{ "id": 5786, "date": "2025-06-08T15:36:54", "date_gmt": "2025-06-08T08:36:54", "guid": { "rendered": "https:\/\/technica.ai\/?p=5786" }, "modified": "2025-06-08T15:55:31", "modified_gmt": "2025-06-08T08:55:31", "slug": "aiknow", "status": "publish", "type": "post", "link": "https:\/\/technica.ai\/en\/news-release\/aiknow\/", "title": { "rendered": "Technica Launches Hybrid RAG Service \u201cAIKNOW\u201d" }, "content": { "rendered": "
\n

A Next-Generation AI Solution for the Workplace, Combining Knowledge Graphs and Vector Search for Accurate and Secure Results<\/strong><\/h4>\n

Tokyo, February 13, 2025, 11:10 AM<\/p>\n

Technica Inc. (Headquarters: Chiyoda-ku, Tokyo; CEO: Tsuyuri Satoki) has launched the hybrid RAG (Retrieval-Augmented Generation) service \u201cAIKNOW.\u201d As a hybrid RAG that combines multiple search methodologies, AIKNOW is a next-generation AI solution that supports business transformation with generative AI and provides end-to-end consulting, from introduction to operation.<\/p>\n

The Potential for Business Transformation with Generative AI<\/strong><\/h4>\n

Generative AI has the potential to greatly impact operational efficiency and decision-making. However, its introduction often requires significant changes to conventional business processes, such as knowledge management, decision-making procedures, and workflow optimization. If these changes are not properly addressed, expected results may not be achieved.<\/p>\n

Additionally, many organizations lack clear methodologies for implementing generative AI, resulting in frequent failures during the PoC (proof of concept) stage. This is often due to a technology-driven approach that overlooks the real needs and challenges at the operational level.<\/p>\n

Successful adoption of generative AI requires a deep understanding of day-to-day operations and solutions to practical challenges. Leveraging our management team\u2019s 20+ years of consulting experience, we thoroughly analyze our clients\u2019 current business processes.<\/p>\n

With the combination of \u201cAIKNOW\u201d and consulting, we provide comprehensive support\u2014from planning an effective generative AI introduction, through system construction, to post-implementation business process improvement.<\/p>\n

Key Features of AIKNOW<\/strong><\/h4>\n
Field-Driven Approach for Business Improvement with Generative AI:<\/strong><\/h5>\n

AIKNOW enhances the accuracy of generative AI\u2019s responses for unique corporate data by combining conventional vector similarity search with graph search based on relationships. This approach delivers practical, actionable insights for business operations. By promoting generative AI tailored to business processes, we help drive true operational transformation.<\/p>\n

Accuracy and Reliability with Hybrid RAG:<\/strong><\/h5>\n

By leveraging knowledge graphs, AIKNOW adds reasoning capability to RAG, boosting search accuracy. This reduces hallucinations and provides highly accurate, reliable information.<\/p>\n

Customizable and Integrative for Business-Specific Needs:<\/strong><\/h5>\n

We optimize data structures and customize the generative AI system according to client requirements.
Moreover, AIKNOW\u2019s SDK and API enable seamless integration with existing client systems and tools (such as DocuWorks, Salesforce, Teams, etc.), facilitating smooth transformation of entire business workflows.<\/p>\n

Multi-LLM Support and Flexible Deployment:<\/strong><\/h5>\n

In addition to leading LLMs such as GPT and Gemini, open-source Llama is also available.
For organizations handling highly confidential data, such as financial institutions or healthcare providers, Llama can be installed on-premises and operated as a private LLM. This enables a fully secure RAG service without relying on external clouds while maximizing in-house data utility. Eliminating cloud dependence also helps reduce token usage costs.<\/p>\n

AIKNOW\u2019s Vision<\/strong><\/h4>\n

Through AIKNOW, Technica maximizes the value of corporate information assets to drive operational efficiency and sustainable business reform. By unlocking the potential of generative AI and providing highly practical solutions, we support our clients\u2019 competitiveness and aim to build smarter business environments.<\/p>\n

About Technica Inc.<\/strong><\/h4>\n

Technica employs around 40 AI scientists and engineers, providing end-to-end services from AI consulting to system development. With extensive experience in machine learning, deep learning, and generative AI applications, the company is highly regarded both domestically and internationally, especially in AI research and development. Our management team combines expertise in both the research and business application of AI, leading clients from AI strategy formulation to implementation. By integrating state-of-the-art technology with business transformation expertise, Technica supports the sustainable growth of its clients.<\/p>\n<\/div>", "protected": false }, "excerpt": { "rendered": "

A Next-Generation AI Solution for the Workplace, Combining Knowledge Graphs and Vector Search for Accurate and Secure Results Tokyo, February 13, 2025, 11:10 AM Technica Inc. (Headquarters: Chiyoda-ku, Tokyo; CEO: Tsuyuri Satoki) has launched the hybrid RAG (Retrieval-Augmented Generation) service \u201cAIKNOW.\u201d As a hybrid RAG that combines multiple search methodologies, AIKNOW is a next-generation AI […]<\/p>", "protected": false }, "author": 2, "featured_media": 0, "comment_status": "closed", "ping_status": "open", "sticky": false, "template": "", "format": "standard", "meta": { "inline_featured_image": false, "footnotes": "" }, "categories": [ 65 ], "tags": [], "class_list": [ "post-5786", "post", "type-post", "status-publish", "format-standard", "hentry", "category-news-release" ], "acf": [], "_links": { "self": [ { "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/posts\/5786", "targetHints": { "allow": [ "GET" ] } } ], "collection": [ { "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/posts" } ], "about": [ { "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/types\/post" } ], "author": [ { "embeddable": true, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/users\/2" } ], "replies": [ { "embeddable": true, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/comments?post=5786" } ], "version-history": [ { "count": 9, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/posts\/5786\/revisions" } ], "predecessor-version": [ { "id": 5795, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/posts\/5786\/revisions\/5795" } ], "wp:attachment": [ { "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/media?parent=5786" } ], "wp:term": [ { "taxonomy": "category", "embeddable": true, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/categories?post=5786" }, { "taxonomy": "post_tag", "embeddable": true, "href": "https:\/\/technica.ai\/en\/wp-json\/wp\/v2\/tags?post=5786" } ], "curies": [ { "name": "wp", "href": "https:\/\/api.w.org\/{rel}", "templated": true } ] } }