AI & Machine Learning
We apply machine learning and large language models where they solve a specific, measurable business problem: automating manual review work, forecasting demand, or giving your team an internal copilot grounded in your own data. Every model ships with a monitoring plan, not just a demo.
What we deliver
- Applied AI: document processing, forecasting, and recommendation systems built on your own data
- LLM-based tools — internal copilots, support automation, and retrieval-augmented generation (RAG) search
- Predictive models for demand, risk, and churn, evaluated against a business metric your team already tracks
- Model evaluation and guardrails so outputs stay accurate and auditable
- Monitoring for drift and cost so accuracy stays visible after launch, not just at demo time
Our approach
Assess
Audit data quality and the specific decision the model needs to support.
Architect
Model and pipeline architecture scoped to a measurable success metric, agreed up front.
Build
Iterative delivery with a working prototype early, refined against real data and real users.
Operate
Monitoring for drift and cost, with a review cadence to retrain or retire models.
Proof
A telehealth provider was processing patient intake and documentation by hand. We built an LLM-based intake and document-classification pipeline inside their existing HIPAA-compliant infrastructure, with a human-in-the-loop review step — cutting administrative time by 80%.