Connect your systems and zenith learns your company's data, code, and operational knowledge as a knowledge graph. Answers come with evidence, every execution passes a preview → approve → execute → verify gate, and the whole journey is recorded in an audit trail. Whichever AI model you use, control and proof of execution live in zenith.
Demos are everywhere; production agents are rare. The bottleneck isn't the model, it's the foundation — agents don't know your company, mistakes can't be undone, and nobody can prove who executed what and why. Zenith solves all three in one platform.
Monitoring tools observe your systems 24/7, zenith organizes anomalies and proposes fixes, and on approval it executes data changes or code changes as pull requests — then verifies the outcome. Every turn of the loop builds company knowledge, so the same problem is auto-diagnosed the second time.
Each cycle refreshes your company's knowledge graph and operational baselines. The same class of problem is diagnosed automatically from the second occurrence.
Alerts become organized incidents → similar-case search → proposed fixes → on approval, a data change or a pull request. If a change gets reverted, rework starts automatically.
Every change is recorded and recoverable. Request → verdict → approval → execution → verification connect in one record, generating audit and compliance evidence automatically.
Chatbots stop at answers, coding agents don't know operations, and rule-based automation can't judge. Zenith combines company-knowledge learning, governed execution, code changes, and self-learning in one system — and leaves all of it as auditable records.
A general chatbot tells you "here's how" and stops. Zenith actually does it — through gates, leaving records.
Coding AIs stop at the PR; automation tools stop at data changes. Zenith runs alert → decision → data/code execution → verification as one flow.
Every cycle accumulates as your company's operational knowledge — the same kind of problem is auto-diagnosed from the second time.
Connect your systems and zenith reads schemas, code, and docs to build a knowledge graph and draft operations. Adding a project is configuration, not development — and onboarding to a first answer takes 30 minutes, no engineers required.
Zenith performs only the operations your company registered. The AI never invents queries, so "accidentally deleted everything" is structurally impossible.
Every data change first shows "this many rows affected, here's the result." After execution, the outcome is re-checked against the source system before the work closes. When judgment is impossible, nothing executes.
Bad executions are instantly restored from the auto-saved prior state. Who changed what, when — every action is recorded for complete forensics.
Zenith executes approved work in an isolated environment and produces only a pull request. Merging and deploying stay your decision — AI can never write to production branches directly.
MCP-compatible clients (Claude Desktop, Cursor, and more) connect to zenith directly. Whatever the agent, it passes the same gates and the same permission ceilings.
Schemas, code, docs, and ops history are learned into a graph and refreshed daily. Key claims in AI answers carry citations — and when evidence is insufficient, zenith says so instead of guessing.
Monitoring alerts become incident cards automatically. Secrets are masked at ingest; similar cases and candidate fixes appear on one screen.
AI proposes → an authorized human approves → AI executes → outcomes are verified. A workflow with separate approval and execution keeps concurrent work conflict-free.
Compose department-level AI agents on screen — goal, data scope, tools, and approval policy. Every agent you build inherits the platform's permissions, gates, and audit automatically.
The decision layer holds no execution credentials. Only the data-execution layer reaches databases, only the code-execution layer reaches repositories, and each layer is isolated with least privilege. Gates, audit, and masking are enforced in one place — controls don't drift as you scale.
Knowledge, control, execution, and proof — operating as one platform.
New capabilities are recorded here as soon as they are verified in production.
Sign-up stays open, but features require approval. Unapproved users see a request-access screen; admins grant and revoke from the console. Email/password login added.
The full console now runs inside the macOS app. Parallel sessions, live intermediate steps, smooth stop/resume, and follow-up instructions mid-run — local data connections and a governed workspace in one app.
Run an approved task and zenith makes the code change in an isolated environment, opens a pull request, and keeps the task in sync with PR status. GitHub and Azure DevOps supported, duplicate-run protection, fully recorded.
Keyword and semantic search combined — a question like "where's the settlement logic?" finds code, tables, and documents in one pass. Every knowledge item carries an AI summary.
The organization layer — the unit of contracts and policy — landed in the console. Notion / Jira ingest only the pages you pick, not entire workspaces; documents outside your selection are cleaned up automatically.
The risk of agent automation is "AI acting on its own." Zenith guarantees these four properties — in architecture, not prompts.
Beyond registered operations, the AI cannot invent queries or API calls. When judgment is impossible, nothing executes.
"This many rows, this result" comes first; execution requires approval. Code ships only as PRs — AI cannot write to production branches.
Approval is restricted to designated authorities. If a permission check fails, the platform falls back to the narrowest access — no error path escalates privileges.
Request → verdict → approval → execution → cost recorded as one record. Bad executions restore instantly; unrecorded executions cannot happen.
Sign in to get a workspace; once approved, connect your systems and zenith learns your company automatically. From connection to first answer in 30 minutes.