The Challenge: Structured Graphs from Unstructured Chaos
Even the world’s leading graph database provider—serving 84% of Fortune 100 companies—faced a modern paradox: as unstructured content exploded, the ability to extract usable knowledge from it remained frustratingly outdated. Unstructured content was multiplying, but turning it into usable knowledge remained labor-intensive and inaccessible. PDFs, webpages, and transcripts held valuable insights—but the journey from text to knowledge graph was too slow, too manual, and too technical.
Manual extraction processes burdened subject matter experts (SMEs) and data teams. Schema definition had to come before experimentation, limiting agility. And even when graphs were built, non-technical users couldn’t query them without help. In a world of instant AI summaries, the graph creation experience felt outdated.
Beyond inefficiency, this also limited knowledge reuse. Without automation, high-value insights were locked in documents—difficult to connect, scale, or activate across business functions.
Without automation, the knowledge graph process remained a bottleneck in an otherwise AI-first world.
The Turning Point: Building the LLM Graph Builder
The limitations were clear—and so was the opportunity. To reimagine how unstructured data becomes queryable knowledge, the team partnered with Persistent Systems. Together, they co-developed the LLM Graph Builder—a GenAI-powered platform that transforms text into connected, explainable graphs in minutes.
The solution used LLMs to ingest content from:
- PDFs, webpages, YouTube transcripts
- Cloud storage (AWS S3, GCS)
- Proprietary enterprise repositories
From this, the engine automatically extracted:
- Entities and relationships
- Domain-specific context
- Suggested graph schemas or accepted user-defined models
This automated transformation wasn’t just about speed. It marked a fundamental rethinking of the graph building lifecycle—from data acquisition to interface design—enabling organizations to embed intelligence where it was previously infeasible.
Built for the Real World: Flexible, Fast, and Explainable
With the core transformation engine in place, the next step was to ensure it worked across real-world conditions—multiple use cases, domains, and user profiles. That meant building for flexibility, performance, and trust.
- Multi-LLM Integration: Combined OpenAI, Gemini, Claude, Llama3, and more for accuracy across languages and domains. This allowed fallback options and domain specialization depending on the input.
- Schema Intelligence: Auto-suggested graph models, or user-defined control when needed, enabling teams to shift from experimentation to execution seamlessly.
- Natural Language Interface: Ask “How is Product X related to Initiative Y?” and get precise graph-driven answers without Cypher queries. This opened up usage to cross-functional teams, including support and product.
- Built on Open Architecture: LangChain, FastAPI, React for scalable, cloud-native deployment (GCP, hybrid, on-prem). The stack enabled rapid iteration cycles and orchestration of modular AI services.
User Experience Wins
Making the platform technically capable wasn’t enough. It had to be usable by everyone—from analysts to engineers. The natural language interface lowered onboarding barriers significantly. Instead of weeks of training, new users were productive in hours. By eliminating the Cypher query prerequisite, the platform expanded internal usage 5x.
This usability breakthrough had a ripple effect across business units. Teams that were previously reliant on data engineers could now self-serve, accelerating insight cycles and enabling truly decentralized intelligence.
Impact in Action
That ease of use wasn’t just appreciated—it drove enterprise-wide adoption. The platform’s rollout proved the model. Persistent’s co-developed platform became more than a prototype—it was productized and embedded into the company’s core offering.
- 50%+ reduction in manual effort: Automating knowledge extraction dramatically cut down SME workload and onboarding timelines.
- Faster insight cycles: Natural language interactions replaced technical queries, letting business teams access answers on their own.
- Platform adoption surged: A visual, intuitive UI unlocked new users across product, support, and analyst functions.
- Innovation momentum accelerated: The Text-to-Knowledge Graph capability went from PoC to production within the product roadmap. Their flagship summit featured it prominently, with Persistent engineers co-presenting alongside the Chief Product Officer.
Strategic Value Beyond the Feature Set
Behind the technical lift was a strategic shift. This wasn’t just a feature update—it was a product philosophy overhaul. It was a rethink of what graph databases could become in a GenAI world.
The team went from delivering infrastructure to enabling intelligence. Customers were now empowered to:
- Build domain-specific knowledge graphs without writing code
- Use natural language to explore strategic relationships
- Integrate explainable AI directly into product flows
- Shorten the feedback loop between data ingestion and decision-making
According to McKinsey, companies that embed AI into core business processes see a 40% improvement in decision-making efficiency and a 25% increase in operational scalability.
These figures point to a broader enterprise reality: knowledge isn’t just being queried—it’s being orchestrated.
Why Persistent
None of this happened by accident. Persistent brought:
- Deep expertise in GenAI, LangChain, multi-agent orchestration, and graph modeling
- A collaborative delivery model aligned with the company’s product strategy
- A track record of co-creating platform-level differentiators with fast production rollouts
Ready to bring structure to your enterprise knowledge chaos?
Let Persistent turn your unstructured data into scalable, intelligent advantage.
