Prepare Your Data for AI

Most AI projects fail at the data layer

Your organization 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 reach it.

Without structure, AI tools hallucinate, search returns noise, translation loses context, and 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 it.

It usually isn’t a search problem either. Search finds documents. What it can’t tell you is that two records describe the same customer, which certificate belongs to which batch, or which source produced a given claim. Those are questions about relationships, and every AI tool you put on top inherits whatever the layer underneath can’t answer.

From documents to decisions

A knowledge graph extracts the entities, relationships, and meaning from your unstructured data and organizes them into a structured, queryable form, one that machines and people can both work with.

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

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

Most AI can structure a great deal of your data on its own. Where it usually stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value actually lives. That is the layer Galaxia computes from your own material rather than from a generic template.

Galaxia: the engine that builds the graph

We build on Galaxia, made by our partner Smabbler. It reads your data, structured or unstructured, ordinary prose included, and autonomously builds a semantic hypergraph of what is there, how it all connects, and where it came from. It is not only a map: the structure carries logic, and can be computed over.

Galaxia does not use a language model to build that structure. It computes rather than predicts, so the same information produces the same structure every time, and every connection traces back to its source. You can put a language model on top afterwards for natural-language interaction. The model no longer has to be the memory, the knowledge base, or the source of truth.

• Building the graph costs no language-model tokens. Anyone who has priced a language-model graph build over a real corpus knows what that is worth.
• Quick enough to stay current. Minutes for a small corpus, hours for hundreds of millions of characters: the difference between a memory layer that reflects the business and one that reflects last quarter. It is versioned and available through the API as soon as it is built, and because the knowledge lives in a persistent structure rather than a context window, growing the corpus never means deciding what to leave out.
• Runs on ordinary CPU, on infrastructure you control. No GPU dependency.
• Enterprise language works from day one. No model retraining.
• Knowledge can be federated. Separate graphs keep their own boundaries while working together as one coherent environment for people, applications, and AI agents.

It ships retrievers for LangChain and LlamaIndex, so it sits behind the tools your engineers already use.

The evidence behind it

• European-built. Co-financed by the European Union under the European Innovation Council and Horizon Europe, whose evaluators called it “game-changing system-level innovation.”
• Seven years of development, the first three inside a tier-one company as their vendor.
• Published retrieval benchmark. On PubMedQA, across its 500-question expert-labelled test set, Galaxia returns the correct source article first 93.2% of the time and inside the top five 97.2%. The published run measures Galaxia on its own, with no competing system in the comparison. Reproducible from the repository.
• Alumni of Endless Frontier Labs.

“What the heck just happened?”
A top-tier customer, on watching Galaxia turn their data into usable knowledge within hours, rather than after months of knowledge engineering

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 organization, delivering the same four benefits every time: connected data, hidden insights surfaced, faster decisions, and teams amplified.

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.

The payoff is sharpest in regulated work. In a peer-reviewed evaluation of compliance in translation (Gene & Sosoni, NeTTIT 2026), a knowledge-graph-driven system caught 100% of source-compliance violations on a controlled 30-error corpus (English into Greek and French), where two GPT-5 baselines caught none. Strong evidence on that corpus, and the direction this approach is built for.

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 signaling

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.

Agents that agree with each other

Once more than one AI agent acts on the same domain, they need to mean the same thing by the same word. Without a shared definition, two agents reading identical business logic can reach different conclusions and act on both. A better model doesn’t fix that, because it isn’t a model problem. The graph is the shared layer they reason from.

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 memorize.

One context today. A federated foundation tomorrow.

Each use case above is a knowledge graph for one industry or business context. The strategic opportunity is what comes next. Separate graphs can hold their own boundaries and still work together, so the one you build for a single domain is not stranded from the next.

Start with a single context. Add a second, then a third. Instead of ten disconnected projects, they compound into one knowledge environment spanning the organization, with the provenance intact at every step.

AI needs memory, context, and evidence. That is the layer being built here.

Where this already runs

Knowledge graphs are not an emerging idea in large organizations. Several have run them in production for more than a decade. Three that are publicly documented:

• BBC. Ontology-driven publishing since the 2010 World Cup, with more than 800 pages assembled automatically from one connected content graph while journalists edited by exception. Its ontologies are published today by the press industry’s standards body, the IPTC.
• Thomson Reuters, now the London Stock Exchange Group. A customer-facing knowledge graph of over two billion relationships, built on RDF with open PermID identifiers that remain a live public service.
• Telstra. A production knowledge graph sitting above the network and billing systems it already ran, supporting autonomous network operations, recognized with a TM Forum Excellence Award.

All three built it in-house, at a time when there was no other way to get one. There is now.

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. Together they make your existing infrastructure significantly more useful for AI workloads.

It also targets where the money already goes. A large share of enterprise IT budgets is spent connecting systems that were never designed to talk to each other. A shared semantic layer is how you stop paying that integration tax again with every new project.

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.

Where governance is a requirement, not an afterthought

LangOptima works with organizations where getting AI wrong has real consequences: regulatory penalties, patient safety, financial exposure, national security. Each industry below links to how the knowledge graph applies there.

• Financial services. KYC, AML, and fraud detection across entities and relationships that span jurisdictions and counterparties.
• Life sciences. Data harmonization, 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 & defense. Intelligence fusion, acquisition management, and cross-domain knowledge integration under strict data-governance frameworks.
• Legal. Contract, matter, and obligation intelligence: legal documentation ingested into one queryable graph, with citations back to the source.
• Research & academia. Publications, datasets, grants, and expertise connected, so related findings and collaborators surface with the citation trail intact.
• Human resources. Policies, handbooks, and institutional knowledge in one queryable knowledge base, so onboarding and routine policy questions answer themselves with citations.
• Customer support. Technical documentation connected into one queryable knowledge base, so customers and agents get cited answers to complex, multi-document questions in seconds.
• Content & media. Archives, asset systems, and rights records connected, so teams find what they already own in seconds, each result cited to its source.
• Cybersecurity. Threat feeds, incidents, assets, and identities connected, so an alert traces to its full blast radius in one query, each hop cited.
• Telecommunications. Network inventory, services, and circuits connected, so impact and change questions resolve across the whole topology in seconds.
• Retail & e-commerce. Catalog, suppliers, inventory, and customer data connected and de-duplicated, so merchandising questions become a single query.
• Healthcare. Records, claims, provider networks, and guidelines connected within your compliance boundary, so cross-system questions answer with the source attached.

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

Your content also stays yours. The graph is built on your data in an environment agreed before we start (on-premise or air-gapped where that is a requirement), and nothing in it is used to train anyone else’s model.

You don’t have to solve everything at once. Start with a single business context and prove it there. Once that foundation is laid properly, the same connected data tends to open opportunities in other departments, so the next team builds on the work already done rather than starting from zero.

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 first structure over your real corpus comes back in minutes to hours, depending on its size, and you can put questions to it straight away. A scoped pilot of 8 to 12 weeks then connects it to one working application and hardens it for production. You see structured knowledge from your own data, queryable via API, before any long-term decision.

In practice that means one business decision at department scope: one desk’s archive, one country program, one question such as “which customers does this fault affect?” You supply a starting corpus and access to the data. We build the graph, open it to plain-language questions, and iterate until the answers hold up against what your people know to be true. Then it goes to production behind an API.

Each context gets its own graph, and because graphs federate, the second and third join the first rather than starting over.

Your data already contains the answers. Let’s make it usable.

Want to see it on your own data?

Book a scoped proof-of-concept call →

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