Difference in context
We place your work in a broader scientific, analytical, and technical context by bringing together the relevant literature, established practice, competing approaches, and the evidence that has developed around the field. This wider view helps distinguish the genuinely novel aspects while also making it easier to judge the competitiveness of the position.
without structured analysis
Novelty is argued from a small set of examples
The claim depends on a limited literature review, expert familiarity, or a handful of known comparators.
after analysis
Novelty is positioned within the wider source landscape
The analysis shows how the concept relates to relevant publications, datasets, methods, databases, and technical approaches in a much broader frame of reference.
without structured analysis
Important evidence dependencies, repeated assumptions, and influential sources can remain hidden behind keyword-based searches.
after analysis
Evidence and citation pathways become clearly visible
By examining citation networks and source dependencies, we can trace where important evidence originates, concentrates, or weakens.
without structured analysis
Differentiation remains largely qualitative
“This is different because…” may be persuasive, but it may not be sufficiently convincing for technical review, proposal development, or investment evaluation.
after analysis
Differentiation logic is qualified and compared
The output separates stronger signals of originality from weaker assumptions, crowded areas, and claims to be validated.
without structured analysis
Missed repurposing opportunities
A dataset, biomarker, method, or database may contain useful information beyond the purpose for which it was originally developed.
after analysis
Adjacent uses and knowledge-source connections are explored
The same source-mapping approach can reveal plausible new applications, database-repurposing opportunities, and potential cross-domain directions.
More than a literature review
We don’t just collect publications around a topic. We identify the sources that truly shape the field, trace how evidence and citations connect, and examine meaningful differentiation. Our analysis can support R&D, validation, adoption, partnership, and investment decisions, while remaining clearly separate from legal or IP advice.
Our citation network analysis framework
Our proprietary method extends conventional citation mapping with temporal and spatial network analysis and statistical characterization of the source structure. It can reveal influential source lineages, trace how knowledge develops through the network, and provide quantitative evidence for assessing information-source quality and components of originality and novelty. The method can be used as a standalone analysis or integrated into a broader information source & novelty analysis workflow. Application field examples: hypothesis building for drug target discovery, tech novelty assessment, quantitative project decision-support, etc.
The path from source evidence to a robust novelty position
The workflow starts with the central decision question, instead of a broad, uncontrolled search. This keeps the analysis focused during the whole process.
Define the novelty question
During this step we clarify whether the decision concerns concept prioritization, technical differentiation, source selection, repurposing potential, feasibility, or validation direction.
Map the relevant information landscape
Here we identify the scientific, database, dataset, technical, methodological, and cross-domain information sources that influence the interpretation.
Trace evidence dependencies
We review citation pathways, claim origins, recurring assumptions, and source dependencies that shape the current knowledge landscape.
Interpret signals of originality and differentiation
In this phase we look at source overlaps, information gaps, technical distinctions, underused combinations, and potential repurposing routes.
Connect the findings back to the decision
During the final phase, we separate the strongest evidence identified during the analysis and use it to inform the next stage of R&D, validation, feasibility assessment, or positioning.
Different decisions, different stakes, the same need for perspective
The decision itself you need to make may be different from one project to another, but the underlying question is often similar: how well does the available evidence support the scientific or technical position under consideration, and what does that position look like when we compare it with the wider field?
technical differentiation review
Deep-tech and technology owners
“We need to understand where our method, dataset, or platform sits in relation to existing scientific and technical work.”
This is often relevant when a company is preparing its technical positioning, developing a proposal, entering partnership discussions, or building the evidence behind an investor-facing case.
independent evaluation
Investors and innovation evaluators
“The concept appears promising, but we would like an independent technical view before we rely on the team’s own interpretation.”
Investors, accelerators, corporate innovation teams, and technical reviewers often need to look beyond the presentation itself. We help them by examining the underlying sources, technical differentiation, evidence quality, and maturity of the concept before larger commitments are made.
knowledge-source repurposing
Research teams and consortia
“We already have data, methods, or prior outputs that may support a new direction, but we want to know more about the strength of that connection. ”
For academic teams, applied R&D groups, and consortia, it is highly important to work out whether that connection is merely suggestive or whether it has sufficient scientific and technical substance to carry a new line of research, a different use of existing resources, or a more ambitious development direction.
