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Reliable Group

HOW WE BUILD

Automation first, then AI, then people for judgment.

Your AI operating partner for mid-market healthcare: rules-based automation for the volume, AI for suitable unstructured work, people accountable for decisions.

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Three layers in the order we build them. Automation at the base takes rules-based work first and moves data between systems. AI above it takes suitable unstructured work, reading, drafting and classifying while a person checks. People at the top, marked in orange, hold judgment and accountability: decisions, exceptions and sign-off.

THE DEFINITION

What does AI-native mean when an auditor asks?

A department built in a set order: rules-based automation first, AI for suitable unstructured work, people for judgment and accountability. Every step is logged, so an audit asks for a trail you already have. It does not mean more software. Clinical decisions stay with your clinicians.

  • Defined inputs, auditable outputs
  • A named owner for every decision
  • Your data, your governance
  • Clinical decisions stay clinical

Four kinds of work that stay with people

  • Judgment

    Decisions that need context the system does not have, or where a wrong call costs more than a review.

  • Exceptions

    The cases that do not fit the pattern. The agent surfaces them, a person resolves them.

  • Relationships

    Clients, members, providers, payers and auditors. Anything where the work is the relationship.

  • Escalations

    Work routes to a person on set triggers: a missing document, a dollar threshold, a rule conflict or a member complaint.

Illustrative scenario, not a client result: one prior authorization in six steps, all done by hand today. The request waits in a shared inbox, records are hunted across systems, clinical notes are read against policy, the decision letter is drafted from scratch, exceptions are chased by email, and a clinician decides with the audit trail assembled later. Badges show automation taking the first two steps, AI assisting the next two, and people keeping the exceptions and the decision.

ONE WORKFLOW, ILLUSTRATIVE

One prior authorization, before and after

An illustrative scenario, not a client result. Today every step is done by hand. Rebuilt, rules-based automation takes intake and record pulls, AI reads notes against policy and drafts the letter, and exceptions route to a named person. A clinician decides.

  • Exceptions route to a named person
  • Every case logged and monitored
  • Run cost tracked per case
  • A spend ceiling with an alert

Four tests before work goes to an agent

  • Defined workflow

    A documented start, a documented end, and a success criterion that can be judged without interpretation.

  • Context that stays

    Case history and past decisions live in your systems, so the next case starts with them and nothing leaves with a person.

  • High volume

    The work happens often enough to justify the build against its run cost per case.

  • Auditability

    Every output can be logged, reviewed and audited, and the trail survives the engagement.

Before and after view of where a department's cost sits. Left, cost from volume and turnover: hiring to meet volume, month-end overtime, re-keying rework, training new hires, temp staff for backlog, audit prep by hand, missed turnaround and know-how that leaves. Right, cost designed around the work: automation takes the volume first, AI agents run the defined workflows, every step is logged, and a smaller team holds the judgment, marked in orange.

THE AI-NATIVE ORG CHART

Cost to serve that climbs with every new claim, member or client

Most organizations add AI tools to a department shaped before the tools existed. The hours barely move, so cost still tracks volume. We redesign the department: a smaller team and a set of agents, each with a named owner, a reviewer and a person who takes the exceptions.

  • People keep judgment and relationships
  • Agents run the defined workflows
  • Automation moves work between systems
  • The chart defines what gets built
How we start
Five questions your auditor and board ask of every agent, answered in writing before it runs a case: who owns it, a named person in your organization; what it may do, the workflow and the boundary; what it logs, every input, output and review; what it keeps, case history in systems you control; and who signs off, every case or a sampled rate.

GOVERNANCE

Who answers when an agent gets it wrong?

Before an agent runs a single case, five answers are written down: who owns it, what it may do, what it logs, what it keeps and who signs off. That is what your auditor, your compliance lead and your board will ask to see.

How we handle regulated data
Table of what five functions cost today and what changes once rebuilt: claims intake backlog and missed turnaround, prior authorization requests aging toward deadlines, enrollment errors found at month end, billing follow-up cash sitting in aging receivables, and new providers waiting to bill on credentialing. In each, automation or an agent takes the routine work and a person decides; in prior authorization a clinician decides any denial.

FIVE FUNCTIONS

Five functions where backlog, cash and audit risk sit

In each function the routine volume moves to automation and agents, and the discovery shows how much added volume they absorb before you hire. People keep the judgment calls, the disputes and the sign-offs.

  • Automation sorts and routes the volume
  • Agents compile, validate and follow up
  • People take disputes and sign-offs

Medicare distribution, anonymized

Healthcare services

Sales capacity held and the business grew while commissions, HR and onboarding were rebuilt

A field marketing organization placing Medicare plans for 225,000+ lives through roughly 1,500 agents. Reliable Group rebuilt commissions, HR and onboarding around automation and AI, and gave agents a voice assistant for plan comparison and compliance. Sales capacity held and the business grew through the year, while the corporate team went from about 450 to under 200.

Grew, capacity held

One year: the business grew and sales capacity held. The corporate team went from about 450 to under 200.

anonymized realized

One anonymized engagement in Medicare distribution. Shown for the operating model, not as a promised outcome.

  • Growth and sales capacityanonymized realized

What transfers: The operating model this page describes: defined workflows run by automation and AI agents, with a smaller team on exceptions and decisions.

Read the full case

Where AI efforts quietly break

  • AI spend nobody budgeted.

    Usage costs grow with volume unless someone caps them. Each agent runs against a cost per case, a ceiling and an alert.

  • Operations that cannot pause for a rebuild.

    We start with one department, inside the systems you already run. Your team keeps working and deciding while we rebuild around it.

  • Pilots that never get past the demo.

    The pilot ran beside the old process, so nothing changed. We redesign the workflow, the staffing and the ownership together.

We run this way ourselves

We do not recommend this from theory. We run our own recruiting, compliance and finance this way: defined inputs, logged outputs, a named owner at each decision and our data in our systems, so we know where it breaks before it touches your queue.

Next step

Stop guessing how your operation works.

Tell us which department eats the most hours. We will say candidly whether measuring it is worth it.

Talk to usHow we start