Capturing Knowledge
Extract content from documents, handwriting and speech, retaining relevant structure and context for downstream use.
Putting new AI to work means knowing exactly where it applies—and where its limits lie.
We continuously evaluate research from around the world while conducting our own research into foundational technologies such as knowledge representation and reasoning.
We carry these insights into our products and tailored solutions, refining them into technology that meets complex business requirements.
These nine technologies support the broader capabilities described on our Services page. They can be combined according to the information sources, business tasks and deployment requirements of each project.
Extract content from documents, handwriting and speech, retaining relevant structure and context for downstream use.
Organize concepts, relationships and procedures, with portable representations where appropriate for the application.
Use knowledge for retrieval and authorized actions, maintain it through operational feedback, and assess suitable model deployment configurations.
Interpret diagrams, tables and formulas through layout and context—beyond text extraction.
Technical documents combine text with tables, figures, formulas and layout. Visual document understanding analyzes these elements together, retaining relationships such as values and conditions or figures and captions for downstream use.
Technica AI combines vision-language models with OCR and document processing to prepare structured content for retrieval and knowledge applications. AIKNOW Capture applies this approach to technical materials, with extraction quality assessed on representative documents.
Read handwritten Japanese through visual evidence and context.
Handwritten forms and records contain information that can be difficult to retrieve and reuse. Handwriting AI-OCR combines visual recognition with field labels and surrounding context to extract structured data from varied writing.
Technica AI applies image processing and field-specific checks to support business use through AIKNOW Capture. Accuracy and confidence scoring are evaluated on representative forms, with additional checks defined for the intended application and critical fields.
Organize conversations with their context intact.
Calls and meetings contain useful discussions, decisions and action items. Speech intelligence combines transcription with speaker organization, topic segmentation and contextual analysis to prepare conversations for retrieval, review and downstream knowledge applications.
AIKNOW Voice supports Japanese and English, including real-time processing, and configurable personal-information masking in text. Original transcripts are retained, while extraction and masking quality are assessed using representative recordings and sample tests for the intended application.
Reveal meaning through the relationships between pieces of information.
A knowledge graph records explicit relationships between concepts, such as prerequisites, dependencies and related skills. These connections complement text search and help users explore relevant context across documents, processes and business entities.
Technica AI defines relationships for the target domain and combines graph-based retrieval with other search methods. AIKNOW Matching uses a configured skill ontology to support recommendations; related skills indicate possibilities to verify, while people make final selection decisions.
Exchange and reuse knowledge through an open specification.
Open Knowledge Format represents linked knowledge using Markdown files and structured metadata. The open specification provides a human-readable format for exchanging content between compatible applications, helping organizations retain access to their knowledge outside its original system.
AIKNOW supports configured OKF import and export. Portability depends on supported versions and content mappings; integrations, access permissions and application behavior require separate assessment when moving knowledge between systems or adapting it for a new use.
Combine knowledge of how work gets done with the information that grounds decisions and answers.
Content knowledge provides facts and reference information; process knowledge defines checks, decisions and actions. Connecting these roles helps AI applications use current information within an agreed procedure, including prerequisites, exceptions and handoff conditions.
Technica AI structures procedures separately from their supporting content to reduce duplication and support maintenance. AIKNOW Answer applies this approach to configured customer-service automation, with operating rules, action permissions and knowledge-update controls agreed for the intended application.
Plan, search and refine the evidence to build answers to complex questions.
Agentic RAG adds planning and iterative retrieval to retrieval-augmented generation. An agent can break a question into parts, assess available results and refine its search to gather context across permitted sources and tools.
Technica AI uses this approach to support enterprise knowledge applications through AIKNOW. Iteration limits, access controls and source references help manage the process; answer quality, handling of missing evidence and response time are evaluated for the intended workload.
Apply knowledge to real problems, learn from new discoveries and refine it for the next challenge.
Solve & Evolve Loops connect daily problem-solving with knowledge maintenance. Operational records, corrections and recurring issues can reveal useful lessons and candidate updates, while broader reviews assess knowledge freshness and the impact of changes.
Technica AI configures update controls for each application: AIKNOW Answer reviews and tests proposals, while AIKNOW Project incorporates knowledge automatically. The approach supports traceable maintenance and measured improvement, rather than assuming that every update makes the system more capable.
Adapt generative AI to your confidentiality, computing and domain requirements.
Quantization reduces model weight precision to lower memory requirements. Tuning adapts selected behavior to domain terminology, style or output formats. These techniques provide options for balancing model capability with available hardware and deployment requirements.
Technica AI evaluates configurations on representative business workloads, including local or private environments where needed. Quality, total memory use and response time are assessed together; smaller weights do not guarantee faster responses or replace security controls and current reference knowledge.
Tell us about your information sources, intended use and deployment requirements.
We can assess suitable technologies and a practical evaluation scope.