Philologica
Traceable AI-assisted manuscript review for researchers
The final days before submission often create the wrong kind of review: a fast reread of everything, with no clear order of priority.
A more useful approach is to start with the parts of the manuscript that carry the most risk.
Step 1: Identify the claims carrying the conclusion
Mark the sentences that support the central result, recommendation or interpretation. These claims deserve more attention than low-impact background statements.
Ask:
What exactly is being claimed?
How certain is the wording?
Which source or analysis does the claim depend on?
Step 2: Check the evidence path
For each high-stakes claim, connect the sentence to the relevant source or analysis.
Check whether:
the source can be retrieved;
it addresses the exact claim;
the population, method and outcome match;
the wording goes beyond the evidence;
a limitation should be visible.
Step 3: Inspect the argument bridge
A manuscript can move from a supported observation to an unsupported recommendation in a single transition.
Look for words such as:
therefore;
demonstrates;
proves;
should;
causes;
reliable;
effective;
generalizable.
These words are not automatically wrong. They are signals to inspect whether the evidence supports the level of certainty being expressed.
Step 4: Review terminology across sections
Technical terms can drift as a manuscript develops. A term may be defined one way in the introduction and used more broadly in the discussion.
Create a short list of terms that carry methodological or theoretical meaning, then check whether their use remains stable.
Step 5: Turn findings into actions
Do not leave every observation at the same priority level. Classify each issue as:
fix now;
verify;
qualify or contextualize;
discuss with a supervisor or co-author;
record for human review.
The result is not a prediction of peer review or editorial acceptance. It is a clearer preparation process.
Philologica is being built around this kind of review path: from claim to source, from evidence to argument and from observation to an explicit next action.
Discover Philologica: https://app.philologica.com
What is your highest-value pre-submission check, and what do you usually discover too late?
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Many AI products begin with the same promise: generate more text, more quickly.
For academic work, that is only one part of the problem. Researchers also need to know whether the text is supported, whether an inference is justified and what should still be checked by a human before submission.
That led us to a different product direction with Philologica: a review layer rather than another writing layer.
The problem we are focusing on
A manuscript can be:
grammatically polished but logically incomplete;
full of real references but weakly supported claims;
consistent in style but inconsistent in terminology;
fluent after AI assistance but unclear about source scope and human responsibility.
These are not solved by adding more words.
They require a visible path between the claim, the source, the argument and the next review action.
What “traceable review” means to us
A review note is more useful when it can answer:
Which claim is being discussed?
Which source is connected to it?
What does the evidence actually cover?
Where does the inference become too broad or uncertain?
What should the author verify, revise or explain?
Traceability is not a promise of automatic truth. It is a way to make the reasoning and the remaining decisions easier to inspect.
The product boundary matters
Philologica is not intended to replace:
the researcher’s subject-matter judgment;
a supervisor’s guidance;
a journal’s editorial process;
or peer review.
An AI-assisted workflow should make human responsibility clearer, not less visible. That includes checking important sources, qualifying claims, protecting confidential material and following the applicable disclosure rules.
Why this is also a founder problem
Building for a narrow professional workflow forces difficult choices:
What problem is painful enough to revisit before every submission?
Which part of the workflow can be made more visible without overpromising automation?
How do we measure useful review actions rather than generic engagement?
How do we earn trust when the correct product behavior is to surface uncertainty?
Those questions are part of building Philologica.
If you work on academic writing, research software or trustworthy AI, we would be interested in your perspective.
Explore the product: https://app.philologica.com
Where should an AI research tool stop automating and hand the decision back to the author?
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A citation can be real, relevant to the topic and correctly formatted—and still fail to support the exact sentence it follows.
That distinction matters because academic manuscripts often compress several steps into one sentence:
a source reports an observation;
the author interprets the observation;
the manuscript generalizes the interpretation;
the sentence presents the result as a stronger conclusion.
The reference may support the first step while the sentence claims the fourth.
Three questions to ask
1. Does the source exist and can it be retrieved?
Start with the basic check. Is the source identifiable, accessible and the same work cited in the manuscript? A reference that cannot be recovered is already a review problem, regardless of how convincing the sentence sounds.
2. Does the source address the exact claim?
A source can be relevant to a topic without supporting a particular claim. Check the population, method, outcome, comparison, timeframe and level of certainty. “Related to” is not the same as “supports.”
3. What still requires human review?
Even when a source is relevant, questions may remain about scope, limitations, causal language, transferability or interpretation. Those questions should become visible review actions rather than disappear behind a citation marker.
A simple review record
For each high-stakes claim, record:
the claim as written;
the source connected to it;
the part of the source that is relevant;
the strength and limits of the support;
the next human review action.
This does not turn scholarly judgment into a fully automatic process. It creates a traceable path from a sentence to the evidence and then to a decision.
That is the problem Philologica is designed to help explore: making the relationship between claims, sources and review actions easier to inspect before submission.
Discover Philologica: https://app.philologica.com
What is the most common citation-support problem you see in manuscripts: missing sources, over-strong wording, unclear scope or something else?
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