HHS AI Power User Advanced Models and Features Pilot

STATEMENT OF OBJECTIVES

HHS AI Power User Advanced Models and Features Pilot

June 26, 2026

1. Purpose

The purpose of this acquisition is to obtain short-duration, firm-fixed-price pilot awards that function as inclusive, all-you-can-eat-style access bundles, within a stated usage envelope, for up to [1,000] authorized portable HHS power users per resultant award, with potential priced options to increase the authorized portable-user quantity up to 10,000. The objective is to let power users exercise advanced models, advanced features, integration paths, native apps, reporting functions, administrative controls, and security-boundary options in a forward-leaning enterprise environment before HHS finalizes its broader enterprise AI solution.

The primary purpose of the advanced power-user pilot is to establish operational baselines for emerging and frontier AI capabilities  to enable HHS to accurately forecast enterprise demand, define enterprise operating models, establish governance requirements, determine enterprise scalability, or develop future pricing structures.

HHS also intends to mature enterprise AI governance, attribution, allocation, and consumption measurement capabilities throughout the pilot period, including support for capability-level attribution, program-level allocation, enterprise reporting, and AI Consumption Unit (ACU) normalization methodologies.

The pilot shall enable the Government to baseline actual power-user usage, determine enterprise-feasible operational methodology, identify and roadmap which models and features require configuration or customization, identify levels and timing of customization, establish security and authorization logic, and formulate a common enterprise logical and operational model for AI use at HHS.

HHS requires the pilot to establish a practical operating model for commercial-parity access to advanced AI models and features in a forward-leaning enterprise environment. The contractor shall support HHS in determining how new commercial model releases, advanced features, agentic capabilities, native apps, coding and data tools, APIs, and administrative controls can be made available to Government users with minimal lag relative to commercial release, while satisfying HHS security, privacy, authorization, logging, identity, data-handling, statutory AI governance, and records requirements.

The contractor shall provide a FedRAMP 20x-aligned certification pathway where applicable, including Key Security Indicator mapping, machine-readable or automation-supporting evidence, persistent-validation approach, continuous monitoring evidence, significant-change logic, and agency ATO support artifacts. Where a feature or model cannot be made available to HHS at commercial parity, the contractor shall disclose the gap, cause, authorization dependency, security-boundary issue, required customization, and target availability date to close the parity gap.

HHS recognizes that certain AI capabilities are sufficiently mature within the Department to support consumption-based analysis, while other emerging and frontier capabilities require operational baselining before future enterprise pricing, licensing, and acquisition structures can be determined. Accordingly, this pilot distinguishes between frontier capability baselining and established capability consumption analysis.

HHS's desired end state is to establish a sustainable operational model that maintains or exceeds commercial parity at enterprise scale – continually operationalizing frontier AI innovation into the enterprise, advanced models, advanced features, agentic capabilities, coding capabilities, research capabilities, scientific capabilities, APIs, administrative capabilities, and other newly released commercial capabilities become available to HHS users at or before the time they become available to commercial enterprise customers, except where a documented security, privacy, legal, authorization, compliance, or technical dependency prevents such availability.

2. Background

HHS is preparing for broader enterprise acquisition of LLM and related AI capabilities, including potential multiple-award BPA and enterprise license or enterprise agreement task orders. Market research indicates that advanced LLM offerings differ materially in buying channel, security boundary, model and feature availability, release cadence, administrative controls, reporting, API and gateway compatibility, and pricing or consumption meters.

The pilot is intended to generate operational evidence that cannot be obtained from paper market research alone. HHS needs to observe how power users utilize advanced AI capabilities, how those capabilities map to HHS mission workflows, what guardrails and administrative controls are necessary, what can be enabled immediately, what requires configuration or integration, and what requires additional security, privacy, records, accessibility, or authorization work before enterprise scaling.

HHS recognizes that enterprise AI consumption measurement and allocation methodologies will continue to mature. The Government seeks to establish the operational data, telemetry, attribution, reporting, and governance foundations necessary to support future enterprise budgeting, allocation, chargeback, pricing, and acquisition decisions. The pilot is intended to support that maturation process without requiring HHS to prematurely adopt a final enterprise pricing model.

The pilot is intended to generate operational evidence that supports future enterprise acquisition decisions, including BPA structure, enterprise licensing strategies, consumption models, governance frameworks, release-alignment approaches, interoperability requirements, and commercial-parity objectives.

Market research supports the following major objective themes: advanced reasoning and chat, coding assistants, data analysis, secure API access, automation and agents, management reporting, OAuth/OIDC and SSO, audit retention and exportable telemetry, a FedRAMP 20x-ready commercial enterprise tenant or equivalent, usage analytics delegated at multiple organizational scopes, and transparent native usage constructs such as tokens, credits, AMUs, requests, messages, searches, tool use, capacity, or equivalent provider-defined units.

HHS seeks to understand not only how AI capabilities are consumed, but also how frontier AI capabilities create mission value, alter workflows, affect governance requirements, and influence future enterprise operating models.

3. Scope or Mission

The contractor shall provide one integrated pilot bundle for the applicable individual LLM. The bundle shall include access, configuration, onboarding, enablement, usage and adoption reporting, feature-readiness analysis, integration-readiness analysis, security and authorization pathway analysis, statutory AI governance collaboration, use-case inventory support, release-alignment planning, and closeout recommendations.

• Provide inclusive access to all offered advanced models, modes, tools, integrations, and advanced features available in the proposed channel and within the ordered usage envelope for up to [1,000] authorized portable users.
• Enable HHS to try, compare, and baseline advanced capabilities beyond baseline chat, including premium reasoning, long-context work, document and file workflows, web-grounded research where permitted, code and data analysis where permitted, connectors, memory or personalization where permitted, projects or workspaces, agentic or delegated-work features, API or gateway integration paths, and release-preview or newly released model and feature evaluation paths.
• Document which capabilities are native and ready, native with configuration, API or gateway interoperable, available only in a different security boundary, preview-only, roadmap or future, or requiring customization before enterprise use.
• Provide a customization matrix showing level of customization, owner, dependencies, expected effort band, schedule implications, and whether customization is required before HHS enterprise scaling.
• Provide security-enablement and authorization-pathway logic for each model and feature category, including boundary, data flow, logging, retention, identity, DLP/PII/PHI handling, FedRAMP/cybersecurity posture, BAA/HIPAA posture if applicable, rollback, release approval, and user controls.
• Provide usage baselining sufficient for HHS to understand which features power users use, how often they use them, what workflows they support, what level of administrative support they require, and what future pricing/order structure is most appropriate.
• Provide collaboration with HHS CAIO, OCIO, privacy, cybersecurity, acquisition, legal, records, Section 508, Operating Division, and program stakeholders to support AI use-case inventory, compliance planning, issue reporting, governance operating model formulation, and future acquisition terms.
• Provide a release-alignment and rapid-implementation roadmap for newly released models and features, including the steps needed to make eligible releases available to Government users with minimal lag relative to commercial release.
• Provide final recommendations for HHS's anticipated AI BPA and ELA task orders, including CLIN structures, evaluation factors, reporting terms, integration expectations, security terms, option or surge structures, governance artifacts, and product/channel segmentation.