Your Data Has Knowledge Trapped Inside It

THE PROBLEM

Most AI Projects Fail at the Data Layer

Your organisation has spent years accumulating documents, reports, policies, product records, and regulatory filings. That corpus holds real knowledge — but it's locked inside PDFs, spreadsheets, and legacy systems where no AI model can reliably access it.

Without structure, AI tools hallucinate. Search returns noise. Translation loses context. Decision-makers get answers they can't trace back to a source.

The problem isn't your AI. It's that your data isn't ready for AI.

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WHAT A KNOWLEDGE GRAPH DOES

From Documents to Decisions

A knowledge graph extracts the entities, relationships, and meaning from your unstructured data and organises them into a structured, queryable format — one that machines and humans can both work with.

Think of it as the layer between your raw data and your AI applications. Instead of feeding language models unstructured text and hoping for the best, you give them verified, contextualised knowledge with clear provenance.

The result: AI outputs that are explainable, auditable, and grounded in your actual data; not in statistical probability.

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TEXTDISTIL; THE EXTRACTION ENGINE

TextDistil: Knowledge Extraction at Scale

TextDistil is LangOptima's knowledge extraction pipeline. It reads your unstructured data — documents, web content, internal records — and harvests the entities, relationships, and domain-specific meaning buried inside.

How it works:

Ingest: Connect your data sources. TextDistil handles documents, databases, and live feeds across formats and languages.

Extract: Large Language Models combined with semantic technology identify entities, classify relationships, and resolve ambiguity — with human oversight at every decision point.

Structure: The extracted knowledge is organised into a semantic knowledge graph: typed entities, named relationships, and traceable provenance for every fact.

Serve: Your structured knowledge is available via API; ready to power search, translation, analytics, signalling, or any AI workflow you're building. The graph stays current through continuous processing, so your applications always reflect your latest data.

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WHAT YOU CAN BUILD ON TOP

One Graph, Many Applications

A knowledge graph isn't a product you use. It's a foundation you build on. Once your data is structured and semantically connected, it powers applications across your organisation:

Context-Aware Translation: Machine translation that understands your terminology, your domain, and the relationships between concepts; not just the words on the page. Consistent across every language and market.

Intelligent Search: Search that understands what you mean, not just what you typed. Query your entire corpus by concept, entity, or relationship; and get answers with full traceability to the source document.

Business Signalling: Detect patterns, anomalies, and emerging risks across your data in real time. Knowledge graphs connect information that sits in silos; surfacing signals that would take human analysts weeks to find.

GraphRAG: Retrieval-Augmented Generation grounded in your knowledge graph rather than raw text chunks. Your AI assistant draws on verified, structured knowledge; reducing hallucination and producing answers you can trace and audit.

Regulatory Intelligence: Map obligations, track changes, and identify gaps across regulatory frameworks. A knowledge graph makes compliance queryable rather than something your team has to memorise.

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WORKS WITH YOUR EXISTING STACK

Additive, Not Competitive

LangOptima doesn't replace your data infrastructure. If you've invested in Snowflake, Databricks, Azure, or AWS; good. A knowledge graph sits on top of those investments as the semantic layer that connects them.

Your data warehouse stores facts. Your knowledge graph stores meaning. They're complementary; and together, they make your existing infrastructure significantly more useful for AI workloads.

We integrate on premise or via API with whatever you're already running. No rip-and-replace. No migration projects. Your knowledge graph connects to your stack and makes it smarter.

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BUILT FOR REGULATED INDUSTRIES

Where Governance Is a Requirement, Not an Afterthought

LangOptima works with organisations where getting AI wrong has real consequences; regulatory penalties, patient safety, financial exposure, national security.

Financial Services: KYC, AML, and fraud detection across entities and relationships that span jurisdictions and counterparties.

Life Sciences: Data harmonisation, interaction mapping, and regulatory submission intelligence across global markets.

Manufacturing & Supply Chain: Supplier risk visibility, parts interchangeability, and compliance traceability across complex, multi-tier supply networks.

Insurance: Claims intelligence, fraud pattern detection, and policy-to-regulation mapping across product lines.

Energy & Utilities: Asset lifecycle intelligence, safety compliance, and operational knowledge capture for workforces in transition.

Government & Defence: Intelligence fusion, acquisition management, and cross-domain knowledge integration under strict data governance frameworks.

Every knowledge graph we build carries full provenance; every fact is traceable to its source document, extraction date, and confidence level. Auditability isn't a feature we add later. It's how the system works.

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HOW WE WORK

Start With a Proof of Concept

We don't ask you to commit to a platform before you've seen results. Every engagement starts with a scoped Proof of Concept using your real data, your real use case, and your real constraints.

A typical PoC runs about 12 weeks. You'll see structured knowledge extracted from your corpus, queryable via API, and connected to at least one working application — before you make any long-term decisions.

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TESTIMONIALS

“Wondering how to use NLP most effectively to help automate knowledge graph creation? [TextDistil] can be useful in preliminary Knowledge Graph generation.”

> Semantic Tech Experts Group

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“The integration of Lead Semantics’ platform and AllegroGraph delivers new types of analytic outcomes and insights to provide ‘Smart Data’ for the Enterprise”

> Franz, Inc.

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"Thank you for creating TextDistil, an excellent tool for enhancing RAG-based large language models' contextualization and reasoning capabilities (LLMs). Generative AI can more effectively respond to inquiries or pose relevant follow-up questions by leveraging the synergy of data and graphs. I experimented with TextDistil in 2022 during my work on ISEEQ while delving into open-domain question-answering. I now observe that TextDistil offers a diverse range of functionalities, making it well-suited for enhancing LLMs' consistency and explainability features."

> Dept. of Computer Science, Univ. of Maryland Baltimore

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Your data already contains the answers. Let's make it useable.

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