R&D strategy
Product and development teams
“Before we commit to another development cycle, we need to know whether there is really enough here to pursue. Is this direction meaningfully different from what is already out there, or are we seeing more novelty than our actual data and research tell us?
That is where a broader source analysis earns its place. It can show whether the apparent distinction survives contact with the surrounding literature and technical work, and just as importantly, which parts of the argument hold up well once the evidence is examined more closely.
Three analytical outcomes
The first two outputs start with a concept already under consideration and test the strength of its position. The third takes the evidence as its starting point, looking for a direction or connection that may not yet have been recognised as an opportunity.
Scientific source map and evidence pathway report
We create a structured overview of relevant publications, databases, datasets, methods, and technical domains, with interpretation of source dependencies, evidence routes, and citation-network relationships that are hard to recognize when sources are considered one by one.
Outcome: a clearer understanding of how the relevant evidence is structured around the novelty question
Technical novelty and differentiation assessment
You will receive a decision-oriented analysis of technical distinctiveness, overlap with existing work, unsupported assumptions, and points requiring further validation.
Outcome: a technically grounded novelty position, including its limitations and uncertainty
Database and knowledge-source repurposing strategy
We identify plausible repurposing options for existing datasets, knowledge bases, biomarker candidates, analytical methods, computational workflows, or technical outputs that may support new scientific or development directions.
Outcome: prioritized reuse opportunities supported by evidence
Independent technical judgment, grounded in scientific and applied work
Our work draws on more than 25 years of experience across scientific research, advanced data analysis, method development, validation, and applied R&D, which also shapes how we approach biomarker development, feasibility, study design, and technical decision support.
When your question extends beyond source analysis
Information source and novelty analysis is often one part of a broader technical question. Depending on what the source evidence shows, the next step may involve computational testing, validation, feasibility work, or biomarker-focused analysis
Data analytics & algorithm development
Useful when findings from the source analysis need to be tested computationally, explored in existing datasets, or taken further through core algorithm development.
Study design & computational statistics
Relevant when the analysis exposes evidence gaps that require a better study framework, stronger statistical planning, or a more rigorous validation strategy.
Feasibility studies
Relevant when the findings need to be evaluated within a broader technical or strategic context, particularly before committing to development, adoption, or a higher level of readiness.
Biomarker discovery & validation
Applicable when the underlying question involves biological signals, biomarkers, bioindicators, or prognostic parameters and their scientific or technical validity.
FAQ
Common questions about novelty and source analysis
A literature review summarizes what has been published. Our analysis also evaluates source structure, evidence pathways, citation dependencies, technical overlap, and signals of novelty, then considers what these mean for the question we are working with. This makes the process more analytical and more directly useful for R&D, validation, feasibility, and positioning decisions.
No. We do not provide legal opinions or formal IP analysis. Our role is to examine the scientific and technical context around the work, so that novelty and differentiation can be considered more carefully when making R&D, validation, feasibility, adoption, or positioning decisions.
Depending on the question, the analysis may draw on scientific publications, citation networks, technical documents, public databases, structured datasets, measurement approaches, analytical workflows, and domain-specific knowledge sources.
Yes. Existing databases, measurement outputs, computational methods, literature structures, or knowledge bases sometimes contain useful connections that were not part of their original purpose. We can examine whether those connections have enough scientific and technical substance to support a new hypothesis, application, or development direction.
Yes. The analysis can show which parts of the proposed work are already well represented in the field, identify the areas where the distinction is more substantial, and assess the strength of the relevant sources that support the position. It can also expose parts of the argument that would benefit from additional evidence or validation before they are presented more broadly.
The exact output depends on the project, but it may include a scientific source map, an analysis of novelty and overlap, evidence and citation pathways, identified gaps or repurposing opportunities, and a decision-focused memo or technical report tailored to the stage of the work.
Bring your question into context
If the originality, positioning, or evidential strength of a concept is still open to question, bring us the background you have so far, and let’s have a conversation! This first discussion helps us understand your project well enough to see where a deeper source and novelty analysis could sharpen the positioning, strengthen the evidence, and make the overall case even more convincing.