The operational knowledge AI agents are missing.
Agents only do real work if they learn from how real work is done. We license operational data, chats and internal knowledge from organizations that agree to share it, anonymized and delivered with its provenance record.
Learning from the real thing.
Agents that follow real procedures
Learning from how tickets, requests and processes actually get resolved.
Assistants that use the language of real work
Grounding in how teams really talk, decide and escalate.
Evaluation on realistic tasks
Testing models against the messy conditions of real organizations.
Three kinds of material.
Conversations
Customer support chats, internal messaging and similar dialogue, with personal identifiers removed.
Internal knowledge
Guidelines, procedures, tickets and documentation that show how work is done.
Operational records
Structured and semi-structured records from business processes, where the owner has agreed to license them.
Domains, volumes and languages depend on which owners we work with.
Rights first, then data.
| Who owns it | The enterprise that created the data licenses it to us under a written agreement covering AI training use. We do not collect it from the open web. |
|---|---|
| Anonymization | Personal identifiers are removed before delivery. The method and checks are recorded for each dataset. |
| Consent | Where the data involves individuals, the basis for using it is documented and stored with the dataset. |
| Quality | Each dataset is reviewed against a written specification. Results are available to your team on request. |
| What you receive | The data, the license terms, and a provenance record covering source, processing and version. |
Pair the data with experts and a platform.
Expert validation and evaluation
Domain experts can check the data and test the models trained on it.
Platform workflows
Annotation, review and governance records in one place, from data to deployment.
Describe the data you need.
Tell us the domain, language, volume and intended use.