Your risk, product, and client systems each hold part of the picture. None of them holds the whole. LangOptima connects them into a single knowledge graph, so full counterparty exposure, regulatory traceability, and hidden subsidiary relationships become questions answered in seconds, not days.
Built on FIBO, the open standard the financial industry uses to describe entities, instruments, and exposures, so your risk, product, and client systems describe the same world in the same terms.
A risk analyst asks what your full exposure is to a named counterparty, across all products, entities, geographies, and related parties. In many risk teams, the answer is still: “Give me two days.”
Counterparty data lives in separate credit, trading, custody, and client master systems. Each has its own identifiers, refresh cycles, and governance.
Analysts pull extracts, reconcile IDs in Excel, and stitch together a view that is obsolete the moment it is published. Every regulatory request repeats the work.
The hidden subsidiary, the shared directorship, the indirect exposure through a collateral chain: all of it sits in the gaps between systems. No single query will find it.
LangOptima sits above your existing risk, product, and client systems without replacing any of them. We structure the relationships they already contain into a semantic knowledge graph that understands that a parent company, its subsidiaries, its counterparties, and the products held between them are all connected, and lets you traverse that connection in a single query.
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.
Credit, trading, custody, KYC, and client master systems unified at the semantic layer. Nothing moves. Nothing is replaced. Every system keeps doing its job.
Traverse from a counterparty to its parent, to its subsidiaries, to shared directorships, to products held, to collateral positions. You surface relationships your analysts did not know to look for.
Regulatory responses, risk committee prep, and ad-hoc exposure questions answered in seconds. The two-day reconciliation becomes a single query.
Your risk analysts stop reconciling data and start asking the questions they were hired to answer. Every analyst becomes a team of analysts.
Representative scenarios for organizations with your profile. Illustrative of the pattern, not published client references.
A bank unifies counterparty, product, and exposure data in a knowledge graph, and a regulatory data request that once meant weeks of reconciliation becomes a same-day answer. The same graph then powers day-to-day risk queries and ad-hoc board requests.
Generic machine translation falls short on specialized financial content. A translation workflow grounded in the knowledge graph draws on validated terminology and domain-specific glossaries, so multilingual output stays accurate and consistent across markets.
However complex the data landscape underneath, the engagement itself stays simple.
We ingest the data you already hold, straight from the credit, trading, custody, and client master systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week paid pilot structures your first decision context, say full counterparty exposure, measured against success criteria you set. 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.
Wondering what your compliance workload adds up to? The free GDPR Compliance Calculator models it in about two minutes, with no signup to see the result.
Try the GDPR Compliance Calculator →For a single document with a single answer, plain search or an LLM pointed at your PDFs does the job well, and a knowledge graph would be overkill. The difference shows up on the questions one document can’t answer: what your true exposure to a counterparty is once you follow every subsidiary, guarantee, and shared piece of collateral, or which apparently unrelated positions move together under stress. Those answers live in the relationships across your core banking, trading, and risk systems and your filings, contracts, and regulatory correspondence, not inside any one of them. A knowledge graph connects the structured systems and the documents into one model, follows the chain across them, and returns each answer with a citation back to the record it came from. Something you can act on, and defend to a regulator.
Start with a 30-minute conversation. We’ll listen to the specific counterparty, exposure, or reporting question that is burning the most time today, then show you how it would be answered on a knowledge graph. No deck, no pitch.
The same connected-data approach, applied across sectors. Explore another industry.