AI that knows your organization.

AI contextual organizational knowledge is the practice of grounding large-language-model answers in your company's own documents, tickets, chats, code, and data — so the AI answers questions about your business with your facts, not its general training data. The dominant technical pattern is retrieval-augmented generation (RAG), increasingly combined with knowledge graphs to capture explicit relations between entities (people, products, contracts, customers). ZeroTwo provides the workspace, multi-model generation, and evidence-quoted answers; you bring the corpus.

No, and the most successful 2026 deployments combine both. RAG retrieves relevant text chunks and feeds them to the model as context — strong for unstructured documents, weak at explicit multi-hop relations. Knowledge graphs encode entities and relations as a graph — strong for queries like "every customer who bought product X and works in industry Y", weak at free-form language. Hybrid architectures use the graph for structured filtering and the vector store for semantic retrieval, then have the LLM stitch the result. This is the converged best practice as of 2026.

Public web data is well-formed: articles, papers, books. Company-internal data is the opposite — support tickets, email threads, customer call transcripts, half-finished docs, Slack DMs, code diffs, recorded meeting minutes. The 2026 EnterpriseRAG-Bench paper exists exactly because public RAG benchmarks failed to capture this messiness. Building real enterprise knowledge AI means designing for noise, partial information, conflicting versions, and access-control boundaries from day one.

Files (PDF, DOCX, XLSX, TXT, Markdown, HTML), web URLs, code repositories, and any MCP-server-exposed data source. For broad enterprise integration (Confluence, Notion, Google Drive, Slack, Salesforce, HubSpot, ServiceNow, etc.) ZeroTwo works alongside or downstream of your existing data-platform integrations — pull the chunks into ZeroTwo's workspace, run multi-model retrieval and generation, then write results back through your tools.

Five layers: (1) good retrieval — hybrid vector + keyword + graph + role filters; (2) retrieval evaluators that score whether retrieved chunks actually contain the answer before generation; (3) prompting the model to abstain when the corpus doesn't cover the question; (4) post-generation evaluators that check the answer against the retrieved chunks (citation-grounding); (5) human-in-the-loop pauses for high-stakes answers. Hallucinations don't go to zero, but with these layers they get rare enough to trust at scale.

Documents inherit ACLs from the source system. At retrieval time the user's identity scopes the query — finance docs only return for finance roles, customer PII only for permitted teams. The vector store and graph both carry per-chunk ACL metadata so a user can never get an answer grounded in chunks they shouldn't see. For sensitive workloads ZeroTwo also supports on-premise model routing so the generation step never leaves your boundary.

Each source connector has its own refresh cadence (real-time webhook, hourly, nightly, weekly). At retrieval time the chunk timestamp is part of the ranking signal — recent chunks outrank stale ones. For high-velocity sources (tickets, chat) the index is incremental and near-real-time. For slower-moving sources (policies, contracts) batch refresh is fine.

Free tier with daily message and retrieval quotas across GPT-5, Claude Sonnet 4.6, Gemini 3 Pro, DeepSeek R1. Pro at $29.99/month removes caps, unlocks all 60+ models, and adds longer contexts. Pro 2x at $59.98/month doubles capacity. Ultra at $120/month is for full-time analyst workloads. Enterprise tier (custom) covers SSO, DPA, data-residency, on-premise routing.