An Ontology-Based AI Decision-Making Engine That Goes
Beyond Answers to Propose Validated Action Plans

There is a difference between generating plausible answers and making executable decisions backed by evidence and simulation.
AkasicAI helps deliver optimal decision-making through validated action plans only.
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Today’s RAG generates plausible answers well, but it does not connect to decision-making and execution.

AI Search Without Aligned Terminology and Standards

When terms carry different meanings across systems, the quality of answers varies even for the same question. AI without unified standards cannot guarantee consistent responses.

Similarity-Based Document Search That Misses Relationships and Context

Relying on similarity-based document search fails to identify causal relationships and connection structures. This can lead to unreliable results in business queries.

Search-and-Summary-Focused RAG That Does Not Lead to Actionable Plans

Finding and organizing information is one thing; making decisions and presenting action plans is another. Without reasoning logic, it cannot lead to decision-making.

AkasicAI goes beyond search to connect judgment and execution.

Experience AI that makes evidence-based decisions on top of defined rules.
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Beyond answers to execution and decision-making

this is how AkasicAI’s AI agent operates.

  • 1. Automatically Combining the Most Suitable Search Methods Based on the Query

    Different questions require different search methods. AkasicAI identifies the intent behind each query and automatically determines the optimal combination of vector, graph, and SQL search.

  • 2. Tracing Causes and Effects by Following Relationships Between Data

    AkasicAI traces how one event impacts other data through graph-based relationship structures. This is the kind of context that simple similarity search cannot uncover.

  • 3. Accurate Aggregation and Full-Scan Queries Without Omissions via RelationRAG

    Delivers accurate and complete results for business-type queries such as overall status reviews and condition-based list retrievals, ensuring reliability without omissions.

  • 4. Presenting Optimal Action Plans After Simulation-Based Validation

    Generates execution candidates through reasoning logic, then validates their impact using AkasicON’s What-if simulation. Only validated action plans are presented as final recommendations.

A Decision-Making Engine That Produces Executable Judgments, Not Just Plausible Answers

AkasicAI’s results include evidence, context, and simulation-based validation. They do not stop at answers—they lead to actionable decisions applicable to real-world operations.
"What are the causes of the Q3 revenue drop and proposed countermeasures?"
Business Query Input
STEP 01
Hybrid Search
Identifies query intent to
automatically combine optimal search methods
Vector Graph SQL
STEP 02
Reasoning & Action Generation
Traces relationships and context
to derive evidence-based action candidates
RelationRAG Context
STEP 03
Simulation Validation
Pre-assesses impact and risks
through What-if simulations
What-if Verified
Present Validated Action Plans
Decisions backed by evidence, context, and simulation results
VERIFIED

Consistent Standards for the Same Questions—Traceable Results the Business Can Trust

What enterprises need from AI is not plausible answers, but repeatable and traceable results built on consistent standards.
10:30 AM
"What is the overall risk status and primary impact scope for Client B?"
03:15 PM
"What is the overall risk status and primary impact scope for Client B?"
Identical Query
Define Entity Relations
Ontology-based
Apply Policies & Rules
Consistent Standards
Fixed Reasoning Path
Guaranteed Identical Logic
Result for Risk Manager A
IDENTICAL
Risk Level
HIGH — 12.4% Delinquency Rate
Impact Scope
3 Associated Clients, $1.5M AR
Basis
7 Entity Relations, 3 Policy Rules Applied
Result for Ops Manager B
IDENTICAL
Risk Level
HIGH — 12.4% Delinquency Rate
Impact Scope
3 Associated Clients, $1.5M AR
Basis
7 Entity Relations, 3 Policy Rules Applied
100% Match
EVIDENCE TRAIL — Trace the basis of results back to the raw data level
Source Data
Applied Rules
Reasoning Path
Final Result

A Built-in Action Generation Workflow from Action Plan Creation to Simulation-Based Validation

AkasicAI generates execution candidates through reasoning logic, then verifies their impact through AkasicON’s simulation. Only action plans that reflect validated results can be reviewed, minimizing operational risk.
STEP 01
AkasicIN
Preparation
Collecting scattered data and standardizing it for AI utilization
STEP 02
AkasicDB
Integration
Unified storage for Vector, Graph, and SQL in one engine
STEP 03
AkasicON
Governance
Defining semantics and rules to make AI controllable
STEP 04
AkasicAI
Decision
Proposing evidence-based and validated action plans

GraphAI: A trusted AI data technology company across all industries.
GraphAI’s AI data solutions support fast and accurate enterprise decision-making.

What AI needs is not better answers—it is executable decisions.

See how AkasicAI transforms your enterprise’s decision-making.

Request a Demo

Interested in Adopting GraphAI Solutions?

Have questions about GraphAI solutions?
Contact us and we will help you find the answers.





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