Achieve more across every office
Transform departmental operations with autonomous AI agents, bespoke applications, and custom-trained ML models.
Agents
Autonomous AI teammates, built for the enterprise.
Agents are the automation layer of the platform. They run on top of your ontology — listening for events, coordinating with users in different roles, and taking action on the work that shouldn't require a human to do it.
Powerful
- Long-running and autonomous
- Self-configuring — talk to your agent to shape its behavior
- Fully observable — reports, evals, and deep dives on activity, on request
- Approval gates configurable to any risk tolerance
- Massive tool surface: MCPs for Slate, Workday, Banner, Salesforce, Canvas, TMA, plus ontology querying, code execution, browser, documents
Multichannel
- Listens across every channel (inbound email, SMS, phone, webchat, system triggers, scheduled jobs)
- Reaches out across every channel (email, SMS, voice, chat, in-app)
- Always on, always listening, always responsive
Multiparty
- Same agent serves admins, staff, and students simultaneously — behavior determined by role
- Data access enforced at the database layer, not by prompt or policy
- An agent acting for a student can only see and do what that student can
- Zero-trust by construction — misconfiguration can't create a leak
Read the full security model
Applications
Any software your teams need.
Available instantly.
Give every team on campus the leverage of software built for how they actually work. Describe the tool — dashboard, workbench, workflow, portal — and it's built on your ontology, wired to real data and real actions, with enterprise-grade security and permissions.
Bespoke
- Any shape your team needs: dashboards, decision queues, deterministic workflows, internal tools, external portals, full business apps
- Tailored to your institution's exact processes
- Shipped in minutes, not months
Native to your ontology
- Sits directly on your data — every entity, every property available
- Uses ontology actions — trigger real work in source systems from inside the app
- Everything stays in sync automatically
Safe by construction
- SDK handles auth, permissions, and ontology access correctly
- Deployment pipeline verifies correctness before anything reaches production
- Production-ready from day one, no security review cycle required
Real Apps in Action
ML Models
Any prediction your institution needs.
ML models are the prediction layer of the platform. Trained on your ontology, their outputs publish back as properties on the entities they describe — available wherever they're needed, hours after they're requested.
Deeply engineered
- Agents crawl thousands of candidate features across your ontology
- Engineer new features and test interaction terms no data scientist would think to try
- Explore signal across the full graph of your institution before a single model is trained
- The deepest possible shape for your problem, every time
Battle-tested
- Dozens of model variants trained in parallel, not just one
- Benchmarked against cross-validation, holdout sets, and real-world backtests
- Only the proven winner is promoted to production
- Approval-gated before any deployment
Compounding
- Every output publishes as a new property in your ontology
- Immediately available to every agent, app, and future model
- Your institution's signal grows with every model — and every new model sees a richer world
