Documentation / Concepts
CongLang
CongLang is the internal query and reasoning language used within Congregation DB.
The language operates over contextual decision graphs where enterprise signals remain separated by type and provenance.
CongLang allows reasoning queries across:
Deterministic signals
Signals that can be asserted directly from source systems while retaining lineage.
Probabilistic signals
Signals that carry weighting, confidence or inferred interpretation rather than direct assertion.
Derived intelligence
Signals produced through synthesis, interpretation or graph-based decision reasoning.
Language path
Conceptual language flow
The internal language surface for contextual query and reasoning expression.
A stable reasoning representation that preserves semantics before execution.
The context synthesis engine where provenance, relationships and dependency structure remain intact.
Downstream systems consume outcomes with reasoning context preserved.
CongLang expressions move through CongIR before reasoning through CongSynth and producing traceable downstream context.
Design philosophy
Design principle
Queries should never destroy the meaning or origin of the signals they operate on.
preserve signal provenance
maintain separation between signal types
support contextual reasoning
enable provable decision outputs
Example CongLang Query
CongLang examples are conceptual. They illustrate how reasoning can move across graph structure, provenance and mixed signal classes without exposing implementation details.
Portfolio exposure query
MATCH portfolio_company WHERE sector = "energy" RELATE supply_chain_partner RETURN exposure_risk WITH provenance
In plain language, this query starts from portfolio companies in the energy sector, follows their supply-chain relationships, and returns an exposure-risk view with provenance still attached to the result.
Mixed-signal reasoning query
MATCH portfolio_company WHERE covenant_status = "active" RELATE demand_signal WHERE confidence > 0.72 RETURN continuity_risk WITH deterministic_signals, probabilistic_signals, provenance
This example combines deterministic operating facts with a probabilistic demand signal so reasoning can occur across both classes without collapsing them into a single undifferentiated result.