Consultancies
Deliver a strategy deck and leave. Cannot write production code.
Writes the code. Stays until it runs.
Forward deployed engineeringHealthcare Revenue Cycle
We embed engineers inside your revenue cycle until it runs on AI.
Compress DSO by 22+ days, push clean claim rates above 99%, and resurrect millions in distressed A/R — without adding billing headcount.
$15B+
Annual RCM outsourcing market
22 days
Average DSO reduction with AI
99.8%
Achievable clean claim rate
40%
Denial management cost reduction
One engagement. From the first day on the floor to production.
Revenue Cycle Operations01
What the engineer found
400-employee regional clinic network spanning 12 locations, $180M in annual net patient revenue, mixed payer portfolio with 45% commercial, 35% Medicare/Medicaid, 20% self-pay
DSO had crept to 61 days, trapping $12M in working capital. The billing department was requesting 8 additional FTEs ($520K annual cost) just to keep pace with volume growth. Clean claim rate was 91.2%, and the CFO was watching margin erode at 15bps per quarter.
02
What the engineer built
Revenue Cycle Operations
Neume deployed an AI-driven revenue cycle layer across the claim lifecycle: real-time charge validation, payer-specific denial prediction scoring, autonomous payment posting, and contract variance analysis. A Human-in-the-Loop medical billing team handled the 5% of claims requiring clinical judgment.
Built in the client’s repository. Runs on their infrastructure.
03
What the company got
DSO compressed from 61 to 39 days, freeing $7.2M in working capital. Clean claim rate reached 99.8%. The 8-FTE hiring request was eliminated entirely, and the existing billing team was redeployed to high-value payer negotiation and complex case resolution.
22-day DSO reduction with 30% decrease in billing department vendor spend
90 days from kickoff to full production
Every number is from a confirmed engagement. Names withheld under NDA.
01The model
Not a consultant. Not a dev shop. An engineer on your floor.
A forward deployed engineer is a senior engineer who works inside your company rather than for it. They sit with the claims processor, the dispatcher, the underwriter. They find the workflow where AI pays off first, build the system on top of the software you already run, ship it to production, and train your people to run and extend it. Then they move to the next workflow.
In 2019 the hard part of enterprise AI was the model. Today the models are commodity infrastructure. The hard part is the twenty-year-old ERP, the undocumented process, the compliance rule nobody wrote down, and the operations manager who has watched three digital transformations fail. That is an engineering problem, and it is solved on site.
Dev shops
Build what you spec. Never see your operations.
Sits in your operations. Finds what to build.
Hiring
Six months to hire one AI engineer who starts from zero.
Arrives in a week with patterns from 47 deployments.
SaaS tools
Horizontal products configured for nobody in particular.
Systems built for your data, your edge cases, your compliance.
We don’t consult. We engineer intelligence.
02How it works
Contact. Audit. First deliveries. Monthly.
From the first call to a system in production in six weeks. Then a retainer that scales up or down every month.
01 / Contact30 minutes
A call with an engineer, not a salesperson.
We find out whether there is a fit and which workflow to start with. No deck, no discovery workshop, no proposal cycle.
- Who
- Your COO, your head of operations, and a Neume engineer.
- We ask
- Where the work piles up. Which systems it lives in. Who reviews it today.
- You get
- A straight answer on fit, and the workflow we would start with.
Nothing to prepare. Bring the person who owns the process.
02 / AuditWeek 1
The engineer embeds with the teams doing the work.
Every workflow mapped and ranked by AI leverage. The output is a 90-day build plan with the first systems specified and ROI attached to each.
- 01Build first
End-to-End Claim Lifecycle Management
DSO: 55 days | Net collection rate: 95.2% | Initial denial rate: 10.5% | Charge lag: 3.8 days
- 02Queued
Payer Contract Performance Monitoring
Remittance audit coverage: 3% | Underpayment recovery: $180K/year | Renegotiation data: anecdotal
- 03Queued
Patient Responsibility Estimation & Collection
Point-of-service collection: 20% | Overall patient collection rate: 60% | Average time to first patient payment: 52 days
90-day build plan signed. First system: Revenue Cycle Operations.
03 / First deliveriesWeeks 2 to 6
The first system ships to production with human review switched on.
Real data, real compliance constraints, real users. This is the proof, before any retainer.
Human review
- WK 02All
- WK 04All
- WK 06Flagged
Starts at 100% review. Falls as the system earns trust.
22-day DSO reduction with 30% decrease in billing department vendor spend
90 days from kickoff to full production
04 / MonthlyMonth 2 onward
The engineer stays. Department by department, workflow by workflow.
Review thresholds fall as the systems earn trust. Scale the engagement up or down each month. Our revenue depends on it working, so we stay until it does.
