SciBite

In the age of big data and scientific explosion, one of the key challenges is converting mountains of unstructured text (papers, lab notes, reports) into machine-readable, actionable datasets. That’s exactly where SciBite steps in. SciBite develops award-winning semantic analytics software designed for scientific and life-science organisations, enabling them to harmonise, tag, search and analyse their data in a scalable and domain-specific way. 

What sets SciBite apart is its ontology-led, API-first, and Life-Sciences-aware stack. Rather than being a generic text-analytics vendor, it brings domain expertise, curated vocabularies, enterprise scalability, and integration with Elsevier’s large scientific content ecosystem. If your organisation is in pharma, biotech or research, and you need to extract value from legacy data, lab notebooks, literature or regulatory reports—SciBite is designed for that.

Core Solutions / Tools – What They Do

Here is a breakdown of the main tools and how each functions, along with how they fit together.

1. CENtree – Ontology and Terminology Management

CENtree is SciBite’s platform for creating, curating, managing and serving ontologies/terminologies at scale.
Key features:

• Collaborative editing, versioning, governance (roles for editors/suggesters). 
• Supports standards such as OWL, OBO, SKOS, has SPARQL querying and API interface. 
• Scalable to millions of concepts/synonyms, import/export bulk templates, “application ontologies” derived from base vocabularies.
  Use-case: Suppose a pharma company wants to unify terminology across its ELN, assay database and literature: CENtree lets them standardise all the terms (gene names, drug synonyms, assay methods) and govern changes over time, making downstream data consistent.

2. TERMite – Named Entity Recognition & Text Extraction Engine

TERMite is the high-throughput NER engine in SciBite’s ecosystem. It takes raw text (scientific articles, lab notes, regulatory filings) and tags entities (e.g., genes, diseases, compounds) using curated vocabularies (SciBite’s VOCabs) and mappings to ontologies.
Key features:

• Reports claim processing rates up to 1 million words per second for domain-specific text corpora. 
• Integrates with the vocabularies: over 20 million synonyms covering 50+ life-science topics.
  Use-case: A regulatory safety team wants to mine adverse-event narratives from multiple databases in varied formats. TERMite can ingest those documents, tag the relevant mentions, align them to standard ontologies, and produce machine-readable output for analytics.

3. Semantic Search & SciBite Chat

Once data is enriched via ontology/NER processing, the next challenge is retrieval: how do you ask the right question and find what you need across structured + unstructured datasets? SciBite’s Semantic Search and SciBite Chat (an AI-powered conversational interface) address this.
Key features:

• Semantic Search: lets users search across PDFs, PowerPoints, lab notebooks, databases, search terms are linked via ontology relationships so synonyms, abbreviations, and variant expressions don’t block retrieval. 
• SciBite Chat: a conversational UI that uses Retrieval-Augmented Generation (RAG) architecture grounded in ontologies, ensuring transparency and traceability of AI answers (documents cited, query trace shown).
  Use-case: A research team wants to ask “What drugs target gene X in disease Y and have had adverse event Z?” Instead of multiple keyword searches, they can interact with SciBite Chat, which understands ontological context and provides evidence-backed results.

4. Datasets, FAIRification & Integration Services

Beyond tools, SciBite offers curated domain-specific datasets and support for FAIR data (Findable, Accessible, Interoperable, Reusable) practices.
Key features:

• Legacy data ingestion, harmonisation of unstructured lab notebooks/ELNs, automated enrichment.
• Integration with data-management platforms, ELNs, knowledge-graph providers and pharma informatics stacks.
  Use-case: A biotech wants to wrap its internal ELN, literature database and assay repository into a unified knowledge-graph. SciBite helps harmonise the source terminologies, tag all documents, and deliver a machine-readable foundation for AI/ML workflows.

How the Solutions Fit Together

1. Start with your corpus: text documents, lab notes, databases.
2. Manage vocabularies/ontologies with CENtree to define the domain terms and relationships you care about.
3. Run TERMite to tag and extract entities from raw text, aligning them to the ontology concepts.
4. Store/enrich the resulting structured metadata and link across datasets (for example build knowledge graphs).
5. Enable retrieval via Semantic Search and SciBite Chat so users can ask questions and get evidence-backed answers.
6. Integrate everything into your scientific informatics stack, FAIR data pipelines, data-management platforms, or AI/ML workflows.

Why Organisations Choose SciBite

• Domain specificity: Unlike generic text-analytics vendors, SciBite is built by life-science ontologists and IR scientists. 
• Scalability & performance: Engines like TERMite claim very high throughput in biomedical domains.
• Governance & interoperability: With CENtree you get enterprise-grade terminology management with standards support.
• Integration readiness: API-first architecture, cloud/SaaS options, compatibility with major data-platforms. 
• Transparency in AI: SciBite emphasises explainable and traceable AI search results (via SciBite Chat) rather than opaque LLM “black-box” results.