A/R Agent

Stop working denials after they land. Elixir scores every account and every claim as it is created, so your team spends its day on the money actually at risk.

Explore Live Demo View Architecture

Claims scored by Kratu AI and routed into flagged and approved queues

Your next denial is often predictable

Four models read the account, the claim and the payer, then hand your team a decision instead of another dashboard.

Risk you can act on
Every account and every claim arrives with a 0–100 score and a clear band. No sorting by age, no guesswork.
Deterministic Scoring
Cash you can commit to
Know the window each claim will pay in, and exactly how much to trust it.
Payment Windows
Appeals already written
Root cause, ANSI codes, next steps and a ready-to-send appeal letter, in one pass.
Zero Draft Latency
Nothing new to learn
The intelligence sits in the patient header and claims table your team already works in.
Native Embedded UX

Precision Engine

A/R Agent capabilities

Wherever you see the purple sparkle, Elixir has already done the analysis for you.

Pre-Claim
Account Revenue Risk
How much of this account is genuinely at risk, scored 0–100 with collections propensity, before you open a single claim.
Scrubbing
Claim Recommendation
Rejection probability with the exact fields to correct, before the claim is submitted.
Claim Line
Denial Risk Score
The odds this claim comes back denied, on the claim line, while there is still time to fix it.
Remittance
Denial Intelligence
Why the claim was denied, what to do about it today, and whether an appeal is worth filing.
Cashflow
Predicted Days to Pay
When the money actually arrives, as a window you can put in a forecast.
Appeals
Appeal Letter Drafting
A payer-ready letter drafted for the claim in front of you. Edit, export and track it as sent.
Closure
Write-Off Assessment
When a claim is not worth chasing, the evidence to close it cleanly and defend the decision.
Patient Desk
AI Patient Insights
A written account summary with five next actions in priority order, below the patient header.

End-to-End Cycle

Kratu AI prepares the work. Your team decides.

One account, followed from the first look at the balance through submission, follow-up and appeal.

Before Submission

Opening the account and building the claim

Step 01
Account Revenue Risk
Scored 0–100 with probability bands before opening.
Step 02
AI Patient Insights
Instant account summary and top 5 priority actions.
Step 03
Claim Recommendation
Rejection propensity checks and specific field alerts.
Step 04
Your team
Billers verify recommended fixes and submit cleanly.

After Submission

Following up on what is outstanding

Step 01
Denial Risk Score
Claim-line likelihood rating while in adjudication.
Step 02
Predicted Days to Pay
Payer-specific arrival window for defensible cash forecast.
Step 03
Your team
Prioritizes follow-up on high-risk, delayed encounters.

After a Denial

Deciding and appealing

Step 01
Denial Intelligence
ANSI code breakdown, viability check & write-off audit.
Step 02
Your team
Approves appeal pathway or executes justified write-off.
Step 03
Appeal draft
Complete payer-specific clinical argument compiled.
Step 04
Review and send
One-click electronic submission or print export with tracking.

Pre-encounter Metric

Account Revenue Risk

Spot the accounts that will cost you, first

Kratu AI continuously evaluates insurance eligibility, past encounter patterns, deductible depletion, and historical collections propensity. Every account surface presents a clean 0–100 score classified into actionable high, medium, and low revenue risk tiers before any clinician or biller enters the file.

Low Risk (0–30)
Moderate (31–65)
High Risk (66–100)

Example patient record header for Lando Norris showing Account Revenue Risk High 82 and collections propensity Medium

Synthesized Briefings

AI Patient Insights

The score says attention. This says what to do.

Raw figures alone create cognitive fatigue. Kratu AI synthesizes months of patient charts, electronic remittance advices, and payer rule shifts into a concise written summary with five immediate, sequenced tasks directly beneath the patient header.

Example AI Patient Insights panel: a patient summary, key drivers and five recommended next actions

Pre-Submission Scrubbing

Claim Recommendation

Catch the errors before the claim leaves the building

Standard clearinghouse scrubbers rely on rigid syntax checks. Kratu AI applies deep clinical coding context, pinpointing missing modifiers, incompatible diagnosis pairings, or missing medical necessity flags before batch submission takes place.

Key Advantage

98.4% first-pass clean claim acceptance rate across all major national commercial and managed Medicaid payers.

Example Claim Recommendation Matrix: 60% rejection probability, three issues identified, three recommendations and a line item analysis

Dynamic Prioritization

Denial Risk Score

Work the claims that may be denied, not the oldest ones

Legacy revenue cycle workflows sort follow-up queues by aging (30, 60, 90+ days). Kratu AI inverts this obsolete paradigm. Claims are ranked by dynamic denial propensity so collectors address high-dollar exposures before contractual filing deadlines expire.

Example Active Adjudication Queue listing seven claims with their Denial Risk scores

Automated Adjudication Analysis

Denial Intelligence

Every denial, already worked out

When a payer issues a denial, Kratu AI immediately translates cryptic ANSI remark codes into clear clinical english, cross-references payer policy updates, evaluates recovery viability, and compiles an automated appeal letter with full supporting chart references.

Generates fully cited payer-ready appeals in seconds

Example denial root cause classification (Missing Authorization) with ANSI codes, 78% confidence and three recommended next steps

Cashflow Predictability

Predicted Days to Pay

Forecast cash with a number you can defend

Revenue teams no longer rely on static averages. Kratu AI models seasonality, weekly batch cadences, and specific adjudication turnaround per clearinghouse to calculate defensible expected remittance dates.