Shaip helps enterprises collect, curate, de-identify, structure, annotate, and license approved internal business data—including documents, emails, records, and connected workflows—for AI training, evaluation, document intelligence, knowledge extraction, and task-specific model adaptation.
Collection scope, source permissions, de-identification requirements, and permitted AI uses are defined for every engagement.
Collect approved data across departments while preserving links between documents, emails, decisions, approvals, exceptions, and outcomes. This context helps models learn how real enterprises communicate, reason, and operate.
Prepare connected transaction records for extraction, matching, compliance checks, audit retrieval, and exception-handling agents.
Transform procedures, incidents, work orders, and procurement records into grounded knowledge and multi-step task data.
Connect discovery notes, proposals, approvals, contracts, and outcomes to train copilots and agent workflows across the sales cycle.
Structure briefs, brand guidance, campaign assets, approvals, and performance reports for content intelligence and workflow automation.
Prepare organization-approved policies, handbooks, recruiting content, onboarding records, learning material, and service communications while applying appropriate privacy controls.
Connect supplier onboarding, contracts, quotes, purchase requests, compliance records, correspondence, and performance outcomes for procurement intelligence.
The value isn't document volume — it's the relationships between systems.
In a real company, a single decision leaves a trail across many tools. Shaip preserves that trail, so models learn how enterprises actually communicate, reason, and operate — not just what individual documents contain.
Training and evaluation data for the hardest agent problems.
Evaluate whether an agent can find information, understand context, and complete tasks across multiple enterprise systems.
Realistic multi-application tasks spanning engineering, collaboration, documentation, design, and business tools.
Source code, commits, tickets, QA, and technical documentation form a rich testbed for coding agents.
Years of organizational history for studying retrieval, memory, and historical reasoning.
Questions that demand evidence from several systems, not a single source.
Research into whether agents understand ownership, dependencies, project history, and process.
Shaip works with enterprise teams to collect approved internal sources, apply privacy and quality controls, preserve business context, and deliver datasets aligned to a defined AI objective.
Define the target model, departments, source systems, document and communication types, permissions, and measurable success criteria.
Collect approved documents, emails, records, and attachments; remove duplicates; apply de-identification rules; and document provenance.
Preserve cross-document relationships, extract fields, link communications to business events, and create task-specific labels or instruction data.
Run human quality review, privacy checks, train-test leakage controls, baseline evaluation, and error analysis.
Deliver securely with data cards, agreed commercial-use terms, supported formats, version controls, and an optional update cadence.
Support legal, privacy, security, procurement, and AI-governance review with documented source permissions, configurable de-identification, quality evidence, controlled access, and clearly scoped licensing terms.
Share your target use case, departments, source systems, data types, languages, volume, and model objective. Shaip can help define a controlled collection and preparation pilot.
