changes flagged by Litera,
with full structural accuracy
changes reported by Fable 5.0,
with 42 classified as substantive
comment changes Fable detected
on a document that had them
Your redline can’t afford to be a best guess.
Every lawyer knows the unease of reviewing a redline they didn’t generate. It is the daily reality of document comparison, and speed-first AI tools have made it more urgent.
- Did the tool catch the table change on page 14?
- Did it flag the comment opposing counsel deleted between drafts?
- Did it read the live field values, or pull stale data from the cache?
The question isn’t “how fast can AI compare my documents?” It’s “can I trust what it gives me back?”
Where it counts
Litera’s Comparison engine is rules-based and deterministic. It parses the underlying structure of a Word file, the field codes, table cell boundaries, comment anchors, and embedded objects, and compares them element by element, so it returns the same correct redline every time it runs. Fable 5.0 predicts a likely answer from the text it can see.
Both tools did the same job on the same 30 MB file. On every high-stakes accuracy dimension, the rules-based engine delivered the correct result.
A false positive sends a reviewer chasing a change that never happened. A missed change is worse: it can slip into a signed document. When your comparison tool is guessing, every line of the redline becomes something you have to verify rather than something you can trust. The review doesn’t get shorter. It just gets more stressful.
Faster only matters when
the answer is right
Fable 5.0 finished the job in about 10 seconds.
But in those 10 seconds it missed every comment, skipped images entirely, confused a table, and flagged changes that weren’t real. Litera’s engine flagged 157 changes. Fable reported 57, classifying 42 as substantive. A shorter list looks cleaner, but here it came from missing real changes, not from smarter filtering.
changes flagged by Litera,
with full structural accuracy
reported by Fable 5.0,
with real changes left out
A wrong result delivered in 10 seconds isn’t a win. It’s a hidden risk that
costs far more time and trust to fix downstream.
Lito, the award-winning Legal AI agent built on a redline you can verify
Lito is built on Litera’s deterministic engines for the practice and business of law, alongside leading foundational models.
For a redline, Lito layers change classification and legal risk context on top of the Comparison engine’s deterministic output, so the accuracy foundation stays uncompromising while the intelligence layer helps lawyers prioritize.
Rules-based comparison, refined over three decades, doesn’t guess. It reads. That is the ground Lito stands on.
5 hours → 30 min
Lito and Litera’s Comparison engine compress a five-hour review into thirty minutes, without sacrificing accuracy.
Two approaches. Only one gives
the same answer twice.
DETERMINISTIC
It reads.
Rules-based logic produces the same correct output every time, given the same input. No randomness, no variation, no guessing. It reads the actual structure of the document and compares it element by element.
PROBABILISTIC
It predicts.
The kind that powers most general-purpose Large Language Models generates a statistically likely answer. Fast, often impressive. But its output is a prediction, not a fact, and you can’t tell which parts it got right just by looking at the output.
What a firm should look for in a Legal AI comparison tool
Probabilistic LLMs can’t match this on high-stakes legal work. Your comparison tool should deliver the same correct result every time, not a fresh prediction.
A tool built by engineers who specialize exclusively in legal workflows, shaped by Legal Knowledge Engineers and refined across 30 years of real-world use, will catch things a general-purpose model won’t. No AI startup can shortcut that depth.
A tool that requires a new platform, a new login, or a change in behavior won’t get adopted. The best ones are natively integrated across Microsoft 365 and Google Workspace, on Mac, PC, tablet, and iPhone.
Comparison is part of a larger matter lifecycle. A platform that connects every step from intake to closing turns comparison data into intelligence that compounds over time.
Firms that invest in productivity without an equal focus on growth risk optimizing toward lower revenue. The time AI saves has to be converted into expanded matters and new client relationships.
Efficiency alone is the wrong scoreboard
Efficiency Growth
Time saved on the mechanical work: extraction, comparison, classification, and first-pass review.
Relationship Growth
Deeper client trust, earned by work that is right the first time and does not need to be walked back.
Business Growth
Existing and new revenue, as recovered hours turn into expanded matters and new client relationships.
Accuracy is the foundation of trust
Fable 5.0 is fast, but not fully accurate. It made real mistakes on the elements that matter most in legal review: field values, tables, comments, and images. For high-volume triage where speed is the priority, fast tools have a place. For production legal review, where the redline has to be right, there is no substitute for a rules-based engine backed by 30 years of legal expertise.
When clients can count on your work being right the first time, you protect the relationship, and the revenue that grows from it. That’s what it means to Raise The Bar™.
See how Litera handles your most complex documents
Accuracy on high-stakes work isn’t something you should take on faith. Bring your own documents and see the results for yourself.
Frequently asked questions
It uses a rules-based, deterministic approach that reads the full structure of a document, including fields, tables, comments, and images. In the benchmark test, it correctly processed a 30 MB document pair and flagged 157 changes with full structural accuracy.
Yes. For high-volume triage and preliminary review where speed is the primary goal, faster tools can play a role. But for production legal review, where the redline has to be right, a deterministic comparison engine is the foundation that high-stakes work requires.
RoAI stands for Return on AI. Unlike traditional ROI, it measures AI’s full value across three dimensions: Efficiency Growth (time saved), Relationship Growth (deeper client trust), and Business Growth (existing and new revenue). Most AI platforms deliver one. Only Litera is built to deliver all three.