Start With The Question
Use patterns capture actors, decisions, evidence needs, answer shapes, and source-system categories. From there, scientist and business language becomes controlled, testable competency questions.
Synergic Data for life sciences & R&D
Grounding AI in reusable enterprise knowledge.
turns fragmented data, systems, and expertise into governed knowledge assets - reusable across teams, workflows, and AI applications. AI operationalization, powered by lean semantics.
Knowledge operating layer
From governed data to trusted AI
01FAIR by designFAIR
02Provenance & governanceTrust
03Modular SaaSScale
04RDF + LPGGraph
Question-driven platform
From intent to governed semantic data products
turns rough user intent into normalized competency questions, governed query contracts, reusable schema seeds, validation gates, and answer-ready semantic data products. The result is a practical path from the questions your teams need to ask to the graph structures, provenance, and readiness checks that make answers trustworthy.
Make Answers Explicit
Each question traces to a query contract: the admissible data scope, parameters, expected answer set, provenance obligations, uncertainty behavior, acceptance tests, and executable query skeleton.
Package Reusable Semantics
Query contracts drive schema seeds, LinkML, OWL, SHACL, mapping-ready vocabulary alignment, data-fitness checks, artifact graphs, and generated views that can move into graph databases or other semantic products.
Strategic services
Get running quickly without separating experts from delivery
services minimize friction between subject matter experts and information technology teams. Each service informs and leverages the platform - from modeling and FAIR alignment to deployment of a functional knowledge graph your team can own.
Bring Your Own Datathons (BYOD)
Starting with key use cases, competency questions, and real or synthetic data, we model and transform data using the platform, including touch points with your current technology stack.
Born FAIR
The platform is built around FAIR implementation patterns that grow with your FAIR maturity journey and establish right-sized data governance and stewardship patterns.
Leveraging the World
We help determine the informational and operational metadata standards that fit your use cases and organization, easing maintenance and growth while enabling emergent use cases.
Knowledge-first approach
Closing the gap between business goals and IT delivery
was founded to reconnect strategy with execution so technology serves the goals it was meant to achieve. We synthesize FAIR data, data-centric principles, knowledge graphs, and generative or agentic AI into pragmatic implementation patterns.
Tight SME-delivery loop
Subject matter experts and delivery teams stay tightly coupled from design through operations so the why behind each decision is explicit and preserved.
Reusable building blocks
Challenges are broken into functional units - schemas, ontologies, services, and modules - that can be recomposed as needs evolve.
Invest in knowledge, not bespoke software
Strong FAIR practices for operational and information metadata create organization-wide reusable knowledge assets and reduce one-off custom code.
Graph-grounded GenAI and agents
Flexible graph structures give LLMs and agents the context required per use pattern, making agentic AI more reliable, explainable, and economically viable.
RDF + LPG choices
reflects RDF triples at its core while also supporting labelled property graphs, allowing the right paradigm for performance, security, and operational needs.
Remote-first, global presence
We work with R&D, IT, and data-science leaders from Boston, London, Nice, Dubai, and beyond as teams move from proof-of-value to operational knowledge graphs.
Meet the Team
The people building
Tom Plasterer, PhD
CEO & Co-Founder
Bioinformatician / systems biologist with 20+ years across pharma, biotech, and consulting. Decades of applied semantics, FAIR data, and life-sciences knowledge graphs.
Ivan Stankov, PhD
CTO & Co-Founder
Technology leader building large-scale AI, data, and semantic systems. Deep expertise in computational semantics, GraphQL architectures, and RDF/LPG interoperability.
Eric Little, PhD
CDO
Theoretician and semantics SME with 20+ years applying knowledge engineering across industries, especially life sciences. Eric joins from Accenture, where he helped found its Knowledge Engineering for AI practice.
Dana Wojtasinski
Strategic Partnerships & Customer Growth
Commercial life-sciences leader building strategic partnerships, customer relationships, and growth programs for science-led, data-intensive organizations.
Kathy Reinold
Ontology Strategy & Delivery Lead
Kathy brings deep life-sciences expertise in ontology engineering, knowledge graphs, and data integration. She shapes practical knowledge architectures that turn complex ideas into trusted, business-ready solutions.
Tiffany Montgomery
Marketing, Sales & Business Analysis Consultant
Consultant guiding early marketing, sales enablement, business analysis, and customer-development work as the company expands its market presence.
Blog
Ideas and field notes
Contact
Tell us about your data and goals
Start with a focused, knowledge-intensive workflow. We will map what knowledge exists, what is implicit, and what is being relearned - then design an explicit knowledge layer without disrupting your existing tools. Prefer email? Reach us at info@knowledge3.ai. We are remote-first, based out of Boston MA, USA, London, UK, Nice, France, and Dubai, UAE.