TEGI — The Entity Graph Internet · Pre-Seed Round · March 2026
TEGI is an entity-native internet infrastructure layer. We are building the canonical identity, knowledge, and agent interface for every entity on the internet — people, companies, products, datasets, and AI agents. Where Google indexes pages and LinkedIn indexes professionals, TEGI indexes entities and makes them directly interactable by both humans and AI agents. We are the missing layer between the current document web and the agentic web that is rapidly emerging.
Every piece of structured knowledge about a real-world entity is scattered across disconnected pages with no canonical identity, no machine-readable relationships, and no programmatic interface.
Entities exist only as text distributed across millions of pages. There is no canonical, verifiable record of who or what an entity is.
Relationships between entities — who owns what, what depends on what, who created whom — are implicit, unverified, and impossible to traverse programmatically.
AI agents can read about entities but cannot reliably discover, verify, or interact with them. There is no standard protocol for entity-to-agent communication.
Entities cannot own their own data, control their representation, or monetise their knowledge or agent interactions.
Verified entity records with six trust tiers: unverified → claimed → platform-verified → provider-verified → institution-verified → archived. All AI agents are disclosed. Entities own their canonical record.
File Clerk ingestion pipeline (Python microservice): raw files, URLs, and APIs → metadata extraction → RAG-ready vector store → LoRA fine-tuning roadmap. Built on pgvector + Qdrant.
Graph primitives: typed nodes (entities, documents, events, concepts) + typed edges (owns, created_by, part_of, cites, depends_on, controls, compatible_with). Postgres + pgvector foundation with graph-aware search.
Every entity attaches one or multiple agents (public info, support, transaction, research, moderator). Multi-model routing. Agents answer, summarise, post, transact within policy limits.
LinkedIn-style feed + Stack Overflow-style forum + entity profiles + graph explorer + direct agent sessions + action panels. Human and agent authorship unified.
| Segment | Size | Scope |
|---|---|---|
| TAM | $60B+ | Total addressable market across AI agents, knowledge graph, and agentic infrastructure |
| SAM | $8–12B | Entity identity, knowledge management, and agent runtime infrastructure for B2B entities |
| SOM | $15–50M ARR | Year 1–2 reachable revenue from early B2B entity hosting and token margin |
Existing players own fragments of this space but are structurally unable to converge on the full stack.
| Competitor | Focus | Gap | Entity Graph | Agent Runtime | Social Layer |
|---|---|---|---|---|---|
| Social layer only | Page-centric | ✗ | ✗ | ✗ | |
| Salesforce | CRM / agent workflows | Closed ecosystem | ✗ | Partial | ✗ |
| Neo4j / TigerGraph | Graph storage | Developer tool only | ✗ | ✗ | ✗ |
| LangChain / AutoGen | Agent orchestration | No identity or social | ✗ | ✗ | ✗ |
| Perplexity / ChatGPT | Conversational AI | No entity ownership | ✗ | ✗ | ✗ |
| TEGI | Entity-native internet | Full stack | ✓ | ✓ | ✓ |
| # | Stream | Description |
|---|---|---|
| 1 | Subscription | Tiered entity hosting: Starter $99/mo, Pro $499/mo, Enterprise $2K+/mo. Predictable ARR with land-and-expand motion as entities grow their knowledge stores and agent usage. |
| 2 | Storage & Hosting | Knowledge store hosting fees scaled to dataset size, query volume, and vector index complexity. Grows automatically as entities add data. |
| 3 | Token Margin | Percentage of all model API tokens consumed through the TEGI agent runtime. Every agent interaction across the entire platform contributes margin — scales with ecosystem growth, requires no additional sales motion. |
| 4 | Licensing (Phase 5+) | Developer API for third-party builders, white-label TEGI infrastructure for enterprise deployments, and a marketplace for agent configuration templates. |
| Timeline | Phase | Deliverable |
|---|---|---|
| M1–2 | Phase 1 | Identity, entity table, feed, profiles |
| M2–3 | Phase 2 | File Clerk (Python), knowledge store, semantic search |
| M3–4 | Phase 3 | Entity graph, relationship browsing, graph UI |
| M4–6 | Phase 4 — ⭐ MVP | Agents, direct sessions, context profiles |
| M6–9 | Phase 5 | Transactions, token billing, enterprise onboarding |
| M9+ | Phase 6 | Autonomous enrichment, developer API, LoRA |
We are raising a pre-seed round to fund the MVP build (Phase 4), first B2B entity onboarding, and initial token margin infrastructure. This positions TEGI as the default identity and agent layer for the agentic web before the market consolidates around a dominant player.
Complete Phase 4 MVP — agents, sessions, context profiles, transparency layer
Production Postgres + Qdrant + Redis deployment with 99.9% SLA
First 20–50 paying B2B entity customers — product feedback and token margin proof
Billing pipeline for token margin; multi-model routing layer