enterprise workplace search that connects 100+ apps with permissions and provides unified search/assistant features; targets similar enterprise buyers needing secure multi‑app search and assistants (connectors).