AI-Powered Solutions for the Modern Enterprise
We design and deploy production AI systems that solve real operational problems — built with the explainability, security, and governance requirements that government and regulated industries demand.
AI & Automation Services
Four core AI practice areas, each staffed by engineers with hands-on production deployment experience.
NLP & Text Analytics
We build language AI systems that understand, extract, classify, and summarize unstructured text at scale. From intake document triage to regulatory analysis engines, our NLP solutions reduce manual review burden dramatically.
- Document classification and routing (fine-tuned transformer models)
- Named entity recognition (NER) and information extraction
- Summarization and key insight extraction pipelines
- Sentiment analysis and content moderation
- Generative AI and RAG (retrieval-augmented generation) systems
Computer Vision
Image and video intelligence systems for facility monitoring, inspection automation, identity verification, and document digitization — built for real-world lighting, occlusion, and scale challenges.
- Object detection and tracking (YOLO, Detectron2, custom architectures)
- Image classification and semantic segmentation
- Document digitization and OCR pipeline engineering
- Video analytics and anomaly detection for surveillance feeds
- Edge deployment for disconnected or resource-constrained environments
Predictive Modeling
Forecasting and classification models that drive proactive decision-making across resource planning, risk management, and program performance. We emphasize model interpretability and auditability for regulated environments.
- Time-series forecasting for demand, budgets, and staffing
- Classification models for risk scoring and eligibility determination
- Anomaly detection for fraud, waste, and abuse identification
- Survival models for equipment maintenance and lifecycle planning
- SHAP / LIME explainability reporting for model audits
Process Automation
Intelligent process automation that combines RPA, AI, and workflow orchestration to eliminate repetitive manual work — reducing costs, errors, and cycle times across back-office and operational processes.
- RPA development with UiPath, Power Automate, and custom solutions
- Intelligent document processing (IDP) for forms and correspondence
- AI-assisted workflow routing and decision automation
- Human-in-the-loop workflows for exception handling
- Process mining and automation opportunity identification
AI in Action: Use Case Scenarios
Federal Claims Processing
An intelligent document processing system ingests incoming benefits claims, classifies supporting documentation, extracts key fields, and routes cases to the appropriate adjudicator — reducing intake processing time from 3 days to under 2 hours and achieving a 40% reduction in manual data entry errors.
Contract Anomaly Detection
A predictive analytics system monitors contracting spend patterns across a defense component, flagging statistical anomalies and potential duplicate payments in real time. The system processed 180,000 transactions monthly and surfaced $2.3M in potential recoveries within the first six months of operation.
Healthcare Outcome Forecasting
A clinical outcome prediction model ingests EHR data, social determinants of health, and historical treatment records to identify high-risk patients before acute episodes occur — enabling care coordinators to intervene proactively and reducing 30-day readmission rates by 22% in a pilot health system.
Responsible AI
We believe that AI deployed in government and enterprise environments must be trustworthy. Our responsible AI framework is not a checkbox — it's built into our engineering process.
Explainability
Every model we deploy includes SHAP-based or LIME-based explainability reports. Stakeholders can understand why a model made a specific prediction — essential for audit trails, appeals processes, and regulatory compliance.
Bias Detection
Before deployment and on an ongoing basis, we evaluate models across protected demographic categories using Fairness Indicators, Aequitas, and custom analysis. Disparate impact thresholds are defined upfront and monitored continuously.
Security & Privacy
AI systems handling sensitive government data are built with PII minimization, differential privacy where applicable, adversarial robustness testing, and full compliance with applicable data handling requirements.
AI Implementation Methodology
A structured four-phase process that takes AI from concept to production — with clear gates and deliverables at each step.
01 — Assess
Problem definition, data readiness assessment, feasibility analysis, and business case development. We evaluate whether AI is the right tool before committing to build it.
02 — Design
Architecture design, data pipeline design, model selection, evaluation framework, and responsible AI plan. Stakeholder alignment on success metrics and acceptance criteria.
03 — Build
Iterative model development, pipeline engineering, bias testing, and explainability implementation. Regular demos and stakeholder reviews throughout the build phase.
04 — Deploy
Production deployment with MLOps infrastructure, model monitoring dashboards, drift detection alerts, and retraining pipelines — ensuring the model continues to perform over time.