Generative AI & LLMs — key capabilities
- Custom LLM-powered applications and internal assistants
- Retrieval-augmented generation (RAG) grounded in your own documents and data
- Model selection across OpenAI, Anthropic, and open-source options based on your requirements
- Prompt engineering and evaluation frameworks to keep output quality consistent
Where this is used
01
An internal knowledge assistant that answers employee questions from your actual policy documents
02
A customer-facing chatbot grounded in your real product catalog and support history
03
Automated first-draft generation for repetitive written work — proposals, summaries, reports
AI-Powered Automation — key capabilities
- Document processing and data extraction from unstructured sources
- AI-assisted classification and routing for support tickets, claims, and requests
- Exception handling that flags edge cases for human review instead of guessing
- Integration with your existing workflow and RPA tools rather than replacing them outright
Where this is used
01
Automatically extracting structured data from invoices, contracts, or intake forms
02
Routing customer support tickets to the right team based on content, not just keywords
03
Flagging unusual transactions or claims for human review instead of blanket manual checks
Artificial Intelligence — key capabilities
- Predictive modeling for forecasting, risk scoring, and demand planning
- Classification and pattern detection models trained on your own operational data
- Model monitoring and retraining pipelines so accuracy doesn't quietly degrade
- Clear evaluation metrics tied to the business outcome, not just model accuracy in isolation
Where this is used
01
Demand forecasting models that account for your actual seasonality and business cycles
02
Risk or fraud scoring models trained on your historical transaction data
03
Predictive maintenance models flagging equipment issues before failure
