ENTERPRISE KNOWLEDGE PLATFORM
A knowledge platform that brings your business information into decisions and action.
AIKNOW turns documents, drawings, audio and business data into knowledge AI can use. Agents work with business context and procedures to find answers, identify next steps and execute permitted actions. We configure the knowledge, updates and integrations around your operations.
Organizations using the same model can achieve different results. Relevant enterprise knowledge, data quality, system design, and evaluation all influence how useful AI is in practice.
LLMs learn patterns from their training data to support language understanding and generation. Their general capabilities do not, by themselves, provide current access to your internal information.
Model selection still matters: quality, security, latency, and cost must fit the intended use.
Internal documents, operational data, and recorded experience provide the context for your business. Their usefulness depends on coverage, quality, freshness, and appropriate access.
Organizing this knowledge helps AI responses and recommendations reflect your products, processes, and operating conditions.
Capture documented expertise and lessons from work, connect them to relevant context, and make them available within agreed access boundaries. Review proposed updates before incorporating them into shared knowledge.
We also offer AISECI, a product that supports the knowledge creation process itself.
Retrieval-augmented generation (RAG) helps ground responses in retrieved information. A basic retrieval-and-answer pipeline may still need additional capabilities to apply business conditions, retain case state, and execute authorized actions. AIKNOW combines retrieval with structured knowledge, workflow context, and execution controls.
Semantic similarity alone does not establish whether information applies to a case. Retrieval needs appropriate product, market, version, date, and access context. RAG systems can use metadata filters and authorization; those controls must be designed and maintained.
A single retrieval-and-generation step may be insufficient for complex tasks. Multi-step workflows need case state, goals, prior actions, and unresolved issues alongside conversation history.
An answer does not complete a business process. System actions require explicit tool permissions, business rules, approval or escalation paths, and outcome records. Authorization must be enforced by the system, rather than relying on prompt instructions alone.
These questions call for relevant evidence, applicability conditions, relationships, case state, and operating rules. AIKNOW combines these elements according to the workflow and available data; insufficient evidence or unresolved exceptions require clarification or review.
AIKNOW combines rule-based processing for defined procedures with AI agents for tasks that require interpretation and multi-step retrieval. Tool permissions, approval requirements, and escalation paths are configured around the workflow. People remain accountable for critical decisions, and system performance is evaluated against agreed criteria.
AIKNOW uses four knowledge engines to support different information needs. It combines retrieval with configured applicability conditions and shared case context to prepare an answer or proposed action. Conflicting sources or missing information may require further retrieval, clarification, or human review.
Retrieve relevant information and assess its applicability using available source and version context.
Support conclusions under defined conditions using relevant evidence and business rules.
Track workflow state and identify next steps, approvals, and exceptions.
Retrieve previous cases, actions, and outcomes to inform the current situation.
AIKNOW connects approved enterprise sources, extracts information, and structures it for retrieval and use by business applications and AI agents. Source coverage, extraction quality, update frequency, and access requirements are established for each deployment.
Applications for talent matching, customer support, and project management can use the platform’s knowledge capabilities through agreed integrations, with access scoped to each application and user.
FromDataToInformation
PerceptionMultimodal processing extracts information from supported documents, drawings, and audio using capabilities such as AIKNOW Capture and AIKNOW Voice. Text, tables, figures, and conversation records can be structured for downstream use. Extraction quality depends on the source; uncertain or critical results need validation.
FromInformationToKnowledge
Knowledge StructureAIKNOW extracts entities such as people, products, cases, and decisions and represents their relationships in a knowledge graph that complements document and vector retrieval. Supported knowledge can be imported and exported using Open Knowledge Format (OKF), an open specification. Export coverage and mappings are configured for each deployment; moving a complete application still requires integration and compatibility work.
FromKnowledgeToAction
Reasoning & OperationAI agents use the request and case context to select retrieval steps and permitted tools within configured limits. They can retrieve additional evidence before preparing an answer or proposed action. Agent checks do not guarantee correctness; sensitive actions follow the agreed approval and escalation rules.
These loops support ongoing improvement when outcomes are evaluated and knowledge updates are reviewed.
Technica AI has worked on more than 30 AI projects in Japan and abroad, as of September 2026. Our case studies cover broadcasting, staffing, manufacturing, and contact centers, spanning proof-of-concept evaluation, system development, and operational deployment. They illustrate our wider delivery experience; they are not all AIKNOW deployments.
Tell us about your current environment and challenges, and we will suggest where to start. If you already have a RAG system or knowledge platform, we can also discuss a phased rollout that builds on it.
Contact us to discuss a demo or an evaluation with an agreed scope, environment, and terms.
