Years of experiments, study results, sample data, and publications sit across lab systems, notebooks, and literature databases, each holding a fragment of what your organization already knows. LangOptima connects them into a single knowledge graph, so the next hypothesis starts from everything you have learned, not from another six-week literature review.
The approach behind UniProt, the public protein knowledge graph that connects millions of proteins to their functions, structures, and genes, and links to dozens of research databases through open standards.
In many research teams, the answers already exist somewhere in their own data. The systems just won’t let anyone connect the dots, and every new study pays the price.
Experiments, assay results, and sample data live in instrument systems, lab notebooks, and spreadsheets, often one per team, site, or partner lab. Cross-study questions require manual reconciliation.
Published findings and internal results never meet. Scientists repeat literature searches independently, and sometimes re-run studies whose answers already exist in another system.
The connection between a biomarker signal in one study and an outcome in another sits in the gap between systems that no one owns. It surfaces years later, or never.
LangOptima connects your research, lab, and study data at the semantic layer, modeling studies, samples, biomarkers, publications, researchers, and results as connected entities. Nothing moves. Nothing is validated twice. Your existing systems keep running; the knowledge graph lets your teams see across them.
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.
Lab systems, study databases, sample registries, and literature feeds unified without replacing validated systems. Data lineage and audit trails inherited automatically.
Surface links between biomarker signals, sample characteristics, and study outcomes: relationships that are invisible inside any single system.
Literature reviews collapse from weeks to days. The path from hypothesis to supporting evidence moves at the speed of the question, not the speed of the spreadsheet.
Scientists stop stitching data together and start testing ideas. Every researcher works from the organization’s full memory. Your domain experts become force multipliers.
Representative scenarios for life sciences organizations with your profile. Illustrative of the pattern, not published client references.
A research team connects studies, biomarkers, publications, and internal results in one knowledge graph. A literature review that took weeks compresses into days, and connections no one knew to look for emerge between findings, each traceable to its source.
The same approach scales to the patient level: health, clinical, genetic, and payer data connected per patient in an open-standards knowledge layer, so clinicians and researchers can spot precision-medicine pathways patient by patient.
However complex the data landscape underneath, the engagement itself stays simple.
We ingest the documents and data you already hold, straight from the lab, study, and literature systems you already run. Nothing is replaced. Your teams keep working where they work today.
A scoped 8–12 week paid pilot structures it into a knowledge graph around one decision that matters — say, everything your organization has learned about one target or compound. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions — answers in seconds, accurate and traceable, each with a citation back to the source so you can check it yourself. If the pilot proves value, you expand from there.
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 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: which earlier studies used the same biomarker, assay, or model, across programs that never shared a database, or whether the hypothesis in front of you has already been tested somewhere in your own research. Those answers live in the relationships across your LIMS, ELN, and study databases and your protocols, reports, and published papers, 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 source it came from. Something the next hypothesis can build on, with the evidence attached.
Start with a 30-minute conversation. Tell us about the study, the dataset, or the research question that is hardest to connect today, and we’ll 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.