Every firm sits on years of matter narratives — time entries, billing descriptions, closing memos — that quietly record how the work really unfolded. We use embeddings to read that history, extract the real phases of work inside each matter, and turn it into accurate budgets and defensible pricing for the matters you take on next.
Most firms price new matters from memory, gut feel, and a partner's best guess. The data to do better already exists — buried in free-text narratives that no spreadsheet can read. The problem was never a lack of history; it was that the history was unstructured. Embeddings change that: they let a machine understand what a narrative means, group the work into recognizable phases, and compare a new matter to the ones that genuinely resemble it.
Five moves turn unstructured matter history into a scoping and pricing engine.
Time-entry descriptions, matter summaries, and closing memos hold the real story of each matter — what was done, in what order, and how long it took. We bring that text together as the raw material.
Input Historic matter narratives, time and fee records, matter metadata (type, client, outcome).
We convert every narrative into an embedding — a numerical representation of its meaning — so the system can tell that "reviewed first-round disclosure" and "examined documents produced by opposing counsel" describe the same kind of work, even with no shared words.
Why it matters Keyword matching misses how differently lawyers write. Meaning-based matching doesn't.
Against a phase taxonomy built with your practice groups (e.g. due diligence, drafting, negotiation, closing; or pleadings, discovery, motions, trial), we classify each narrative into the phase it belongs to — reconstructing the real shape of every past matter.
Result Each historic matter is broken into phases, with the hours, fees, and elapsed time each phase consumed.
We aggregate phase-level cost and effort across the firm's history — not as a single average, but as distributions with the drivers that move them (deal size, jurisdiction, counterparty, complexity). You see the typical case and the tail risk.
Result A living library: "for this kind of matter, discovery runs X–Y hours, and here's what pushes it higher."
For a new matter, we describe it, embed that description, and retrieve the genuinely comparable historic matters. The engine assembles a phase-by-phase budget from real precedent — with ranges and a confidence signal — instead of a number pulled from the air.
Output A defensible budget and price, phase by phase, traceable to the matters it's based on.
Like all our data work, governance comes first — matter narratives and financials are highly sensitive, so access, conflicts, and confidentiality are designed in before any data is read.
| Phase | Name | Core question it answers | Typical duration* |
|---|---|---|---|
| 0 | Discovery & Data Access | Where does our matter history live, and how do we access it safely? | 2–3 weeks |
| 1 | Phase Taxonomy Design | What are the real phases of work in our practice areas? | 2–3 weeks |
| 2 | Embed & Classify | How do we turn narratives into phases at scale? | 3–4 weeks |
| 3 | Cost & Effort Modeling | What does each phase actually cost — and what drives it? | 3–4 weeks |
| 4 | Scoping & Pricing Engine | How do we price a new matter from comparable history? | 3–5 weeks |
| 5 | Validation & Embed | Is it accurate, and can the firm rely on it? | 2–4 weeks |
*Indicative; scoped per firm and dependent on data quality. Phases overlap in practice.
Objective — Locate the firm's matter history and establish safe, governed access to it.
Objective — Define the real phases of work for each practice area, with the people who do it.
Objective — Turn every historic narrative into a phase, at scale.
Objective — Quantify what each phase costs and what moves it.
Objective — Produce a defensible budget for a new matter from comparable history.
Objective — Prove it's accurate and make it part of how the firm scopes work.
New-matter estimates grounded in what comparable matters actually cost — not a guess that gets written off later.
Fixed-fee and AFA proposals you can stand behind, with the precedent to justify them to clients and committees.
See the tail risk before you commit to a fee, so the phases that blow budgets are priced for, not absorbed.
Knowledge that used to leave with a retiring partner is captured, structured, and reusable across the firm.
We advise and implement — we build the engine on your data, validate it against your actuals, and stay until your partners are scoping with it.