One screen over the systems your teams already run.
An enterprise interface is a purpose-built front end that sits over several systems of record and gives one role the decision they need, with the reasoning and the source attached. It does not replace your ERP, CRM or claims platform. It removes the eleven tabs a person opens to do one job, and it enforces your permissions model on every screen.
- Build this when the data exists but the decision takes too long — not when the data is missing.
- On a revenue-cycle deployment across 600,000+ weekly claims: collections up 3%, liquidation rate up 12%.
- The interface is where AI becomes reviewable. Every recommendation shows what it was based on.
Six jobs that currently need eleven tabs
The pattern is always the same: an experienced person is fast and a new person is slow, and nobody can say exactly why.
Next best action on a queue
The agent sees the account, the recommended next step, and the diagnosis, payer and historical activity it was grounded in. Expertise that used to take a year to build becomes visible on the screen. In production at scale today.
The file, pre-read
Financials, prior decisions and policy exceptions assembled into one view with the risk factors surfaced and cited. The underwriter spends the hour deciding rather than assembling.
Dispatch with the reason shown
Skills, parts availability, SLA exposure and travel weighed into a recommended assignment the dispatcher can override in one click, with the override recorded as training signal.
The exception cockpit
Late inbound, short stock and at-risk customer orders in one prioritized list ranked by revenue exposure rather than by timestamp, with the recommended action attached to each row.
The account, on one screen
Billing, tickets, usage and contract terms merged into a single view so an agent stops asking the customer to explain the history the company already holds.
The number, with its lineage
A metric leadership actually acts on, showing the definition, the source query and the last refresh, so the meeting argues about the decision rather than about whose figure is right.
Five mechanisms, in this order
- One role, one decisionWe scope the interface to a single job a single role does many times a day. Interfaces that serve everybody serve nobody.
- Your permissions, not oursSSO, role- and attribute-based access control and private endpoints, enforced per screen and per field. The interface can never widen someone's access.
- Recommendations that show their workEvery suggested action carries what it was grounded in. A recommendation a supervisor cannot audit will not be trusted, and untrusted software gets abandoned.
- Guardrails at the speed of the queueHallucination guards, throttling and fallbacks sized to real throughput. On one deployment, matching time came down from 10 seconds to a quarter of a second.
- Override as a signalWhen a person disagrees with the system, the override is captured with its reason and feeds the evaluation set. Disagreement is data, not failure.
600,000+ claims a week, decided on one screen
Client names and locations are withheld; figures are drawn from delivery records and approved business cases.
Next-best-action for 600K+ weekly claims
Scaling expertise across 70,000+ diagnosis codes and 1,000+ agents was nearly impossible. High attrition and long training curves led to inconsistent claim handling, and unstructured agent notes broke both audits and automation.
- A PHI-safe next-best-action recommender grounded in diagnosis codes, insurance metadata and historical claim activity
- A comment-enhancement engine that structures agent notes into clean, audit-ready logs
- A vectorized claim-allocation matcher that cut comparison time from 10 seconds to 0.25 seconds
- Hallucination guardrails and queue-based throttling to hold accuracy at scale
Delivered by a Scale Pod
Read the full case →Adoption is the whole game
An agent runs whether or not anyone likes it. An interface only works if the person in the seat chooses it over the eleven tabs they already know. That changes what gets built: the reasoning is visible, the override is one click, and the screen is designed around the one decision rather than around the data model behind it.
It is also where a domain SME earns their place in the pod. A retired operator from your industry will tell you in ten minutes which field the experienced people actually look at first.
Three times we will tell you to fix something else first
- The underlying data is wrong. A better screen over bad records makes the errors travel faster and reach more people. Fix the source.
- The decision is genuinely rare. If the job happens twice a week, the tabs are annoying but they are not a project.
- Your platform vendor is about to ship it. If the capability is on your ERP or CRM roadmap for this year, take theirs. We would rather build the thing they will never build.
FAQ
What to ask before you build another screen
Isn't this just a dashboard?
A dashboard reports what happened. An enterprise interface is built around a decision someone has to make next, shows the recommended action with the evidence behind it, and lets them act without leaving the screen. If reporting is what you need, your BI tool is cheaper.
Does this replace our ERP or CRM?
No. Your systems of record stay the systems of record. The interface reads from them and writes back through their APIs under your permissions. Nothing runs on an OrbitumAI platform, so there is no second source of truth and nothing to migrate off later.
How do you handle access control across several systems?
Through SSO with role- and attribute-based access control enforced per screen and per field, plus private endpoints and tenant isolation. The interface can never show someone data they could not already reach in the source system.
What if our people ignore it?
That is the real risk, which is why the pod scopes to one role and one decision, makes every recommendation auditable, and treats overrides as signal rather than noise. Adoption against the agreed metric is what the guarantee is measured on.
Most AI pilots die in the demo. Ours don't.
We define one measurable metric before we write a line of code. If it hasn't moved inside the impact window, the pod keeps building until it does, at no additional fee. You never pay for AI that just sits there.
Scope a Proof Pod →One workflow. Real data. A number that moves.
Name the role and the decision they make forty times a day. That is where we start.
Scope a Proof Pod →