2–3 weeks · fixed price
Model Validation Review
An independent review of one model: data, methods, performance, and limitations, with a written report and prioritized fixes.
Services
Rigorous work, delivered in a form you can use
Four services, one standard: methods you can explain, results you can verify, and deliverables your team can run without us.
01
Fraud, anomaly & payment-integrity analytics
We find the payments, claims, transactions, and records that deserve a closer look, and we turn manual, after-the-fact reviews into automated monitoring. Models are tuned to the review capacity you actually have, so analysts spend their time on the cases most likely to matter.
Typical problems we solve
- Which payments, claims, or grants are most likely to be improper?
- Which transactions or records look anomalous or fraudulent?
- Key risk indicators are tracked by hand and reviewed too late.
- Our review team can’t keep up, and we don’t know what to prioritize.
Example deliverables
- Risk-scoring and anomaly-detection models with documented thresholds
- Automated monitoring of risk indicators that replaces manual tracking
- Analysis of historical findings to show what the model catches and misses
- Dashboards in the BI tool you already use, such as Tableau, Power BI, or Looker
02
Credit, risk, forecasting & predictive models
We build models that estimate the probability that a customer, applicant, or account will do something: buy, convert, renew, churn, default, prepay, or respond to an offer. These propensity and risk scores tell your teams where to focus, and our forecasts tell you what volumes, losses, or demand to plan for. We bring large, messy, multi-source data together first, so the model rests on numbers people trust.
Typical problems we solve
- Which customers are most likely to buy, renew, or respond to an offer?
- Who is likely to churn, default, or prepay, and how early can we tell?
- What volumes, losses, or demand should we plan for next quarter or next year?
- Our systems disagree, and nobody fully trusts the numbers.
Example deliverables
- Propensity, risk, and forecasting models with a reproducible training pipeline
- Batch or real-time scoring integrated into your systems and workflows
- Model documentation covering data, features, performance, limitations, and intended use, written for model-risk and audit review
- A monitoring plan with drift and performance thresholds
03
Model validation & AI evaluation
Before you rely on a model, or buy, build on, or deploy an LLM or AI product, we test it independently against your use case, your data, and your risk tolerance. We follow the discipline of bank model-risk management (SR 11-7 style) and map AI risks to the NIST AI Risk Management Framework. We are independent of AI vendors, so our only stake is in getting the answer right.
Typical problems we solve
- A model is in use, but no one has independently checked it.
- A vendor promises accuracy we can’t verify on our own data.
- We don’t know how the tool fails: hallucinations, bias, sensitive-data leakage, or prompt injection.
- Procurement or oversight needs documented evidence before approval.
Example deliverables
- An independent validation report on conceptual soundness, data, performance, and limitations
- A use-case-specific evaluation set and test harness you can rerun as models change
- A risk assessment covering privacy, security, bias, and explainability, mapped to the NIST AI RMF
- A clear go / no-go recommendation, with conditions
04
Custom AI tools & AI services
We build practical AI tools around your work, such as internal assistants, search over your own documents and data, and automation of repetitive steps, using LLMs and machine learning where they genuinely help. Then we stay on to support them: monitoring quality, tuning as models and needs change, and training your team.
Typical problems we solve
- We want to use AI, but aren’t sure where it will actually pay off.
- People spend hours searching documents or re-keying the same information.
- We built an AI pilot, but no one is maintaining or improving it.
- We need AI that runs within our own environment and access controls.
Example deliverables
- A short assessment of where AI fits your workflows, with expected value and risks
- A working AI tool, such as an assistant, search, or automation, deployed in your environment
- An evaluation set that measures accuracy on your own data
- Ongoing support: monitoring, tuning, model updates, and user training
Ways to start
Fixed-scope, fixed-price first engagements
A small, well-defined project is the fastest way to see how we work. Each one ends with a deliverable you keep.
3–4 weeks · fixed price
Fraud Analytics Sprint
A working risk-scoring or anomaly model on a sample of your data, with results measured against known cases.
2–4 weeks · fixed price
AI Tool Evaluation
A use-case-specific test of an LLM or AI product, with a reusable test set and a go / no-go recommendation.
Supporting a proposal or a prime contract? See how we team.
How we work
A clear path from question to handover
Whether the engagement is a focused assessment or a multi-month build, the shape stays the same.
- Step 1
Discover
We learn the decision you’re trying to make, the data you have, and the constraints you work under, including security, compliance, and timeline. You get a short scope with agreed success measures.
- Step 2
Build
We prepare the data and develop the analysis, model, or evaluation iteratively, sharing working results early and checking in often.
- Step 3
Validate
We test rigorously: out-of-sample performance, stability, error and fairness analysis, and review with your subject-matter experts.
- Step 4
Deploy & hand over
We deliver into your environment with documentation, knowledge transfer, and a monitoring plan, so your team can own it with confidence.
Not sure which service fits?
Describe the problem in your own words. We’ll suggest the smallest engagement that gets you a real answer.