Semantia

What it is

Not a tool — the intelligence foundation built into your company.

Semantia is the foundation that accumulates and activates a company's internal and external information — and the decisions made every day — as knowledge the whole organization can reuse.

What we aim for is not a tool or a single feature. It is a state in which each company's purpose, values, and judgement criteria are structured — and remain continuously usable as the organization's own intelligence.

Decisions and know-how that easily become person-dependent — moved into a shape the whole organization can share and reuse. Semantia supports that through six processes.

Six processes

From data to decisions, to organizational intelligence.

Data integration

Turning information from inside and outside the company into knowledge that holds meaning — not just raw data.

We connect information scattered inside and outside the company, integrating it in a meaningful form. ERP (accounting, sales, inventory), CRM/SFA, communication data such as Slack and email, documents like contracts and proposals, logs from operational systems and various SaaS — and external information surrounding the business such as news, papers and patents, market data, regulatory and legal updates. What matters is not simply aggregating data, but connecting it by meaning. We resolve different notations referring to the same customer, multiple codes representing the same product, and varying terminology across departments — bringing the company into a state where information can be treated consistently.

Key technologies

Data IntegrationSemantic SearchEntity ResolutionData Governance

Implementation approach

Data integration via iPaaS (Fivetran, Airbyte, etc.) and custom connectors
Entity-resolution design combining ML, rules, and human review
External-data utilisation under proper licensing
Building a meaning-based search foundation with embedding technology

Our perspective

Data integration that grows over time

Data integration is not something completed in a single pass. Entity resolution and data-quality improvements especially require ongoing tuning through operation. In Semantia, we don't aim for perfection from the start — we design with the assumption that improvement happens through real-world operation. We also place importance on designing external-data usage scope and access permissions in line with contractual and authorisation frameworks.

Decision extraction

Turning judgements and background knowledge — which tend to become person-dependent — into knowledge that can be reused across the entire organisation.

We organise and structure the judgements that exist within the organisation, along with the context behind them. From documents, chat, and meeting records, we extract when, by whom, in what situation, on what basis, and what kind of decision was made — organising it in a form that can be reused later.

Key technologies

LLMNLPIntent RecognitionSemantic Parsing

Implementation approach

Detect decision events from meeting records, chat, and documents
Structure including the reasoning and surrounding context
Preserve source links to maintain verifiability
Human review and annotation on important decisions

Our perspective

Treating 'facts' and 'inferences' separately

Not every decision context is perfectly recorded. For that reason, Semantia handles documented facts and information inferred from relationships separately. Rather than treating inferences as facts, we prioritise organising knowledge in a way that maintains verifiability. For sensitive areas such as HR, executive, and strategy, we design the scope carefully.

Decision structuring

Turning each company's decision-making structure itself into an intelligence foundation that continuously grows.

We accumulate extracted judgements as knowledge the organisation can continuously use. Actions, context, reasoning, results, and relationships are structured to form the company's unique 'decision structure.' In Semantia, rather than just managing history, we accumulate 'in what situation, which judgement was effective' as relationships.

Key technologies

Knowledge GraphVector DatabaseOntology ModelingProbabilistic Reasoning

Implementation approach

Ontology design combining industry-common and company-specific models
Hybrid configuration of graph DB and vector DB
Confidence-metric design tailored to use cases

Our perspective

Multi-faceted reliability evaluation

Decision reliability cannot be expressed by a single score alone. In Semantia, we evaluate by combining multiple perspectives: the amount and quality of supporting data, consistency with similar cases, alignment with past outcomes, consistency across multiple inferences, and human review results. This lets us properly separate 'judgements used as reference information' from 'judgements usable in operational processes.'

Reasoning and reproduction

Decision support where you can confirm not just the 'answer' but 'why that judgement was reached.'

Based on accumulated knowledge and past cases, we derive judgement candidates appropriate to the current situation. In Semantia, rather than just searching, we present judgements with reasoning, drawing on similar cases, relationships, past results, and the organisation's specific judgement tendencies.

Key technologies

Graph ReasoningRAGMulti-hop ReasoningExplainable AI

Implementation approach

Relationship-based search and reasoning using GraphRAG
Presenting the grounds and references for inferred results
Cross-checking inferences across multiple models for consistency

Our perspective

Reasoning grounded in verifiability

Semantia places importance on being able to trace 'why that conclusion was reached.' During inference, we present the relationships with referenced knowledge and documents in a way the user can verify the basis for the conclusion. We also design for consistency — for example, by not directly using relationships that don't exist in the knowledge structure.

Execution and automation

Using AI not just for information retrieval, but as part of the operational process itself.

Semantia is designed not only for decision support, but also with operational-process integration in view. Depending on the judgement and confidence level, operation can be phased — suggestion only, execution after approval, conditional automatic execution, and so on.

Key technologies

AI AgentsWorkflow EngineMulti-Agent SystemsAPI Integration

Implementation approach

Connecting agent frameworks with operational systems
Audit-log and rollback design
Execution control at the operation-unit level
Multi-level approval flow design

Our perspective

Phased automation

AI utilisation in operations is not about advancing automation uniformly — it requires staged design based on risk and impact scope. In Semantia, we proceed gradually: first suggestion, then approval-based execution, and finally automation within limited domains — matching operational understanding and maturity.

Learning and evolution

Bringing the company's activities themselves into a state of continuous learning and evolution.

Semantia continuously improves the company's specific intelligence through execution results and feedback. Beyond explicit feedback such as approvals, rejections, and corrections, we also update the knowledge structure based on impacts to operational KPIs.

Key technologies

Continuous LearningFeedback AnalyticsAdaptive Intelligence

Implementation approach

Integration of explicit and implicit feedback
Lightweight continuous learning with stability priority
Knowledge management via differential updates
Re-evaluation and re-training via drift detection

Our perspective

Learning from both success and failure

Continuous improvement requires not only success cases, but also the history of corrections, rejections, and refinements. In Semantia, we accumulate 'judgements that didn't work out' as well, suppressing judgement bias and forming a more stable knowledge foundation. We also place importance on a structure that can keep updating the knowledge itself as markets, regulations, and organisational structures change.

CONTACT

We support AI and system adoption that drives business growth.

From clarifying "what to build" to design, development, and operations. Practical support grounded in your business, operations, and data.