2026-03-13 — Momor for Enterprise
A healthcare researcher can't paste a patient's chart into a public AI tool to look something up. A wealth manager can't run a client's portfolio through a system where the data gets used to train the next model. An attorney working a sensitive deal can't send case documents through infrastructure her client never agreed to. These aren't edge cases — they're the daily reality for entire industries, and it means millions of people who need AI-powered search the most are the ones who can't use any of it.
That's the problem Momor for Enterprise exists to solve.
The Same Engine, Different Wiring
Momor's core works in three stages: figure out what you're actually asking, gather the right sources, and synthesize a real answer. Every enterprise deployment runs on that same pipeline with no stripped-down version or quality compromise.
What changes is what gets mounted to that pipeline. Think of it like a pegboard: the board is the same, but you swap out which tools are hanging on it depending on the vertical. A real estate team's deployment has tools for MLS lookups and property data, a legal firm's has tools for case research and document synthesis, a compliance desk's has tools for entity screening and due diligence — all running on the same core engine with different domain-specific actions wired in.
Seven Verticals
We've built out seven vertical deployments: real estate (including high-profile buyer confidentiality for luxury transactions), legal (attorney-client privilege for small and mid-size firms), healthcare (HIPAA-grade clinical intelligence), wealth management (SEC/FINRA-regulated portfolio analysis), government procurement (RFP monitoring across fragmented portals), investigations and intelligence (case synthesis with full source confidentiality), and KYC/AML compliance (due diligence workflows that don't expose the data being screened). Each has its own solution page, its own action set, and its own trust configuration.
Tenant Isolation That Means Something
Tenant isolation happens at the data layer — one client's data is physically separated before the AI pipeline ever sees it, not through application-level routing where a bug could mix contexts but through hard boundaries at the storage and query layer.
Enterprise clients also control which AI providers touch their data. If your jurisdiction or compliance posture requires that data not cross certain borders or that specific providers are excluded, you configure that per tenant. The supported providers (Anthropic, OpenAI, Google, DeepSeek, Groq, Together) can each be selectively enabled or disabled, and zero-data-retention agreements are in place with all of them — queries are processed and discarded, and no provider trains on customer data.
The API
We're opening up the full pipeline as an API with over 113 endpoints covering query orchestration, multi-turn conversation threads, document management, connectors, entity and profile management, case and workflow tracking, structured outputs, automations, comparison and diff, reports, and full tenant administration including security and usage management — the entire engine as a programmable surface.
The Contact Form
If you're in one of these industries and what's above sounds like what you've been waiting for, there's now an enterprise inquiry form on the site. We built this because the tools that exist weren't an option for you, and we'd rather talk about your specific deployment than write another paragraph here.
What this means for you: If you work somewhere that's been locked out of AI search because of what it does with your data, the same engine running momor.ai is now available as a private deployment with your data staying entirely yours.