Update case-study-trustgraph.md

qdrant-landing/content/blog/case-study-trustgraph.md

@@ -22,7 +22,7 @@ tags:
 
 ![TrustGraph Overview](/blog/case-study-trustgraph/trustgraph-bento-box-dark.jpg)
 
-# **TrustGraph \+ Qdrant: A Technical Deep Dive**
+# TrustGraph \+ Qdrant: A Technical Deep Dive
 
 When teams first experiment with agentic AI, the journey often starts with a slick demo: a few APIs stitched together, a large language model answering questions, and just enough smoke and mirrors to impress stakeholders.
 
@@ -33,31 +33,31 @@ This is exactly the gap TrustGraph set out to close. From day one, they designed
 ![Failure mode map — “From POC to production](/blog/case-study-trustgraph/failure-map-poc-to-production.png)
 *Failure mode map — “From POC to production.”*
 
-## **Building for Production, Not Demos**
+## Building for Production, Not Demos
 
 TrustGraph’s architecture doesn’t retrofit demo code for the enterprise; it was engineered from scratch for resilience. The system is fully containerized, modular, and deployable across cloud, virtualized, or bare-metal environments.
 
 At its core are three pillars:
 
-* **A streaming spine with Apache Pulsar**. Persistent queues, schema evolution, and replayability provide resilience. If a process fails, it automatically restarts and resumes without data loss.
+* A streaming spine with Apache Pulsar. Persistent queues, schema evolution, and replayability provide resilience. If a process fails, it automatically restarts and resumes without data loss.
 
-* **Graph-native semantics**. Knowledge is modeled in RDF, with SPARQL templates guiding retrieval. This reduces dependence on brittle, model-generated queries and ensures answers are precise and auditable.
+* Graph-native semantics. Knowledge is modeled in RDF, with SPARQL templates guiding retrieval. This reduces dependence on brittle, model-generated queries and ensures answers are precise and auditable.
 
-* **Qdrant vector search**. Entities are embedded and stored in Qdrant, enabling fast, reliable similarity search that integrates seamlessly into the graph-driven workflow.
+* Qdrant vector search. Entities are embedded and stored in Qdrant, enabling fast, reliable similarity search that integrates seamlessly into the graph-driven workflow.
 
 ![Architecture overview](/blog/case-study-trustgraph/architecture-overview.png)
 *Architecture overview*
 
-## **From Documents to Knowledge**
+## From Documents to Knowledge
 
-Instead of breaking documents into arbitrary chunks, TrustGraph extracts **facts**. An LLM identifies entities and relationships, assembling them into a knowledge graph. In parallel, embeddings of entities are stored in Qdrant.
+Instead of breaking documents into arbitrary chunks, TrustGraph extracts facts. An LLM identifies entities and relationships, assembling them into a knowledge graph. In parallel, embeddings of entities are stored in Qdrant.
 
-This dual representation allows queries to ground themselves in both **semantic similarity** and **graph structure**. For example, asking “Tell me about Alice” retrieves the “Alice” entity via Qdrant and maps it to her connections in the graph, rather than just surfacing sentences that happen to contain her name.
+This dual representation allows queries to ground themselves in both semantic similarity and graph structure. For example, asking “Tell me about Alice” retrieves the “Alice” entity via Qdrant and maps it to her connections in the graph, rather than just surfacing sentences that happen to contain her name.
 
 ![Ingestion process](/blog/case-study-trustgraph/ingestion-process.png)
 *Ingestion process*
 
-## **Retrieval That Goes Beyond RAG**
+## Retrieval That Goes Beyond RAG
 
 When a query enters the system, it follows a deterministic path:
 
@@ -74,9 +74,9 @@ This approach surpasses traditional RAG, which stops at semantically similar chu
 ![Query process](/blog/case-study-trustgraph/query-process.png)
 *Query process*
 
-## **Agentic AI at Scale**
+## Agentic AI at Scale
 
-TrustGraph’s retrieval capabilities sit within a broader **agentic AI framework**. Developers can orchestrate pipelines that combine:
+TrustGraph’s retrieval capabilities sit within a broader agentic AI framework. Developers can orchestrate pipelines that combine:
 
 * GraphRAG for structured fact retrieval
 
@@ -91,22 +91,22 @@ This gives enterprises the flexibility to build retrieval pipelines that integra
 ![Ingestion + querying process](/blog/case-study-trustgraph/ingestion-querying-process.png)
 *Ingestion & querying process*
 
-## **Outcomes That Matter in Production**
+## Outcomes That Matter in Production
 
 By combining a resilient streaming backbone, graph-native semantics, and Qdrant-powered retrieval, TrustGraph delivers outcomes that demo architectures simply can’t:
 
-* **Determinism** — Template-driven SPARQL and Qdrant similarity search eliminate fragile query synthesis.
+* Determinism — Template-driven SPARQL and Qdrant similarity search eliminate fragile query synthesis.
 
-* **Resilience** — Pulsar pipelines replay and recover automatically, keeping systems responsive during failures or rolling updates.
+* Resilience — Pulsar pipelines replay and recover automatically, keeping systems responsive during failures or rolling updates.
 
-* **Scalability & Sovereignty** — The platform runs on diverse hardware stacks, including non-NVIDIA GPUs, and supports strict European data sovereignty requirements.
+* Scalability & Sovereignty — The platform runs on diverse hardware stacks, including non-NVIDIA GPUs, and supports strict European data sovereignty requirements.
 
-* **Developer Simplicity** — Qdrant’s open-source, containerized design makes scaling straightforward and reduces operational friction.
+* Developer Simplicity — Qdrant’s open-source, containerized design makes scaling straightforward and reduces operational friction.
 
 *“We haven’t had a reason to revisit alternatives. Qdrant checks the boxes for speed, reliability, and simplicity—and it keeps doing so.”*  
  — *Daniel Davis, Co-founder, TrustGraph*
 
-## **From Demos to Durable Infrastructure**
+## From Demos to Durable Infrastructure
 
 TrustGraph shows how agentic AI can evolve from flashy demos into **mission-critical enterprise software**. By grounding retrieval in graph semantics and Qdrant’s vector engine, they push non-determinism to the edges while maintaining uptime, auditability, and sovereignty.