AI that is measurable, governed, and built for the mission.
From classical machine learning to generative AI and retrieval-augmented assistants, SIS helps organizations move from promising prototypes to evaluated, monitored, and responsibly governed AI capabilities.
Most AI efforts do not fail because of the model. They fail on data readiness, unclear success metrics, missing evaluation, and the gap between a notebook and an operational system. SIS approaches AI as an engineering discipline: define the decision the model supports, establish a baseline, measure honestly, and design for monitoring and human oversight from day one.
Our founder's background in data science and independent verification and validation shapes how we work: every model claim should be testable, every dataset traceable, and every deployment observable.
The technical areas SIS can support, and what each makes possible for your mission.
Classification, regression, and risk scoring on structured data — eligibility, fraud and anomaly flags, demand, and resource forecasting — with explainability built in.
Document summarization, drafting assistance, knowledge search, and structured extraction using large language models with guardrails, prompt management, and output validation.
Grounding AI answers in an organization's own policies, manuals, and records — with citations, access controls, and freshness so answers can be checked, not just trusted.
Multi-step AI assistants that call tools, query systems, and route work — scoped with permissions, approvals, and audit logs so humans stay in control of consequential actions.
Entity extraction, classification, sentiment, and topic modeling across correspondence, case notes, contracts, and public comments.
Image and video analysis for inspection, change detection, object counting, and document digitization — including imagery from drones and fixed sensors.
Workload, capacity, budget-burn, and sensor forecasting with uncertainty ranges so planners see confidence, not just a single number.
Versioned data and models, reproducible training, automated evaluation gates, drift and performance monitoring, and safe rollback.
Test sets, red-teaming, bias and robustness checks, hallucination measurement, and acceptance criteria — applying IV&V discipline to AI systems.
Use-case inventories, risk assessments, model cards, documentation, and human-oversight procedures aligned to federal AI guidance and the NIST AI Risk Management Framework.
Representative ways organizations put these capabilities to work.
Summarize, classify, and route forms, claims, and correspondence to cut manual triage time.
Give staff a cited, permission-aware assistant over policies, SOPs, and technical manuals.
Flag unusual transactions, records, or system behavior for analyst review.
Anticipate call volume, workload, maintenance needs, or supply demand.
Detect change, damage, or objects of interest in aerial and ground imagery.
Use ML to prioritize test cases, cluster defects, and predict release risk.
Define the user, the decision the AI supports, success metrics, and the non-AI baseline.
Inventory sources, quality, labeling needs, sensitivity, and access constraints.
Build quickly, then measure against a held-out test set and agreed acceptance criteria.
Add guardrails, documentation, security review, and human-oversight procedures.
Ship with monitoring for drift, cost, latency, and quality — and a rollback plan.
We are tool-agnostic and start from what you already use. Representative technologies:
Yes. Options include government cloud regions, private model endpoints, and open-weight models hosted inside your boundary so data never leaves controlled infrastructure. The right choice depends on data sensitivity and authorization requirements.
We agree on measurable acceptance criteria up front — accuracy, error types that matter, latency, cost — and test against data the model has never seen. For generative AI we add groundedness, citation accuracy, and refusal behavior checks.
Not always. Many generative AI and RAG use cases work with existing documents. Predictive models need representative historical data; a short data assessment answers this quickly.
Through documented risk assessment, subgroup performance testing, human review of consequential outputs, access controls, and audit logging — aligned to the NIST AI RMF.
SIS designs and validates the pipelines, warehouses, and dashboards that turn scattered records into reliable, governed insight — with data quality…
Explore capability →SIS provides objective, evidence-based verification and validation across the software lifecycle: requirements traceability, functional and…
Explore capability →SIS helps organizations plan, migrate, automate, and operate cloud and hybrid environments — with infrastructure as code, strong security baselines,…
Explore capability →Connect with SIS to discuss technical needs, collaboration, or teaming opportunities.