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Turn business and market change into reviewable risk intelligence

Hopfia Risk extracts risks, relationships, and evidence from internal material and external signals. Agents continuously update a purpose-built risk structure while linking every result to exact source evidence.

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Hopfia Risk turns internal material and external signals into traceable risk knowledge

Hopfia Risk does more than convert documents, tables, slides, and messages into structured data. A risk knowledge graph connects source evidence to structured output so every concept, relationship, and value remains traceable as teams search, review, and reconstruct the analysis.

01NEURO-SYMBOLIC INDEXING

Index meaning, not file format

Split documents and tables into pages, contexts, schemas, and data regions. Semantic vectors and explicit relationships connect every unit to its exact source location, enabling AI to retrieve and reconstruct evidence across formats.

02DYNAMIC RISK ONTOLOGY GENERATION & MANAGEMENT

A risk ontology generated and updated for each analytical purpose

Specialized agents organize risk concepts, relationships, and rules from internal material and external signals. As analysis continues, they add required structures and remove those that no longer apply.

03RISK KNOWLEDGE GRAPH MEDIATION

Turn the risk knowledge graph into a matrix people can review

A risk ontology, confirmed facts and entities, source evidence, and exact source locations form the risk knowledge graph. Hopfia Risk projects this structure into rows and columns that reviewers can inspect.

01 NEURO-SYMBOLIC INDEXING

Turn unstructured files into a knowledge index AI can interpret and reason over

Index content as vectors and concept relationships as structure, so different expressions still lead to the same evidence

PAGE + OVERLAPPING CHUNKS
An actual page from a public NIST document
SEMANTIC VECTORS
v1PAGE[ 0.18, −0.42, 0.71, 0.09, … ]
v2TOKEN[ −0.06, 0.83, 0.24, −0.37, … ]
v3TOKEN[ 0.52, 0.11, −0.68, 0.31, … ]
v4OVERLAP[ 0.29, −0.15, 0.46, 0.77, … ]
v5PAGE[ −0.33, 0.62, 0.08, −0.21, … ]
⋮
VOCABULARY EMBEDDING RECORD
WORDrevenue
CONTEXTS
TF-IDFTOTAL OCCURRENCES
VECTOR  [ 0.18, −0.42, 0.71, … ]
SEPARATE RECORDsales[ vector … ]
SEPARATE RECORDincome[ vector … ]
⋮
NEURO-SYMBOLIC INDEX
PDF FILEPPTX FILEDOCX FILErevenue[.02,.04…]growth[.01,−.01…]sales[−.00,−.00…]•••EBITDA[.01,−.02…]valuation[−.00,−.01…]•••cash[.00,−.00…]liabilities[−.00,+.00…]equity[−.00,−.00…]•••manufacturing[−.01,.05…]inventory[−.01,−.01…]•••distribution[−.00,−.01…]pricing[−.02,−.01…]demand[−.03,.02…]•••capital[.05,.04…]expenses[−.05,−.01…]forecast[.00,−.01…]•••

Separate spreadsheet metadata, schema, and body into vectors to recover the meaning of each value and its exact source cell

STRUCTURE MAP
Operating performance spreadsheet built with a fictional company name and fictional figures
TITLE / PERIOD A1:H4 · HEADERS A6:H6REPEATED RECORDS A7:G15FORMULAS H7:H15 + C:G9,11,13,15
CONTEXT ASSEMBLY
SCHEMA · METADATA REGIONSA1:H4 table identity · period · scenarioA6:H6 column meaning · unit · year
DATA REGIONA7:B15 row identifier · metricC7:H15 values · formulas
SEMANTIC CHUNK PACKAGEcontent schema context + data slicecontext NOVA DEMO · BASE CASE · KRW MILLIONSbody module revenue · 2024A–2028E · values/formulasranges A1:H4 + A6:H6 + A7:H9
EMBED & STORE
EMBEDDING MODEL[ 0.18, −0.42, 0.71, … ]SEMANTIC VECTOR
VECTOR RECORDid chunk_uuidvector semantic vectortype chunkscope access boundary
SOURCE LOCATORid same chunk_uuidsheet operating reviewrange A7:H9tokens chunk size

02 DYNAMIC RISK ONTOLOGY GENERATION & MANAGEMENT

Build and continuously update a risk structure for each analytical purpose

Specialized agents organize risk concepts, relationships, and rules into a risk ontology, then add or prune structures as new material and external signals arrive.

TOP-LEVEL ONTOLOGYOPERATIONS SUB-ONTOLOGYSAME ASSAME ASSAME ASTHINGAGENTRISK CONCEPTINSTRUMENTMEASUREPLACEOCCURRENCEPRODUCT / TECHNOLOGYMEASUREFINANCIAL METRICREVENUECOSTCASHRISKCONCEPTREGULATORY MATTERRISKCONTRACTDATA GAPMEASUREOPERATIONAL METRICCAPACITYVOLUMEFINANCE SUB-ONTOLOGYLEGAL SUB-ONTOLOGYLEGAL DATA GAP +×TOP AGENTFINANCE AGENTLEGAL AGENTOPERATIONS AGENT
IS AParent and child conceptsSAME ASEquivalent concepts

03 RISK KNOWLEDGE GRAPH MEDIATION

Review data with different structures and contexts in one risk matrix

The risk knowledge graph connects material with different structures and expressions through shared risk concepts, preserves source evidence and exact source locations, and projects the result into a risk matrix built for review.

SOURCE DOCUMENTPDF

Paragraph · Definition · Exact page

UNSTRUCTURED TEXT
DATA TABLESPREADSHEET

Entity · Metric · Value

STRUCTURED CELLS
RISK KNOWLEDGE GRAPH MEDIATIONSHARED SEMANTIC LAYER
ISSUESCONVERTS TOADJUSTSPROTECTSEVIDENCED BYISSUERCOMPANYCOMMON STOCKCONVERSION TARGETCONVERSION PRICEADJUSTMENT BASISSERIES D PREFERREDINVESTOR PROTECTIONSERIES DANTI-DILUTION CLAUSEPUBLIC FILING · P.19SOURCE EVIDENCE
INSTANCEFACT / RISKPROVENANCE
RISK MATRIX22 RECORDS
SELECTED MATRIX ROWSeries D anti-dilution clause→ANNOTATION→Public corporate filing
ORIGINAL SOURCE OPENED

Connect the risks you manage to a reviewable risk matrix

Define the first scope around internal material, external signals, and your review process.

Discuss Hopfia Risk