Correlation and scoring
Entity resolution, de-duplication, ranking, and confidence models for turning noisy signals into usable intelligence.
I build the decision layers that sit between raw data and operator action: identity resolution, ranking, confidence scoring, and the logic that makes output defensible. Refreshed Apr 5, 2026 from the current capability matrix and linked archive records.
project records linked as direct proof for this capability lane
technical essays that explain or extend the same operating logic
solution pages downstream that reuse this capability structure
delivery tracks that usually show up in this slice of work
latest matrix refresh carried into this capability page
Where this capability usually matters most.
This page groups fit, outcomes, and deliverables before the proof sections so the capability reads like a working brief instead of a taxonomy stub.
- Teams trying to reduce duplicate noise and fragmented identity records.
- Products that need ranking, prioritization, and evidence-aware retrieval.
- Operators who need better confidence signals before they trust an output.
- Higher-quality results from the same raw input volume.
- Better prioritization for analysts and clearer confidence signals.
- A retrieval layer that balances exact identifiers with contextual relevance.
- Entity resolution models and identity stitching workflows.
- Ranking and scoring systems tied to operator feedback loops.
- Evaluation paths for relevance, confidence, and result quality.
I usually fit best where the hard part is not one feature. It is the system around it: reliability, reviewability, data quality, and the operator experience that determines whether the work will actually be trusted.
Best way to reach me is (929) 631-8842, on LinkedIn, or through the reserve button on the site.
Projects and technical writing behind this capability.
TraxinteL
A modular intelligence core for ingest, enrichment, entity resolution, ranking, and delivery.
SOVRINT
A narrative intelligence platform for tracking coordinated messaging, propagation paths, and sentiment drift across the open web.
Viralink
A propagation and reach analytics engine for measuring how information spreads, accelerates, and compounds across platforms.
Entity Resolution Without Illusions
Identity is probabilistic, not deterministic. Confronting the instability of digital identity in open-source intelligence.
The Hybrid Search Engine: Combining Lexical and Semantic Ranks
OSINT relevance is multi-modal. A technical exploration of why keywords fail and how to fuse BM25 with Vector Embeddings for operator-grade retrieval.
Hybrid Search in Practice: Tuning Relevance Without Lying to Yourself
Relevance tuning is an operational discipline, not a one-time configuration. A deep dive into evaluation metrics, bias suppression, and feedback loops for intelligence systems.
Solution lanes that depend on the same capability.
Due diligence
Screening workflows break when identities are fragmented and review trails depend on manual search tabs.
Entity resolution
Raw search results stay noisy unless fragmented records can be stitched into explainable entities.
Investigations workflows
Case work slows down when search, enrichment, and evidence review happen in different systems.
Threat intelligence
Threat workflows degrade when collection, retrieval, and review are treated like separate problems.
Other technical lanes in the same archive.
Collection and orchestration
Browser automation, distributed workers, scheduling, and fleet-level recovery for public-data systems that need to keep working under drift.
Evidence and forensics
Capture pipelines, artifact integrity, provenance, and review-ready delivery for teams that need defensible outputs.
Monitoring and operations
Observability, alert routing, SLAs, and operator-grade feedback loops for systems that cannot fail silently.