AI & machine learning
Predictive models, RAG and agents — with governance built in.
Overview
What we do.
Our predictive work starts from the analytic baseline, not from a model. We establish what the data already shows, define the decision the prediction has to support, then build the simplest model that moves that decision — and carry evaluation, drift monitoring and governance artifacts with it.
How it works
From input to outcome.
- Frame
- Prepare
- Train
- Evaluate
- Deploy
- Monitor
What we deliver
Services.
Predictive & statistical modeling
- Classification and propensity models
- Regression and forecasting
- Anomaly and outlier detection
- Time series and trend projection
- Clustering and segmentation
- Baseline benchmarking before model selection
Generative AI & agents
- Retrieval-augmented generation over your data
- Semantic Kernel agent orchestration
- Azure AI Foundry model deployment
- Summarization and document analysis
- Natural-language query over analytic models
- Human-in-the-loop approval for agent actions
Model governance & assurance
- NIST AI RMF alignment
- AI inventory and use-case registry
- Responsible AI review checklist
- Bias, fairness and error-cost review
- Drift monitoring and retraining triggers
- Model cards and evaluation documentation
Tools & platforms
- Azure AI Foundry
- Semantic Kernel
- Python
- Cloudera
- GitHub
Fast to put on contract.
EDWOSB/WOSB sole source up to $5.5M, set-aside, or GSA MAS 47QTCA25D00BT.
More capabilities
One bench, six domains.
- Data analytics & visualizationRecurring reporting portfolios, owned end to end.
- Cloud application developmentCloud-native apps, from front end to pipeline.
- Robotic process automationManual work automated against a measured baseline.
- Cyber analyticsCyber analytics leadership can act on — metrics, trends and audit-ready evidence.
- Data & AI governanceGovernance that holds up in an audit.
