Product manuals, release notes, troubleshooting guides, API references, and years of resolved tickets hold the answer to almost every support question. LangOptima ingests the technical documentation you already have and connects it into a single queryable knowledge base, so support teams and customers get answers in their own words, in seconds, each one citing the source document.
Built on schema.org's open FAQ and Q&A vocabulary, expressed as RDF, so support answers connect as shared knowledge instead of being rewritten each time. LinkedIn built a knowledge graph over its own historical support tickets and published the result at SIGIR 2024, a peer-reviewed information-retrieval conference. Median time to resolve an issue fell 28.6% against a vector search baseline.
A customer asks a question in their words. The answer exists in yours: spread across a manual, two release notes, and a configuration guide. Someone has to translate between the two, and that someone usually took months to train.
The full answer to a real customer question rarely lives in one place. It spans the product manual, a release note that changed the behavior, and a troubleshooting guide, and keyword search returns each fragment separately, never the connected answer.
Handling complex questions means knowing how the documentation fits together: which versions changed what, which configurations interact, which known issue explains the symptom. That map lives in senior agents' heads, and every new hire has to rebuild it from scratch.
Customers describe what they see: an error, a behavior, a blocked task. Documentation is organized by feature and version. Bridging that vocabulary gap is exactly the work that makes support slow, and self-service portals rarely manage it at all.
LangOptima ingests the support content your organization already maintains, including product manuals, release notes, troubleshooting guides, API references, and resolved-ticket knowledge, and structures what is inside them: products, versions, features, symptoms, causes, and the relationships between them. Because the knowledge is connected, a question can be answered across documents in one traversal: from the customer's symptom to the version that introduced the change to the configuration step that resolves it, with each hop cited.
Modern AI can structure a great deal on its own. Where it stops is the meaning specific to your organization: the concepts, rules, and relationships that make your business yours, and where its real value lives. We structure that layer with you on open, world-standard semantics, not a proprietary schema, so the graph reflects how you operate and stays yours: no vendor lock-in, portable to whatever you run next. More on the structure beneath it →
You don't have to boil the ocean. 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.
Manuals, release notes, guides, and ticket knowledge unified at the semantic layer. Nothing moves. Nothing is replaced.
Traverse from a symptom to its known causes, from a feature to every version that changed it, from an error to the fix and the doc that describes it. Multi-step connections keyword search cannot follow.
Complex questions that once needed a senior agent and three documents resolve in one query, phrased in the customer's own words, with the sources attached.
Every agent works with the full documentation map behind them, so new hires handle questions that used to require months of training, and customers self-serve answers that used to require a ticket.
Representative scenarios of how support teams apply a knowledge graph over their technical documentation. Illustrative of the pattern, not published client references.
A support team connects its manuals, release notes, and troubleshooting guides in a knowledge graph. A customer describes a symptom in plain language, and the answer assembles across documents: the behavior change, the affected versions, and the resolution steps, each part citing its source, without a ticket being opened.
Instead of memorizing how the documentation fits together, new agents query it. The graph carries the connections between products, versions, symptoms, and fixes that senior agents hold in their heads, so complex tickets stop waiting for the one person who knows where everything is written down.
However complex the documentation landscape underneath, the engagement itself stays simple.
We ingest the documents and data you already hold, straight from the systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering. If it proves value, you expand from there. If it doesn’t, it doesn’t scale.
You query the connected layer in plain language, and answers come in seconds, accurate and traceable, with citations back to the source so you can check them yourself.
Curious what disconnected data may be costing your organization? The free Data Silo Cost Calculator puts a number on it in about two minutes, with no signup to see the result.
Try the Data Silo Cost Calculator →For a single article with a single answer, plain search or an LLM pointed at your documentation does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: how a fault in one component relates to a fix documented somewhere else, across product versions, release notes, and known-issue records that were never written to be read together. Those answers live in the relationships across your documentation, knowledge base, and ticket history, not inside any one of them. A knowledge graph connects them into one model, follows the chain across them, and returns each answer with a citation back to the source it came from. Something agents and customers can act on, and trust.
A 30-minute conversation about your support content landscape: which systems hold your manuals, guides, and ticket knowledge, and which customer questions a queryable view of them would answer first. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.