Poor LLM accuracy and ballooning costs—we trace those root causes and solve them with ontology and knowledge graphs.
Feed AI only the knowledge it needs, raising accuracy while keeping costs down.
Many companies feel that "costs keep rising but accuracy never comes." The cause isn't the model—it's that knowledge has never been structured.
An LLM doesn't remember the past across sessions. You have to hand over knowledge again every time, and insight never accumulates the more you use it.
Throwing in every piece of seemingly relevant knowledge inflates token billing. Costs keep piling up without translating into results.
More noise actually lowers answer accuracy and triggers hallucinations. Search misses the mark and never reaches the information you need.
We structure knowledge with ontology and knowledge graphs, then pass the LLM only what it truly needs at that moment. While keeping costs down, we turn your organization's knowledge into an asset AI can use correctly.
We won the "Legal Data Utilization Award" for structuring legislative data. With this approach we structured knowledge while reducing data volume by about 35%.
We built and operate a knowledge graph platform combining Neo4j and a vector DB as our own AI development foundation. A design backed by real-world operation.
Our CEO—an IT consultant for listed companies, a former CTO, and a Digital Agency Digital Promotion Committee member—works alongside you directly, from strategy to implementation.
Structure your policies, manuals, and past projects to power an in-house AI assistant that answers with supporting evidence.
Turn "we deployed RAG but accuracy isn't there" around with ontology, terminology control, and hybrid search.
Structure laws, internal regulations, and contracts to enable tracking the impact of amendments and cross-referencing clauses.
Unify scattered master data and specifications with a knowledge graph to enable cross-cutting queries.
Of course. Rather than jargon, we start from "which business processes and what knowledge you want AI to use." Just tell us your current challenges first.
Yes. You can start incrementally from a small-scale PoC design focused on a specific process, expanding the scope as you confirm results.
Yes. Cases where existing RAG isn't delivering accuracy are exactly where there's the most room to improve through ontology design and rethinking search.
The first consultation is free. We provide a quote based on project scale (hourly, from JPY 30,000/hour). See service details here.
"We deployed an LLM but accuracy isn't there." "Our internal search keeps missing." Let's solve those root causes together in a free consultation.