AI Implementation and Delivery
Predictive analytics and machine learning
We build and deploy machine learning models on your own structured business data, covering demand forecasting, churn prediction, fraud scoring, recommendation systems and risk models. The output goes into the systems your team already uses, not a new platform.
Overview
This is applied machine learning on tabular business data, not generative AI. We build models that forecast a number, score a risk or predict a category, such as next month's demand, the likelihood a customer cancels, the probability a transaction is fraudulent or which product a customer is likely to buy next.
Work starts with the data you already collect: transaction history, usage logs, claims records or sales history. We assess data quality, engineer the features the model needs and train candidate models, then validate them against held-out data before anything goes near production.
Deployment means the model's output lands where your team already works: a dashboard, a CRM field, a claims queue or a batch file feeding your existing reporting. We are not building a new analytics platform for you to learn.
After launch we monitor for drift as customer behavior or market conditions change, retrain on a set schedule and report on accuracy over time so the model earns its place in the decision, rather than sitting untouched after the first release.
What you get
- Forecasts and scores built from data you already collect
- Model output delivered into the systems your team already uses
- Fewer manual spreadsheet forecasts and one-off judgment calls
- Earlier warning on churn, fraud or demand shifts
- Clear reporting on model accuracy over time
What's included
Data assessment and feature engineering
We review what data you have, what condition it is in and build the features a model needs from it.
Demand and sales forecasting
Models predict demand, sales or inventory needs at the level of detail your planning process requires.
Customer churn prediction
Models score which customers are likely to cancel or lapse, so retention efforts focus on the right accounts.
Fraud and risk scoring
Transaction or application data is scored for risk, with thresholds tuned to your tolerance for false positives.
Recommendation systems
Models suggest the next product, content or offer based on past behavior, built for your catalog and your customers.
Model training, validation and benchmarking
Candidate models are trained and tested against held-out data, with results reported before any model goes live.
Deployment into existing systems
Model output is delivered into your dashboard, CRM, claims system or batch pipeline rather than a separate tool.
Model monitoring and drift detection
Accuracy is tracked after launch so a model that starts drifting is caught before it affects decisions.
Scheduled retraining
Models are retrained on a set cadence as new data comes in and conditions change.
Documentation of model logic and assumptions
Each model ships with documentation of the data, features and assumptions used, for internal review or audit.
How we deliver
A simple, transparent path from first conversation to a team that scales with you.
1. Discover
We learn your goals, volumes, tools and compliance needs, then scope the right team and model. A response within 6 hours.
2. Design
We define roles, service levels, reporting and the ramp plan, and agree a clear, indicative price before you commit.
3. Deliver
We recruit, train and stand up the team inside your tools and processes, with North American management owning quality from day one.
4. Scale
We track performance against your service levels, tune as you grow and flex capacity up or down as your volumes change.
Engagement models
Start where it fits and change as you grow, with no rigid lock-in.
Dedicated team
A team that works only for you, managed by Corpshore to your service levels. Best for ongoing operations and scale.
Staff augmentation
Skilled people who slot into your existing team and tools. Best for adding capacity quickly.
Project or managed service
A scoped deliverable or a fully managed function with an agreed outcome. Best for defined work and outcomes.
Tools and integrations
Models are built to run on the data platform you already have rather than requiring a new stack.
Industry applications
Financial services
Fraud scoring and credit risk models run on transaction and application data to flag risk before it becomes a loss.
Industries we serveE-commerce and retail
Demand forecasting and recommendation models support inventory planning and merchandising decisions.
Industries we serveInsurance
Risk models support underwriting and claims scoring, built on the carrier's own historical claims data.
Industries we serveHealthcare
Scheduling and operational risk models, such as no-show prediction, support administrative planning rather than clinical decisions.
Industries we serveTechnology and SaaS
Churn prediction models score accounts against usage and billing data so retention teams prioritize the right customers.
Industries we serveCompliance considerations
CCPA aligned data handling
Customer records used to train and run models are handled under access controls and retention rules aligned to CCPA.
PCI DSS aligned fraud scoring
Fraud and risk models built on payment transaction data are designed so raw card data stays inside PCI DSS compliant systems.
Model governance and audit documentation
Regulated risk and underwriting models ship with documentation of data sources, features and validation results for audit review.
Frequently asked questions
Whatever structured data covers the outcome you want to predict, such as transaction history, usage logs or claims records. We assess what you have before scoping the project.
Build your team with Corpshore US
Tell us what you want to outsource and we will map a team, a model and a timeline. North American accountability, global delivery.
We respond to every US inquiry within 6 hours.