Enterprise language AI needs more than translation quality. Buyers should review identity, SSO, audit logs, data residency, APIs, usage visibility, and support.
Enterprise language AI looks like a translation purchase from the outside. It's actually a platform decision: how identity is handled, where data lives, which workflows are allowed, what audit trail you keep, and which tools your team will actually use without working around them.
Coverage: Which speech and translation languages are supported? Which are official versus experimental versus model-specific?
Workflow surfaces: Does the tool handle documents, messages, calls, keyboard entry, history, and assistant tasks — or just one?
Identity controls: SSO (SAML / OIDC), RBAC, organization management, audit logs, retention windows.
Data path: Where are text, audio, files, and translations processed and stored? Can you keep them in your cloud, your region, or your own building?
Cost visibility: Can finance see token usage, seat usage, and per-feature cost — or just a flat monthly invoice?
Governance: Can administrators approve which workflows are allowed for regulated data (clinical, legal, financial)?
If the tool only exists as a web app, half the team won't use it. Enterprise users write across ticketing tools, CRMs, documents, inboxes, chats, and live calls. The language layer needs to meet them in those surfaces, not require them to leave their work.
Vavus splits this across three product surfaces sharing one account: Vavus AI for app-driven workflows (translation, messaging, calls, files, history); Vavus Keyboard for inline dictation and translation in any text field; VClaw for persistent assistant tasks (documents, OCR, code, structured tools).
"Quality" benchmarked on a demo pair only.: Ask for accuracy on the language pairs your team actually uses, not just English ↔ Spanish.
"On-prem" that's really single-region cloud.: Read the deployment options carefully — managed cloud, dedicated cloud review, and air-gapped on-prem are three different things.
Audit logs without a retention SLA.: "We log everything" doesn't help if logs roll over after 30 days.
Per-seat pricing for a tool 10% of seats will use heavily.: Look for usage-based fallback or pooled tokens.
Identity (SSO and role management), data path (where translations are processed and stored), BAA or DPA needs, support response SLA, pricing model, and administrator visibility into usage.
Because staff write inside many tools — Salesforce, Outlook, Jira, Notion, Slack, custom CRMs. A keyboard reduces friction without forcing every department to migrate into a new app. Adoption is the single biggest predictor of whether a language AI purchase pays off.