Entity resolution
Multi-system identity and entity matching. Keys, similarity, and business rules to avoid misleading duplicates.
En clair : One person, one asset, one event: a single useful identity.
In plain terms
Rebuild a coherent context.
We correlate events from different systems to rebuild a usable context: entity identification, temporal correlation, multi-sensor fusion, conflict resolution, and consolidation.
Trust in a fusion depends on upstream quality and explicit weighting rules. We document why one source prevails, and how to resolve inconsistencies.
Depending on volume and latency, fusion can live in streaming, in an operational database, or in a lakehouse. What matters is exposure: a single, stable, consumable view.
The result is not one more dashboard, but an operational repository: who, what, where, when, with what confidence level — ready for monitoring, AI or action.
EntitiesMulti-system matching
TrustExplicit scores
LatencyReal-time or consolidated
COPShared view
What we do
Correlate to decide.
Matching mechanisms tailored to your entities, sensors and operating cadence.
Sensor fusion
Multi-sensor fusion for a reliable field reading. Useful when no single source is enough.
En clair : The field is better read with multiple signals.
Event correlation
Temporal and causal event correlation. Reduces cascading alerts and reconstructs incident chains.
En clair : We see the cause, not just the symptom.
Scoring & conflicts
Source weighting and inconsistency resolution. Confidence scores accompany every fused object.
En clair : When sources diverge, the rule is clear.
Knowledge graph
Relational representation of the business context. Facilitates exploration, AI, and complex queries.
En clair : Links between entities become queryable.
Operational view
Unified exposure for monitoring, APIs and applications. Access contracts, freshness and rights are defined from design.
En clair : Everyone looks at the same situation.
Frequently asked questions
Data fusion — details.
Does fusion necessarily require a data lake?
No. Depending on volume and latency, we can fuse in streaming, in an operational database, or in a lakehouse. The choice follows the need, not a trend.
What are typical use cases?
Monitoring of infrastructure, IoT, geospatial, cybersecurity, AI, and business process synchronization. Wherever multiple systems describe the same reality.
How do you manage source conflicts?
Priority rules, confidence scores, time windows, and sometimes human validation. Conflicts are traced, not hidden.
Is fusion real time?
It can be. Some views are consolidated periodically when the business does not require second-level freshness.
What is the link to AI?
Clean fusion is often the best fuel for RAG, agents and detection models. The opposite — AI on fragmented data — produces noise.
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