The AI
coworker
for the
enterprise.
ZenSearch joins your team: it answers with citations, triages tickets, drafts replies in your voice, and runs jobs unattended — asking before it does anything consequential. On your infrastructure.
A support ticket arrives. The coworker handles it.
How It Works
Delegate like you would to a person.
No pipelines to build, no prompts to engineer. Hand over a job the way you'd hand it to a teammate.
01
Give it a job
Ask in Slack, Teams, or the web. Email it. Put it on a schedule. Or wire it to events from Jira, Zendesk, GitHub, Salesforce, and your meetings.
02
It plans, then works
It breaks the job into steps and executes across 100+ tools and your entire knowledge base. Reads run freely — every claim carries a citation.
03
You approve what matters
Consequential actions pause on a risk-tier policy and route to the right human — in Slack, by email reply, or on the web. The run resumes the moment you decide.
04
It delivers — and learns
Results land where you work, with sources and cost attached. Successful runs distill into procedures it reuses the next time the job comes up.
Delegated Work
The work teams hand over first.
Support
"Triage every new ticket: search the knowledge base, draft a cited reply, and ask before posting."
Engineering
"Watch Sentry and Datadog overnight. Open an incident and page on-call when something real breaks."
Revenue
"Every Monday at 7, brief #leadership: pipeline changes, slipping deals, churn, and sprint status."
The Job Description
What your AI coworker can do.
One coworker, many jobs — research, action, and follow-through across every system you already run.
Research & answers
Agentic search across every connected source — hybrid retrieval, cross-source corroboration, reranking, verified citations, and a confidence score on every claim.
Real actions
100+ tools across Jira, Zendesk, ServiceNow, HubSpot, Google Workspace, Microsoft 365, and four SQL databases. Reads run freely; writes wait for approval.
Works unattended
Automations run on cron schedules, inbound email, webhooks, and meeting events — pausing, resuming, and retrying on their own. No babysitter required.
Writes in your voice
Drafts and sends from your own account, sounding like you — or with explicit AI disclosure where policy requires it. Your team picks the mode.
Learns the job
Remembers facts, preferences, and procedures. Runs that go well become reusable playbooks — your coworker is better in month two than on day one.
Knows your org
Reads your org chart, on-call schedules, and service ownership — so approvals, escalations, and pages always reach the right person.
Supervision
Autonomy you can actually manage.
A coworker is only useful if you can trust it with real systems. Every action is risk-tiered, budgeted, attributed, and logged — and the consequential ones wait for a human.
- Risk tiers T0–T5 on every tool, with per-team policy overrides
- Approvals decided from Slack, email reply, or the web
- Approver routing through your org: manager, on-call, then team owners
- Per-run and per-team-day cost ceilings, with a live cost meter
- Automated writes carry attribution — readers know an agent wrote it
- Every action logged: full reasoning trace and audit trail per run
update_zendesk_ticket
Post a reply to ticket #4821 — "Exports fail with a timeout" — including the workaround from KB-2204.
Surfaces
One coworker. Everywhere you work.
The same coworker, the same memory, the same permissions — in whichever tool the conversation is already happening.
And an inbox of its own — email it a job the way you'd email a colleague.
Context
It knows what your company knows.
A coworker is only as good as its context. ZenSearch ingests your sources permission-aware — so it answers and acts with exactly what each person is allowed to see.
Security & Sovereignty
A coworker that never leaves the building.
Run it on your own infrastructure with complete data sovereignty — on-premise, private cloud, or fully air-gapped, down to the model it thinks with.
- On-premise and air-gapped deployment options
- Bring your own LLM — including fully local models
- Fine-grained, document-level RBAC synced from source systems
- Identity federation (Slack, Teams, Google, OIDC, SAML)
- Complete audit logging
- Your data is never used to train models
Enterprise
Built for the enterprise.
ZenSearch is delivered as an enterprise platform — every deployment is shaped to your data, identity stack, and compliance program. Talk to our team and we'll scope the right deployment together.
FAQ
Frequently asked questions
Short answers to the things teams ask most.
What is ZenSearch?
- ZenSearch is an enterprise AI coworker — an AI teammate that connects to your company's tools and knowledge (Confluence, Slack, GitHub, SharePoint, Google Drive, Salesforce, databases, and 10+ more sources) and does real work: answering questions with citations, triaging tickets, drafting replies in your voice, updating records, and running scheduled jobs unattended. Every consequential action is approval-gated and audit-logged. Built for large organizations, available as managed cloud or self-hosted (including fully air-gapped) deployments.
How is an AI coworker different from a chatbot or copilot?
- A chatbot answers questions; a coworker takes responsibility for work. ZenSearch accepts jobs the way a colleague would — through chat, email, cron schedules, and event webhooks from Jira, Zendesk, GitHub, Salesforce, and your meetings — then plans the work, executes it across 100+ tools, pauses for human approval on consequential actions, and delivers the result where you work. It also learns: successful runs are distilled into reusable procedures, so it gets better at recurring jobs over time.
What kinds of work can ZenSearch actually do?
- Research with verified citations across 18+ connected sources; natural-language queries over PostgreSQL, MySQL, ClickHouse, and SQL Server; drafting and sending email from your own Google or Microsoft account in your voice; creating and updating Jira, Zendesk, ServiceNow, and HubSpot records; posting to Slack and Teams; watching Sentry and Datadog; and paging on-call through PagerDuty. Reads run freely — writes are gated by risk-tier policies and human approval.
How do humans stay in control?
- Every tool carries a risk tier from T0 to T5, with per-team policy overrides. Consequential actions pause the run and route an approval request to the right person — a preferred approver, the on-call engineer, or a manager from your org chart — decidable from Slack, an email reply, or the web. On top of that: per-run and per-team-day cost ceilings, attribution footnotes on automated writes, full reasoning traces, and complete audit logging.
Can ZenSearch be deployed on-premise?
- Yes. ZenSearch supports single-machine Docker deployments, production Kubernetes clusters, and fully air-gapped installs where no data ever leaves your network. Bring-your-own-LLM is built in via a Model Gateway that supports OpenAI, Anthropic, Cohere, Groq, OpenRouter, Azure AI Foundry, Amazon Bedrock, and any OpenAI-compatible endpoint including local Ollama.
Is my data used to train AI models?
- No. Your data is never used to train models. Cloud customers route through major LLM providers under enterprise data-protection agreements, and on-premise customers retain full control — including the option to run entirely on local models via Ollama or any OpenAI-compatible endpoint.
Get Started
Make
the hire.
Your team's AI coworker — in Slack, Teams, email, and the web. Ready in minutes.