FROM A HARD QUESTION TO A WORKING SYSTEM
Machine learning, analytical systems, and technical evaluation for teams that need evidence—not another black box.
See the structure.
Understand the signal.
Which decision needs to improve? What evidence would change it? What is the simplest system that can deliver? We start there—then use data science, machine learning, and engineering where they earn their place.
Three connected disciplines. Scoped to your problem, your environment, and the people who will use the result.
01 / APPLIED AI & MACHINE LEARNING
Prediction, classification, anomaly detection, and AI-assisted workflows. Match the method to the evidence—not the trend.
02 / DATA & DECISION SYSTEMS
Data quality, reconciliation, analytical pipelines, and decision interfaces. Bring fragmented inputs into a workflow people can inspect and use.
03 / EVALUATION & ASSURANCE
Baseline comparisons, error analysis, and scenario testing. Surface limitations before a model becomes an operational dependency.
CAPABILITY VISUALS ARE SYNTHETIC SCHEMATICS, NOT CLIENT RESULTS OR PERFORMANCE CLAIMS.
A model is one component. The value is in the complete path from permitted data to a decision that can be understood, reviewed, and acted on.
SOURCE
Confirm origin, access, quality, and fitness for the question.
COMPUTATION
Start with a baseline. Add model complexity only when evidence supports it.
HUMAN REVIEW
Make findings traceable, limitations explicit, and escalation clear.
OPERATION
Integrate with the agreed workflow, acceptance tests, and handover.
Decision support, forecasting, data quality, reconciliation, and AI-assisted workflows for organizations with a defined operational problem.
Discuss your workflow ↗Claims-data quality, supported payment anomalies, model evaluation, and review-queue analysis. Evidence for qualified reviewers—not autonomous adverse decisions.
Scope a focused diagnostic ↗Bounded analytics, model evaluation, and technical work packages for public-sector requirements and prime-contractor delivery teams. Engagement eligibility and flow-down requirements are reviewed for each opportunity.
Discuss a requirement or teaming fit ↗05 / A CLEAR STARTING POINT
One decision. Clear evidence.
A practical next step.
Before committing to a larger build, establish what is feasible, what needs to change, and which action deserves investment.
Discuss a diagnosticENGAGEMENT PROFILE / 001
Payment integrity, model evaluation, and data-readiness questions can be selected as the diagnostic focus. No guaranteed recovery, accuracy, or return on investment is implied.
Build the validated next step: a model, pipeline, evaluation harness, or decision interface. Scope, acceptance tests, and handover are explicit.
Scope the build ↗Direct founder involvement in a bounded work package or an existing delivery team. Ongoing support is agreed only when the work requires it.
Discuss a delivery gap ↗Start with the objective, not a tool. Agree the evidence that matters. Simplify the path. Then build and automate with deliberate controls.
Identify the decision owner, operational constraint, and result worth pursuing. Challenge assumptions before committing resources.
Separate essential requirements from avoidable complexity. Any change to an existing system is explicit, scoped, and approved.
Compare against a useful baseline. Test relevant failure modes and make uncertainty visible.
Use short feedback loops and reviewable increments. Keep the decision owner close to the work.
Automate an understood, validated workflow—not an unresolved problem. Preserve human review, observability, and clear responsibilities.
| Operational layer | Architecture & tooling | Delivery standard | Evidence |
|---|---|---|---|
| Data | Approved sources and environment | Access, quality, and provenance established | Readiness record |
| Model & analysis | Methods selected for the problem | Baseline comparison and relevant testing | Evaluation report |
| Decision | Reviewable outputs and interface | Limitations and review responsibilities explicit | Findings and acceptance criteria |
| Operation | Client-approved integration | Documented handover and support boundaries | Runbook and delivery record |
These are engagement design standards—not claims of independent certification or completed client outcomes.
MERIDIAN, IDAHO
REMOTE COLLABORATION NATIONWIDE
Payette AI is led by founder Brandon Price. Work directly with the person responsible for scoping the question, doing the analysis, and explaining the result.
A focused technical partner—not another layer of coordination. Every engagement starts with clear responsibilities, defined deliverables, and an honest account of what the evidence supports.
Talk with BrandonDescribe the business problem and desired outcome. Use non-sensitive descriptions or synthetic examples. Do not email patient records, claims files, credentials, or controlled government information.
A client-controlled environment is an option, subject to agreed access, approved tooling, and applicable requirements. Before any sensitive-data access, we establish permissions, safeguards, and required agreements.
We can discuss a defined analytics or technical work package and assess delivery fit. Registration, certification, procurement eligibility, security requirements, and contract flow-downs are verified for the specific opportunity. No award eligibility is implied by this website.
You receive evidence and a scoped recommendation. A build, additional evaluation, or ongoing support is a separate decision. There is no automatic retainer or obligation to continue.
What needs to improve? What is getting in the way?
Tell us the context, desired outcome, and timing.
BUSINESS INQUIRIES ONLY. DO NOT SEND SENSITIVE DATA.