Enterprise AI / Company-specific data
Already using an AI model in your business? Give your team a way to evaluate it against your company's policies, customer conversations, and operational knowledge. We design and run scoped programs that turn approved sources and human expertise into private, company-specific datasets.
What we create
Start with a specific workflow and the people who know it. The dataset should reflect what your company considers correct, useful, and appropriate, including when an AI system should ask for help.
01
Build realistic questions from approved policies, product information, and operating documents. Include source-backed reference answers, grading criteria, and cases where the available evidence is insufficient. Specify when the system should abstain, clarify, or escalate instead of inventing an answer.
02
Create realistic conversations around your products, support policies, and escalation paths. Cover routine requests, incomplete information, frustrated customers, and difficult handoffs. Agree whether examples will be newly authored or derived from customer-provided records with the necessary rights and handling controls.
03
Your employees can supply the knowledge: what they check, which exceptions matter, and how they decide what happens next. We structure interviews or demonstrations and turn reviewed observations into scenarios, reference responses, and workflow examples. Your designated reviewers validate company-specific facts and procedures.
Execution & quality assurance
Consequence Labs manages dataset design, contributor coordination, instructions, review, and packaging. Your team supplies approved inputs, access decisions, and the authority to confirm company truth. Required expertise and contributor access are agreed for each project.
Choose the workflow, intended AI users, coverage, acceptance criteria, and the model or deployment decision the data will support.
Identify permitted sources, rights, contributor access, transfer, retention, and deletion requirements before sensitive work begins. Confirm a workable arrangement during scoping.
Prepare a small sample, align reviewers on the rubric, then produce and review the agreed set. Resolve disagreements and record limitations against the approved specification.
Validate the schema and acceptance criteria. Your designated owner reviews the delivery; we package accepted records with version information and documented exceptions.
What you receive
Receive files your team can use in its own evaluation or data workflow. The format, record fields, and permitted uses are agreed in advance. Dataset delivery alone does not establish that a model is ready for production.
Illustrative example
Illustrative scope, not a customer case study: an internal assistant answers employee questions about an expense policy.
01
The policy owner supplies approved policy versions and identifies common questions and exceptions. Employees explain where requests become difficult.
02
We create questions, referenced answers, and a rubric, including a case the policy does not answer. The policy owner validates the references and when to escalate.
03
Your team compares two assistant configurations on the same accepted set and examines unsupported answers before deciding what to change or test next.
Scope a private dataset
Describe the AI workflow, the company knowledge it depends on, and the decision you need to make. We will scope the inputs, contributors, review responsibilities, and dataset delivery.
Keep the first inquiry high level. Do not email sensitive company documents, customer records, employee information, or credentials; agree on an appropriate transfer and access arrangement first.
