Automated Customer Recommendations
Manual promotion building replaced by an automated, machine learning supported offer workflow delivering recommendations in near real time.
Data & AI
AI features with a clear role in your workflow.
Add AI features to existing applications with clear data inputs, review steps and monitoring.
AI inside a real workflow
Useful assistance.
Human judgment.
Human judgment.
Incoming request“We need a customer portal.”
Suggested categoryWeb applications Review before routing
Accept or adjust the suggestion, then take the next step.
Built around your needs
What this makes possible.
- Features inside existing applications
- Models integrated and monitored
- People in the decision process
Illustrative work context.
When it fits and how we approach it
Details
When this helps
An AI feature may help summarize information, classify incoming work or suggest an action. It needs a defined task and a clear place in the product before a model is selected.
What we assess
We review the available data, expected outputs and consequences of an incorrect result. We define evaluation criteria, human review, fallback behavior and ownership alongside the integration requirements.
How we approach it
We integrate and evaluate the feature in its intended workflow. Users receive the context and controls needed to review outputs, while monitoring and feedback help the owning team assess quality after release.
What we deliver
What the engagement can include.
AI Features Inside Existing Applications
Details
We integrate AI directly into your current web or mobile applications. This includes predictions, recommendations, classification, or automation that users can interact with naturally.
- AI outputs delivered inside familiar interfaces
- Changes planned and tested around existing user workflows
- Models designed around real input constraints
- Clear handling of uncertainty and edge cases
- Features reviewed as data and requirements change
Machine Learning Model Deployment
Details
Training a model is only the beginning. We package models into production ready services with proper versioning, monitoring, and fallback behavior.
- Stable APIs for model inference
- Monitoring for data drift and performance drops
- Controlled rollouts for new model versions
- Graceful failure when predictions are unavailable
- Versioned services with documented operating requirements
Automation and Decision Support Systems
Details
We design systems where AI supports people by prioritizing, flagging or suggesting actions. Review requirements are defined around the consequences of an incorrect result.
- AI driven prioritization instead of static rules
- Outputs presented with relevant context and limitations
- Human approval loops where risk is high
- Reduced cognitive load for operational teams
- Review steps and controls matched to the task
Machine learning and data foundations
The recommendation case describes machine learning integration. The healthcare case is data infrastructure relevant to AI, not evidence of an AI model deployment.
Cloud Analytics for Health Information
Daily data cube rebuilds replaced with on demand reporting on a HIPAA compliant Azure data platform, adding peer benchmarking for providers.
How the work runs
Define the task and review needs
We define the task, expected outputs and review requirements before deciding whether AI is appropriate.
Prepare and align data
Data pipelines are checked against expected inputs, with tests for missing, changing or unsuitable data.
Integrate and validate models
Models are connected to live systems and tested under realistic conditions. Behavior is validated before scaling.
Monitor and improve continuously
Performance is tracked in production. Models are refined as data and requirements evolve.
Where it applies
Choose a defined task, provide the relevant context and make review part of the feature.
Healthcare operations
Summarize operational records
Help teams review incoming information.
Details
Summarize or classify records for a defined operational task. We assess data access, accuracy and review requirements before implementation.
Finance
Prioritize records for review
Present flagged items with the information a reviewer needs.
Details
Decision support can help organize records for investigation. The team needs evaluation criteria, context for each suggestion and controls for errors. A flagged record is a prompt for review, not a confirmed finding.
Ecommerce
Suggest relevant customer offers
Connect recommendations to the application workflow.
Details
Use relevant customer and product information to suggest offers within the application. We evaluate the recommendations against the goals and data for each use case.
Tools and platforms
Details
Listed to show fit with existing environments. Tools are chosen per project; a listing is not a partnership claim.
ML Framework
- TensorFlow
- PyTorch
LLM Platform
- OpenAI
- Claude
- Google Gemini
AI Platform
- Azure AI
ML Platform
- AWS SageMaker
Model Hub
- Hugging Face
LLM Framework
- LangChain
Vector Database
- Pinecone
Programming Language
- Python
API Framework
- FastAPI
Containerization
- Docker
Orchestration
- Kubernetes
Where could AI support your workflow?
Tell us about the task, the available information and how people should review the result.