AI-Enhanced Legal Intelligence
Legal firms were drowning in case data. Researching precedents, reading through verdicts, and estimating win probabilities was slow, inconsistent, and heavily dependent on individual experience. They needed something that could absorb that volume and give lawyers real analytical leverage.
A leading legal firm managing high volumes of case research across multiple practice areas.
Disconnected case data and manual research slowed precedent discovery, consistency, and confident decision-making.
Quick Summary
We implemented a secure, AI-powered legal intelligence platform that unified case histories, precedent research, and probability modelling into one governed workspace. Research time down sharply — full-day tasks now take a fraction of the time, freeing teams for strategy over data-gathering.
Challenges Identified
Research was eating the calendar — associates spent days cross-referencing case law across fragmented internal systems.
Search tools did not understand legal language — keyword search returned anything with the words, not the cases that shaped the argument.
Win estimates were informal — partners relied on experience and gut instinct that could not be documented or calibrated.
Data sat in silos — case histories, verdict records, and documents were spread across systems with no unified way to query them.
Sakha Solution
Language understanding via OpenAI API and LLaMA — interprets the legal meaning of documents and reasons through nuanced language like a trained associate.
Precedent retrieval via OpenAI Embeddings — contextual similarity surfaces historically relevant cases by substance, not shared vocabulary.
Distribution-based probability modelling — shows a range of likely outcomes under different conditions, instead of a single gameable win/lose score.
End-to-end platform — analysis, retrieval, and probability scoring wired in a React, Java, and Python stack backed by MySQL and MongoDB.
Technical Highlights
Impact
Measurable outcomes delivered to our client.
Research time down sharply — full-day tasks now take a fraction of the time.
More relevant precedents surfaced — including cases that materially shifted strategy on active matters.
Probability outputs now part of the workflow — used in case-review meetings to guide where to deploy senior time.
Scales without adding headcount — the platform absorbs more case volume without proportional research effort.
"Our solution did not change how lawyers think — it gave them better information to think with."