Focus on a defined business task
Give the capability an explicit purpose and acceptance criteria. Evaluate whether it helps the intended users complete their work, including cases where a simpler approach is preferable.
AI Empowerment
Apply AI to a useful, reviewable enterprise workflow.
Develop AI capabilities around a defined business task, approved information, and measurable evaluation. Connect models to enterprise workflows with clear permission boundaries, human review, and operational ownership.
Description
Useful enterprise AI begins with a task rather than a model. Identify the people who need help, the information they may use, and what an acceptable result looks like. Document the current process and compare an AI approach with conventional software, search, or explicit business rules. Evaluation should include representative examples and difficult cases, not just a convincing demonstration. This helps establish whether the proposed capability is suitable and where human judgment must remain part of the workflow.
Implementation can include knowledge assistants, structured information extraction, or bounded actions connected to existing systems. Retrieval can provide relevant source material, but retrieved text and citations do not guarantee a correct answer. Access rules must follow the user and the underlying information. Tool permissions, output validation, and approval steps should reflect the consequences of an action. The design specifies how the application handles uncertainty, missing context, unavailable dependencies, and requests outside its intended purpose.
Privacy and security decisions come before sending enterprise information to a model service. Provider terms, data handling, retention, deployment region, identity controls, and the system boundary require project-specific review. For federal projects, integrations are designed to support the applicable security and privacy requirements, with project-specific validation of approved services, controls, and evidence for designated reviewers. NIST guidance can inform risk management and evaluation, but adopting a framework does not establish blanket compliance. Operational planning includes model changes, cost visibility, feedback, and the responsibility for reviewing performance over time.
Value Proposition
Connect approved enterprise knowledge
Use relevant information sources with their permission boundaries intact. Make source context available where appropriate and define how updates, missing information, and conflicting material are handled.
Keep adoption controlled
Introduce the capability with bounded access, review responsibilities, and measurable evaluation. Make limitations visible so users can decide when to verify a result or follow another process.
Where We Are Different
Evaluation before expansion
Define examples, failure cases, and review criteria before broadening the application. Use observed results to decide whether the proposed scope should continue, change, or stop.
Bounded actions and human review
Separate generating an answer from changing a business system. Specify permitted actions, validation, and approval points according to their consequences.
Information requirements first
Review permitted data, provider handling, access boundaries, and retention before integration. Treat project-specific security decisions as implementation inputs rather than final paperwork.
Our Core Features
Technology choices follow your existing environment, data boundaries, and operational requirements. The tools below describe relevant implementation options; the final stack is agreed for the project.
Use-case assessment and evaluation
Translate a proposed idea into a defined workflow and evaluation set. Compare approaches, identify important failure modes, and establish how results will be reviewed by people familiar with the task.
Relevant technologies: Python · Jupyter · NIST AI RMF
Knowledge assistants and retrieval
Connect approved material to model-assisted search and answers. Design retrieval, source context, access filtering, and refresh behavior while testing whether the information supports the resulting response.
Relevant technologies: Amazon Bedrock · Azure OpenAI · PostgreSQL with pgvector
AI-assisted workflow integration
Connect bounded model outputs to enterprise interfaces. Validate structured information and tool requests, define error handling, and require review where an action’s impact warrants human approval.
Relevant technologies: Python · OpenAPI · JSON Schema
Privacy and security implementation
Implement agreed identity, secret handling, and permission controls. Review information flows and service configuration against the project’s approved boundary and data handling requirements.
Relevant technologies: Microsoft Entra ID · AWS IAM · Azure Key Vault
Operational evaluation and change
Track relevant behavior, failures, usage, and cost. Maintain repeatable evaluation when prompts, source material, model versions, or integrations change, with defined ownership for interpreting results.
Relevant technologies: OpenTelemetry · GitHub Actions · Python
Practical questions
Will enterprise information be sent to an external model?
That depends on the approved architecture and provider configuration. Information categories, service terms, retention, deployment region, and access must be reviewed before integration. A model choice alone does not resolve those requirements.
Can an AI agent act without approval?
Only actions explicitly permitted by the agreed design should be available. The workflow may require human approval, validation, or additional restrictions depending on the action’s consequences and the organization’s policies.
Is an AI application automatically compliant with federal requirements?
No. Applicable requirements and the complete system boundary need project-specific validation. Engineering can support controls and evidence; designated reviewers and responsible officials determine whether the proposed use is acceptable.
Related services
Cybersecurity
Assess technical risk, strengthen application and infrastructure controls, and connect remediation to the people who own the system. Make security work specific, testable, and useful to ongoing operations.
Development and Operations
Improve the path from source changes to deployment and operational feedback. Build practical delivery automation with relevant testing, security checks, environment controls, and responsibilities that teams can maintain.
Start with one enterprise task.
Describe the workflow, its users, and the information involved. We can discuss whether AI is appropriate and what a controlled initial scope would require.