- M2Prior Authorization Lifecycleshipped
- M3Claim Submission Pipelineshipped
- M4Inpatient Coding Workflowin review
- M5Compliance Audit & Investigation Managementscoped
3.2x
productivity, day 90
8x
at twelve months
0
data incidents
Embedded. Not engaged.
03Field reports
What production looks like when the engineer is in the building.
Three healthcare engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.
Before
400-employee regional clinic network spanning 12 locations, $180M in annual net patient revenue, mixed payer portfolio with 45% commercial, 35% Medicare/Medicaid, 20% self-pay
DSO had crept to 61 days, trapping $12M in working capital. The billing department was requesting 8 additional FTEs ($520K annual cost) just to keep pace with volume growth. Clean claim rate was 91.2%, and the CFO was watching margin erode at 15bps per quarter.
Built
22-day DSO reduction with 30% decrease in billing department vendor spend
Neume deployed an AI-driven revenue cycle layer across the claim lifecycle: real-time charge validation, payer-specific denial prediction scoring, autonomous payment posting, and contract variance analysis. A Human-in-the-Loop medical billing team handled the 5% of claims requiring clinical judgment.
Result
DSO compressed from 61 to 39 days, freeing $7.2M in working capital. Clean claim rate reached 99.8%. The 8-FTE hiring request was eliminated entirely, and the existing billing team was redeployed to high-value payer negotiation and complex case resolution.
Before
Multi-specialty physician group, 85 providers across 8 locations, $95M annual net revenue, high surgical volume requiring extensive prior authorization
Prior authorization delays were causing 22% of scheduled surgical procedures to be postponed or cancelled, representing $4.2M in delayed or lost revenue. The auth team of 12 FTEs could not keep pace with volume, and initial auth denial rates were 15%, triggering weeks-long peer-to-peer review cycles.
Built
Auth denial rate reduced from 15% to 0.8%, eliminating $4.2M in annual care delay revenue leakage
Neume deployed a predictive prior authorization engine that ingested clinical charts, mapped clinical indicators to payer-specific medical necessity criteria (InterQual/MCG), and auto-generated submission-ready clinical justifications. Certified clinical staff reviewed the 5% of complex cases flagged by the AI.
Result
Auth turnaround compressed from 14 days to 24 hours. Initial auth denial rate fell to 0.8%. Surgical cancellation rate due to auth issues dropped from 22% to 1.5%. The auth team was reduced from 12 to 5 FTEs, with the 7 redeployed staff handling expanded surgical scheduling.
Before
Regional health system, 3 hospitals and 22 ambulatory sites, $420M net patient revenue, aging A/R crisis with $15M sitting in the 120+ day bucket
The CFO was staring at an aging report showing $15M in 120+ day A/R — effectively dead equity. The internal billing team had deprioritized these claims because the per-claim cost-to-collect exceeded the expected recovery. The board was demanding a write-off that would hit EBITDA by 180bps.
Built
$3.8M recovered from written-off A/R at zero marginal OPEX — pure bottom-line cash
Neume deployed AI agents against the entire 120+ day A/R backlog. The system autonomously cross-referenced original claims against payer EOBs, identified correctable errors (missing modifiers, demographic mismatches, authorization reference gaps), auto-corrected claims, and batch-refiled. Human medical billing specialists handled the 8% of claims requiring clinical judgment or payer negotiation.
Result
$3.8M of previously uncollectible debt recovered in 90 days. Root cause analysis from the AI identified 4 systematic upstream process failures that, once corrected, reduced new claims flowing into 120+ day aging by 62%.
47+
Production systems deployed
6 wks
Average kickoff to production
3.2x
Productivity gain at 90 days
8x
At twelve months
0
Data incidents
The full report
Every healthcare department mapped before the engineer walks in.
The analysis our engineers work from: pain points, opportunities with ROI, workflows before and after, the regulatory constraints, and a phased roadmap.
Read the healthcare report8
Departments mapped
26
AI opportunities
25
Workflows, before and after
04AI native, defined
Five levels. The engineer moves you up one workflow at a time.
AI native means AI is inside the workflow, not beside it. Every recurring process has been examined for what an agent does and what a human must do. Every knowledge worker has a copilot wired to the company’s own data. And the people who work there can extend the systems after the engineer leaves. The audit tells you where you are today.
Audit · Week 1
Where does the company sit today?
- 0level
Curious
ChatGPT on personal accounts. No policy, no systems, no data connected.
- 1level
Experimenting
A pilot or two in a sandbox with clean data. Nothing in production.
- 2level
Operational
One or two systems live in one department, with humans reviewing every output.
- 3level
Embedded
AI inside the core workflows of most departments. Humans handle the exceptions.
- 4level
Native
New work is designed AI-first by default. Your own team extends the systems.
