An AI that knows your data model, your interface, and your actions.
Most AI in operations stops at suggestion — it can talk about your business, but it can't reach into it. Core AI runs inside the platform, with grounded access to your model and the same actions your team uses. It reads what's on screen, references real records, fills forms, and executes Core actions behind a confirmation step.
A chatbot bolted onto your data isn't operational AI. It's a search box.
Generic copilots don't know your entities, your states, or your rules. They guess at IDs, hallucinate fields, and stop short of doing the work — because they don't have safe access to the systems of record.
The result: AI that drafts replies but never closes the loop. The judgment moves faster; the operation doesn't.
Operational AI has to be grounded in the same model your team works from, and it has to be allowed to act — under the same permissions, audit trail, and approvals as anyone else.
Grounded context. Real references. Actions that actually run.
Operational logic awareness
Reads the operational logic behind your Core deployment — entities, workflows, and rules — so answers reflect how your business actually runs, not a generic template.
Current page context
Knows what you're looking at — the record, the role, the screen. Ask a question and the answer is scoped to where you are, not a fresh chat with no memory.
Model & schema access
Reads every viewable property and the schema behind it. Field types, relationships, allowed states — all available to ground responses and prevent hallucinated values.
Domain object linking
References resolve to actual records. "This client's last invoice" is a link to the invoice — not a paraphrase. You can follow it, edit it, act on it.
Form editing
Fills, validates, and updates forms through the same UI rules your team uses. No bypass paths — every change runs through Core's validation.
Action execution with approval
Runs Core actions — create a quote, escalate a case, dispatch a job — behind a confirmation step. Every execution lands in the audit trail attributed to both you and Core AI, the way a git commit credits a co-author, so it's always clear who approved what and who carried it out.
Permission-aware
Sees only what the calling user is allowed to see. The model inherits role-based access from Core — no separate AI permissions to manage or get wrong.
Core AI multiplies the value of everything else.
Data makes Core AI accurate — grounded in your model, not a generic vector store.
Workflows extend Core AI outward — let it participate in routing, drafting, and triage steps.
Applications gives Core AI a surface — the same screens your team uses, with the agent embedded.
Security governs every read and every action — the AI's writes land in the same audit trail as anyone else's.
We deploy AI inward — read-only first, then trusted to act.
Start with read access scoped to one role. Let the agent answer grounded questions and surface records — no writes, no actions. Build trust against real work.
Expand to forms and actions one workflow at a time, each behind explicit approval. The agent grows into the operation the way a new hire would — visibility first, judgment second, authority last.