Scope the decision
Choose one repeated document bottleneck, one primary input, and one accountable output.
Specialized engineering for RCM and healthtech teams
Turn medical documents into review-ready data—with the evidence intact.
We design and deliver focused document workflows for coding, audit, and healthtech operations. Every important output can return to its source, pass through human review, and fit a controlled deployment model.
Source-level traceabilityReviewer controlCustomer-hosted option
Review case
Synthetic chart · 4 pages
PAGE 2 / 4
Assessment and plan reviewed with patient.
Type 2 diabetes mellitus without complications.
Continue current medication and monitoring.
No acute symptoms reported at this visit.
Primary diagnosis
Type 2 diabetes mellitus
Page 2 · Assessment
Suggested ICD-10-CM
E11.9
Evidence found
Documentation check
Ready for human review
3 of 3 rules passed
Designed around operational review
RCM operationsClinical documentation teamsHealthtech product teams
Evaluate before scaling
Define the safe test set, acceptance thresholds, and expensive failure modes before implementation.
Ship a controlled workflow
Deliver source-linked results, review controls, exceptions, and a clear production recommendation.
Focused solutions
Start where document friction is measurable.
We do not begin with a platform replacement. We isolate one recurring review step, make its evidence visible, and prove the operational value.
Documentation completeness
Identify missing signatures, dates, orders, pages, or required fields before a chart enters the next queue.
Coding evidence review
Link candidate ICD-10-CM concepts to the exact source language so a qualified reviewer can inspect the basis.
Document-to-data workflows
Transform PDFs and faxes into review-ready JSON, CSV, or a defined FHIR resource while preserving source coordinates.
Delivery architecture
A controlled path from source document to approved output.
The model is only one part of the system. A dependable workflow also needs document normalization, explicit rules, evidence mapping, exception states, review controls, and usable export.
01
Ingest
PDF, fax, image, or structured source
02
Normalize
Pages, fields, sections, and metadata
03
Analyze
Models plus explicit workflow rules
04
Review
Evidence, confidence, and exceptions
05
Deliver
JSON, CSV, FHIR, or customer environment
Defined acceptance criteria
Agree what “correct” means and which errors carry the highest operational cost.
Measured reviewer impact
Compare field quality, coverage, exception rate, and human review time.
Explicit operating boundaries
Document hosting, access, retention, deletion, and approval responsibility before production.
10-day fixed-scope pilot
An evaluation you can say yes—or no—to.
Design-partner pricing
From $1,500
Days 1–2
Map
One workflow, safe sample set, success metrics and failure costs.
Days 3–6
Build
A working review flow for one input and one agreed output.
Days 7–9
Evaluate
Field-level results, coverage, exceptions and human review time.
Day 10
Decide
A live walkthrough, findings report and clear production recommendation.
20–50 synthetic or de-identified documentsEvidence-linked results and review UIDocker or customer-environment optionEvaluation report with failure cases
Engineering partnership
Domain context without enterprise-project drag.
We combine medical-document workflow knowledge with rapid software delivery. The engagement stays narrow enough to evaluate, but the implementation is designed with production constraints in view.
Workflow-informed
We start from reviewers, queues, exceptions, and downstream decisions—not a generic AI feature list.
Evidence-first
Important outputs keep a visible connection to source text, page context, and reviewer action.
Built to hand over
Pilot outputs include failure findings, deployment considerations, and a clear next-step recommendation.
Data approach
Earn access. Do not assume it.
The first conversation and demonstration require no patient data. If a real-data pilot is justified, data flow, retention, hosting and responsibilities are defined before access.
Safe-data first
Start with synthetic or appropriately de-identified samples.
Customer-hosted option
Run the workflow in a customer-controlled environment when required.
Human approval
Important outputs remain reviewable and approval-gated.
Defined retention
Agree what is stored, for how long, and how it is deleted.
Questions before a pilot
Clear boundaries from day one.
Do we need to send protected health information?+
No. Discovery and the first demonstration use synthetic data. A pilot can also begin with appropriately de-identified samples or run in your environment.
Is this autonomous medical coding?+
No. The pilot supports qualified reviewers with evidence-linked suggestions and checks. It does not replace professional judgment or automatically submit claims.
What is included in ten days?+
One workflow, one principal input format, one agreed output, a review interface, evaluation results, and a production recommendation.
Can you integrate with our current system?+
Integration is evaluated during discovery. CSV, JSON and a defined FHIR resource are common pilot outputs; production integrations are scoped separately.
Workflow review
Bring one manual document step.
Tell us what happens today. We will respond with the next useful question and, if there is a fit, a proposed evaluation plan.
No patient data required
No platform replacement pitch
A focused answer, not a generic AI deck