Wissly Journal
From making better use of internal documents to preparing for AI adoption.
Test an air-gapped AI coding agent's project access, execution permissions, network connections, repository instructions, secrets, and release boundaries with practical acceptance scenarios.
Separate model training restrictions, data retention, and transfer boundaries when choosing enterprise AI. Map documents, prompts, responses, and logs to practical SaaS and on-premises requirements.
Evaluate AI search for Korean HWP and HWPX documents by checking tables, footnotes, embedded images, revisions, and source evidence with representative files.
Diagnose RAG errors across extraction, chunking, hybrid search, reranking, and answer grounding. Use the same questions to locate failures and compare changes.
Platum covers the launch of Wissly Code. Explore local AI code writing and execution, code changes and app previews, and what to prepare for an isolated network.
Understand enterprise data ontology through objects, properties, relationships, and shared definitions. Explore assets, products, teams, and a practical document and ERP example.
Prepare enterprise ontology adoption by reviewing business questions, sources, identities, relationships, definitions, permissions, and updates. Includes practical evaluation cases.
Compare ontology models, knowledge graph connections, and RAG retrieval. Explore how they work together, how they relate to digital twins, and what to evaluate separately.
StartupN covers the launch of WISSLY IN A BOX. Explore its integrated hardware, local AI stack, and Wissly software, plus the conditions to review before installation.
Compare an AI appliance with SI deployment on your existing GPU servers, including hardware readiness, integrations, installation requirements, and operational support.
Prepare local models, runtimes, packages, project files, and an end-to-end test before using an AI coding agent inside an isolated network.
Compare SaaS, private cloud, and on-premises AI through data location, network access, document integration, and operational responsibility before choosing a deployment.
Review enterprise AI data flows, document permissions, external connections, retention, deletion, and incident ownership with practical questions and test scenarios.
Define workflow scope, repository integration, document permissions, evaluation, deployment, and maintenance in an enterprise document AI RFP, with practical requirement examples.
Build a practical RAG PoC scorecard using real work questions, and evaluate retrieval, answer quality, citations, permissions, and document updates separately.
Define inputs, aggregation rules, error handling, and verification when building a CSV business tool with an AI coding agent, using a concrete example.
Choose a bounded module, prepare development notes and reproduction steps, then review code changes and test results when introducing an AI coding agent to an existing project.
Prepare repository scope, representative files, access accounts, user permissions, duplicate versions, and update conditions before connecting NAS and shared folders to AI search.