We design and put into operation the framework an organization uses to approve, inventory, assess, and monitor its AI systems. The policies, intake process, risk register, monitoring, and training are built inside the tools you already run, and your staff are trained to operate them.
Agencies and regulated companies already have AI in production systems, in pilots, and in everyday use by individual staff. Few have a single record of those systems, one way to approve a new one, a method for rating risk, or evidence that each system still performs as intended.
Oversight bodies, auditors, and boards now ask for all four. A policy document on its own does not satisfy them, so Sprinklenet builds the framework as a working program: each policy is tied to a control that runs in a tool your staff already use, and each control leaves a record an auditor can inspect.
Seven components, delivered together as a complete framework or separately where part of the work is already done.
Principles, decision rights, and review stages for the full life of an AI system, from proposal through retirement, written for your mission and legal authorities.
Policies and standards for staff use of generative AI, data handling, transparency notices, human review, third-party AI services, and AI output used in decisions or as evidence.
A standard intake form and approval path for every proposed use, feeding one inventory that records the owner, purpose, data classification, and status of each system.
A tiering method that sets review depth by impact, and a register that links each risk to a system, a control, an owner, and a review date.
Measures and thresholds for accuracy, fairness, and model drift, with scheduled checks that open a ticket when a threshold is crossed.
Role-based training for executives, product owners, developers, and end users, and a train-the-trainer program that prepares your own staff to deliver it.
The council charter, roles, meeting cadence, release checks, and annual recertification cycle that keep the framework current after the engagement ends.
Each proposed use of AI moves through the same five stages. Monitoring results revise policy, and the use cases a change affects are reviewed again.
The work moves through four stages, and each stage ends with something your organization can use.
We read your current AI policies, decisions, and system lists, interview the people who own and use AI, and record each required element as complete, partial, or missing.
We define decision rights, risk tiers, the system record, and measures of success with your leadership, and publish them as a blueprint for approval.
We write each control, configure it in your tools with your administrators, and prove it on one or two real systems before extending it to the rest.
We train your staff by role, prepare your own trainers, and hand over a framework your team runs without us.
The framework does not require a new platform. Intake forms, the inventory, the risk register, release checks, and dashboards are configured in your existing data catalog, ticketing system, code repositories, analytics environment, and collaboration suite. The design is vendor-neutral, so it continues to work when you change models, vendors, or monitoring tools.
Each framework is mapped to the NIST AI Risk Management Framework (AI RMF 1.0) and, for generative AI, the NIST Generative AI Profile (NIST AI 600-1). For federal agencies, the mapping extends to OMB AI guidance, including Memorandum M-25-21 on agency use of AI and Memorandum M-25-22 on AI acquisition, and to GAO's AI Accountability Framework.
The mapping is delivered with the framework. An auditor can trace each requirement to the control that satisfies it and to the evidence that the control ran. When guidance changes, the mapping shows which controls need to be revised.
The framework, policies, configurations, and training materials produced for your program belong to you.
Chief AI Officers, CIO offices, and oversight and audit organizations that need a complete inventory, risk tiers for high-impact AI, and documented practices consistent with OMB AI guidance.
Risk, compliance, and legal leaders in financial services, healthcare, and other regulated sectors who need oversight of AI across the business that a board and regulators will accept.
Teams that need a responsible AI lead and governance specialists on a federal program, under a subcontract or teaming arrangement.
The practice does not depend on any Sprinklenet product. Where an organization wants a governed environment for AI assistants and knowledge retrieval, Knowledge Spaces provides one as a hosted service or under a commercial license, with role-based access, configurable guardrails, scored evaluation runs, and an audit log. Knowledge Spaces is Sprinklenet's proprietary platform and is licensed separately from the consulting deliverables. Sprinklenet operates it every day for its own clients, and that experience informs the controls we design for yours.
Many AI evaluations stall while approval to share real data is negotiated. Sprinklenet builds synthetic datasets that mirror the structure, volume, and difficulty of an organization's records, using fictional parties and a recorded correct answer for every test question. The data is loaded into an isolated Knowledge Spaces environment, where assistants, models, and retrieval settings are tested under the same access rules, guardrails, and audit logging that apply in production.
Each synthetic collection is generated with a ground-truth record, so accuracy, citation, and refusal behavior are scored against facts.
Collections run to thousands of documents, which shows the retrieval, response time, and cost behavior that a small sample hides.
The same test set runs against several models, so the choice of model rests on measured results.
No sensitive record leaves the organization's control during the evaluation, and the test sets remain in place for regression testing after launch.
Sprinklenet builds and runs a governed AI platform in production. The access rules, guardrails, evaluation runs, and audit records we design for clients are ones we operate ourselves.
A senior practitioner leads every engagement and works directly with your leadership, counsel, and technical staff.
Federal buyers can order through Sprinklenet's GSA Multiple Award Schedule for management consulting and IT professional services, or through a prime contractor's vehicle.
Sprinklenet contributes to federal AI standards. Read our public comment to NIST on AI agent identity and authorization.
For organizations that need to know where they stand, we review existing AI policies, the system inventory, and oversight practices against the NIST AI Risk Management Framework and applicable guidance, then deliver a gap assessment and a sequenced plan.