Most companies arrive at level 0 or 1. The first system takes them to level 2 in six weeks.
05What we build
Fourteen systems an engineer ships. Each one has a use in healthcare.
The audit decides which one comes first. Each brief covers what the system is, how it is built, and what the first weeks look like.
01
Autonomous Agents
Prior authorization, eligibility verification, and denial management are multi-step, multi-system workflows perfectly suited for agent automation. Agents reduce revenue leakage and accelerate cash flow.
Read the brief
02
Team Copilots
CDI copilots, coding copilots, and denial management copilots that improve documentation quality, accelerate reimbursement, and reduce revenue leakage across the revenue cycle.
Read the brief
03
Document Intelligence
EOBs, clinical documentation, prior authorization forms, and medical records require document intelligence that handles PHI with HIPAA-grade security while extracting complex medical coding data.
Read the brief
04
Decision Engines
Clinical resource allocation, utilization review, and operational capacity planning require real-time decision support that balances patient outcomes with operational efficiency.
Read the brief
05
Workflow Automation
Prior authorization, referral management, and clinical documentation workflows involve complex routing rules across clinical and administrative functions.
Read the brief
06
Predictive Analytics
Patient no-show prediction, readmission risk scoring, and resource demand forecasting help health systems optimize scheduling, staffing, and capacity planning while improving patient outcomes.
Read the brief
07
Conversational AI
Patient intake automation, appointment management, and clinical triage with HIPAA-compliant data handling.
Read the brief
08
Data Integration & ETL
Claims data normalization across payer systems, patient identity resolution, and clinical-to-billing data mapping benefit directly from semantic schema intelligence.
Read the brief
09
Knowledge Management
Health systems need instant access to current clinical protocols, formulary decisions, and committee rulings across facilities. Knowledge management reduces practice variation and improves compliance.
Read the brief
10
Compliance & Audit AI
HIPAA Privacy and Security Rules, HITECH enforcement, CMS Conditions of Participation, and state health privacy laws require continuous monitoring of PHI access and security controls across complex, distributed care delivery organizations.
Read the brief
11
Computer Vision
Medical imaging triage, pathology slide pre-screening, and clinical document digitization apply vision models to accelerate diagnosis workflows and reduce manual data entry in revenue cycle operations.
Read the brief
12
Process Mining
Clinical and administrative workflows (patient flow, revenue cycle, discharge) span multiple systems and stakeholders, making process visibility a persistent challenge that directly impacts patient outcomes and reimbursement.
Read the brief
13
AI Training & Enablement
Clinical and administrative staff require carefully calibrated AI training that addresses patient safety, HIPAA compliance, and the distinction between AI-assisted and AI-dependent decision-making.
Read the brief
14
Custom LLM Fine-Tuning
Domain-adapted models for medical coding, clinical note summarization, and prior authorization achieve accuracy levels that satisfy CMS compliance requirements and reduce denial rates.
Read the brief
Not sure which one?
If the work runs on documents, decisions, and handoffs, one of these applies. The audit tells you which to build first.
06Engagement shapes
Monthly. Scale up or down each month.
No minimum term. The shape is agreed on the call and can change as the roadmap does.
Two days a week
Fractional
One engineer, part time, one workflow at a time. The right shape for a 50 to 200 person company taking its first system into production.
Talk about this shapeOne full-time engineer
Embedded
One engineer inside one department, shipping a system every four to six weeks and training the team that runs it.
Talk about this shapeTwo to three engineers and a lead
Pod
Several departments at once, with a lead who owns the roadmap across them. For companies that want to move up the ladder fast.
Talk about this shape07Built for companies that cannot get this wrong
Your data. Your infrastructure. Your repository.
Four guarantees, in every contract, before any system goes live.
Human in the loop
Every system ships with a review layer your team controls. It starts at 100% human review and falls as the system earns trust. By month six most clients run 85 to 90% autonomous, with humans on the edge cases.
100% audited outputs
Data sovereignty
Your data stays on your infrastructure and never trains public models. SOC 2 Type II, with every access, inference, and review logged and attributable.
0 data incidents
You own everything
Everything the engineer builds lives in your repository and runs on your infrastructure. Your people are trained to run it and extend it. Nothing walks out the door when the engagement ends.
Yours code and repository
Contractual KPIs
Processing time, error rate, cost per transaction, hours freed. Agreed before we build, reported monthly. If the return is not there, we tell you first.
Agreed before deployment
The window
Every week you wait, the gap compounds.
Productivity is 3.2x at month three and closer to 8x at month twelve, because the systems learn from every transaction and the people learn alongside them. A competitor starting from zero a year from now faces the same six-week build. They are twelve months of institutional learning behind, and that gap does not close.
Book a call with an engineer
30 minutes. An engineer, not a salesperson.
Or forward this page to your CEO.