AI Workflow Automation for Government

Public-sector technology lives or dies by two forces that pull in opposite directions: constituents now expect Amazon-grade digital service, while agencies must move software through FISMA authorization, an Authority to Operate (ATO) package, and procurement cycles that were designed for waterfall mainframe projects. SpeedMVPs closes that gap by delivering a production-ready AI MVP in 2-3 weeks — scoped, priced, and architected against the NIST AI Risk Management Framework (AI RMF 1.0) and the AI-use guardrails set out in OMB M-24-10 from the first discovery call. We have shipped 18+ AI MVPs across regulated domains with a team of 15+ engineers, and every government engagement ends with your agency owning 100% of the source code and IP — no proprietary runtime, no per-seat licensing that reappears at renewal.

Compliance is designed into the architecture, not bolted on before the audit. We build to the NIST 800-53 control families that underpin FedRAMP Moderate/High and StateRAMP, wiring in encryption at rest and in transit, least-privilege RBAC, and immutable audit logging so your ISSO has the artifacts an ATO reviewer actually asks for. Where the workload touches criminal-justice data (CJIS Security Policy), federal tax information (IRS Publication 1075), or controlled unclassified information for a defense supplier (CMMC Level 2), we scope the boundary accordingly and deploy inside AWS GovCloud, Azure Government, or GCP Assured Workloads — or fully air-gapped, running open-weights models (Llama, Mistral, Phi) when data sovereignty rules out any commercial LLM API.

Intelligent document processing is the highest-leverage first AI project for most agencies, because the backlog is measurable and the forms are structured enough to automate responsibly. We build IDP pipelines — OCR plus LLM extraction and classification over a RAG index (pgvector) — for permit intake, benefits eligibility, grant applications, and FOIA/public-records fulfillment, including automated PII/PHI detection and redaction so records officers stop hand-blacking documents in Acrobat. Critically, every extraction surfaces its source span and a confidence score, and low-confidence cases route to a human reviewer; that human-in-the-loop design is what keeps the system defensible under the transparency and contestability expectations of the NIST AI RMF and state administrative-procedure law.

Constituent-facing AI has to clear accessibility and language-access bars that consumer chatbots simply ignore. Every citizen interface we ship targets Section 508 and WCAG 2.1 AA conformance, and our multilingual assistants are built to satisfy Title VI language-access obligations for limited-English-proficiency (LEP) populations rather than bolting on a generic translation widget. We integrate identity through Login.gov or ID.me, support PIV/CAC smart-card and SAML 2.0 single sign-on for staff, and connect the assistant to the systems agencies already run — ServiceNow, Salesforce Government Cloud, Tyler Technologies, Esri ArcGIS, Socrata open-data portals, and 311/CRM back ends — so an answer about a pothole or a business license reflects live case status, not a stale FAQ.