Public trust is built on good decisions. Good decisions need better data.
Government agencies hold the most consequential data in the world — census records, transportation networks, public health statistics, economic indicators. But legacy systems trap it, siloed departments can't share it, and AI adoption is blocked by governance gaps that no one has mapped. We modernize the infrastructure, build the analytics, and implement the responsible AI frameworks that let public sector organizations actually use what they have.
maintaining legacy systems
agencies must implement NIST RMF
spend over the next 5 years
state government decisions are made
Legacy IT modernization, statistical agency data platform transformation, responsible AI governance aligned to NIST AI RMF and OMB M-24-10, federal AI use case inventory and risk classification, and AI literacy programs for federal workforce.
Municipal data platforms and open data initiatives, operational efficiency analytics, public health and social services intelligence, budget and performance dashboards, constituent services analytics, and AI governance frameworks for state and local AI adoption.
Traffic and congestion analytics, transit ridership forecasting and optimization, infrastructure condition monitoring and predictive maintenance, safety incident analytics, multimodal transportation data integration, and capital planning intelligence.
NIST AI RMF is now a
federal mandate — not optional.
Executive Order 14110 and OMB M-24-10 require federal agencies to inventory, classify, and govern AI systems using the NIST AI Risk Management Framework. Most agencies have AI systems already in use that haven't been inventoried, classified, or governed under this framework.
We implement the four NIST AI RMF functions — Govern, Map, Measure, and Manage — as a structured engagement, giving agencies a defensible, documented AI governance posture before the next compliance review.
Explore AI Governance & Responsible AIThree forces reshaping
public sector data and AI.
AI executive orders, legacy IT crisis, and sovereign AI infrastructure demand are converging. Here's what's creating urgency — and opportunity — for government technology leaders.
Executive Order 14110 directed all federal agencies to inventory, classify, and govern AI systems. OMB M-24-10 provides the implementation guidance. Agencies that can't demonstrate NIST AI RMF compliance face procurement restrictions and audit exposure.
The GAO's 2024 federal IT report identified 10 critical legacy systems — some over 50 years old — that are security risks and barriers to modern data sharing. Every dollar spent maintaining COBOL on mainframes is a dollar not building the data infrastructure that AI requires.
State legislatures are moving faster than agencies can prepare. Colorado, Illinois, Virginia, and Texas have all passed or advanced AI governance legislation. Most state technology offices are scrambling to build compliance posture for AI systems that are already in production — often without an inventory of what they have.
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Most federal and state agencies run core operations on systems built in the 1980s and 90s. The data exists — census records, financial systems, benefit databases — but it's inaccessible to modern analytics tools, can't be shared across agencies, and requires manual extraction for any reporting beyond the system's native output.
We build the integration and transformation layer that extracts data from legacy systems — COBOL mainframes, COTS government platforms, flat-file exports — normalizes and governs it, and routes it into a modern cloud analytics environment without requiring full system replacement.
The OMB AI inventory process has surfaced 710+ federal AI use cases — most without documented risk classifications, human oversight mechanisms, bias monitoring, or incident response plans required by NIST AI RMF. Agencies face compliance exposure on systems that were deployed before these requirements existed.
We implement all four NIST AI RMF functions — Govern, Map, Measure, and Manage — as a structured agency engagement: AI system inventory, risk classification, governance control design, technical documentation, bias monitoring, and human oversight implementation.
Federal and state statistical agencies — census, labor, health, economic — collect massive datasets but publish on quarterly or annual cycles. The platforms underlying this work are often decades old, and the gap between data collection and accessible publication leaves policymakers making decisions on stale information.
We modernize statistical agency data platforms — migrating collection, processing, validation, and publication workflows to cloud architectures — reducing cycle times, enabling real-time dashboards for internal analysts, and improving public data portal accessibility.
Transit agencies, DOTs, and infrastructure operators collect sensor data, ridership records, maintenance logs, and safety incident reports across dispersed systems. The data that should drive route optimization, predictive maintenance, and capital planning is fragmented across vendors, siloed by department, and days or weeks stale by the time anyone analyzes it.
We build integrated transportation data platforms that unify sensor feeds, ridership data, maintenance records, and incident logs — powering real-time operations dashboards, predictive maintenance for infrastructure assets, and data-driven capital planning.
Municipal governments manage dozens of independent data systems — permitting, utilities, public safety, budget, social services — with no unified layer connecting them. Residents can't access meaningful open data. Administrators can't see cross-department trends. And state and federal reporting requirements consume staff time that should be spent on service delivery.
We build municipal data platforms that unify city department data into a governed analytics environment — enabling executive dashboards, cross-department intelligence, automated state and federal reporting, and public-facing open data portals.
Federal and state agencies consistently lose data scientists, ML engineers, and data engineers to private sector salary competition and hiring speed. Government procurement cycles often take longer than a private company takes to make an offer. The result: agencies either go without critical technical capacity or rely on expensive prime contractors for work that shouldn't require them.
We source vetted LATAM data engineers, analysts, and ML practitioners for government agencies and their prime contractors — at 40–60% below US rates, in US time zones, with 2–4 week placement timelines that fit within existing contract vehicles.
What sets us apart
for government engagements.
Government procurement is slow. Government data is sensitive. Generic consultancies don't understand NIST RMF, FedRAMP, or the political realities of agency technology change. We're built differently — and these differences matter when the stakes are public accountability.
Our Washington DC presence means federal procurement conversations, agency relationship development, and compliance review meetings happen in person — not across time zones. It matters for trust-building in government.
NIST SP 800-53, NIST AI RMF, and FedRAMP-aligned architecture are structural requirements of every government engagement — shaping platform choice, access controls, and documentation from the first design session.
Federal procurement trust is built in person. Our Washington DC presence means agency relationship development, compliance review meetings, and procurement conversations happen face-to-face — not over video calls from a city that's never navigated a GSA Schedule.
IBM watsonx is among the few AI platforms with on-premise deployment, government cloud availability, and built-in bias monitoring and explainability. As an IBM Silver Partner, we have deployment depth that certification alone doesn't convey.
Not ready for a full engagement?
Start with a defined, auditable pilot.
Government procurement requires defensible scope and clear deliverables. Our fixed-scope starter programs are designed for agency procurement realities — defined outputs, defined timelines, and a clear path to a larger follow-on.
We audit your data infrastructure, map your AI use case portfolio against NIST RMF risk tiers, identify legacy integration opportunities, and deliver a phased modernization roadmap with acquisition-cycle-aligned milestones and budget ranges.
See what's includedWe conduct an AI system inventory, classify each system against NIST AI RMF risk tiers, identify governance gaps, and produce a documented compliance posture with prioritized remediation steps — audit-ready for OMB and inspector general review.
See what's includedWe extract, normalize, and load data from one legacy system into a governed cloud analytics environment — with a working dashboard and an integration architecture that can be extended to additional source systems in subsequent phases.
See what's includedA free NuScienta diagnostic that benchmarks AI and data literacy across your agency workforce — identifying where staff confidence is high, where gaps are largest, and where AI literacy investment will have the most impact on mission delivery.
Take the free assessmentLet's talk about what your agency's
data should be doing for the public.
Whether you're modernizing legacy infrastructure, implementing NIST AI RMF, building a transportation analytics platform, or deploying a municipal data intelligence system — the conversation starts here. Chicago and Washington DC, ready to engage.
Government data & AI —
the questions we hear most.
From CDOs at federal agencies to data directors at city governments — here are the questions that come up in every first conversation.