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  • Legal managed review & ALSPs
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For managed review and ALSPs

AI is repricing document review.Your clients are the ones repricing it.

Taiso checks every extracted term against the document it came from — escalates only what it can't confirm to your reviewers, and ships each matter with an evidence pack your client's counsel can defend. Same reviewers. Every document covered.

Run one closed matter, verified →See it run on a matter
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The arithmetic nobody says out loud

You can't sample your way
out of an accuracy question.

Your client asks how you know the review was right. Your QC sampled ten percent — so you can answer for ten percent.
You bought an AI review tool. It was fast, it was cheap, and nobody could tell you which of its answers to trust.
Under time-and-materials, every hour the machine saves is an hour you can't bill. Efficiency on its own just shrinks you.
Hiring more reviewers scales the cost, not the confidence. The ninety documents nobody opened are still your liability.

This isn't a staffing problem you can fix with better people. It's a coverage problem — and coverage is an architecture decision, not an effort decision.

See it run — one closed matter

Watch every document get checked.
Not a sample.

A two-and-a-half-minute walkthrough of one hundred documents from a single matter — narrated by the reviewer who used to read all of them.

STEP 1 OF 6Your matter today
NarratorOne hundred documents land on the review team today.
One matter · 100 documents10 sampled · 90 unopened
Taiso verification gate — every extracted term checked against the document it came from, before it reaches the deliverable
sampled by a reviewerdelivered unopenedchecked against sourcethe unconfirmed few
90documents delivered to the client that nobody opened
10documents your QC actually sampled

The ninety you didn't open are the ninety you still certify.

0of 100 checked against their source text
confirmed0
the machine couldn't confirm0

No reviewer re-reads a page.

Document 66 of 100Master Services Agreement — limitation of liability
The extraction says$4.5M
The agreement says“Aggregate liability shall not exceed $1.5M over the term.”
VerdictDISPUTED · held for a human
Confidently wrong, and defensible-looking on the page. The kind of term a ten percent sample finds after the client does.
Reviewer queue0
Your reviewers open these ten — not all one hundred. The same hours you spend on the sample today, spent on the documents that actually need judgment.
Evidence pack0 / 100
confirmed0
adjudicated by a reviewer0
delivered unopened0

The trail your client's counsel can defend.

Sampled review10 wrong terms delivered — found later, if ever. Your reviewers re-read the rest by hand.
Verified review0 documents delivered unchecked · 10 adjudicated by a reviewer · 100 in the evidence pack. Same review hours.
Run one closed matter, verified →
←/→ step through · space plays · V mutes

Illustration of the mechanism on a hundred matter documents. Volumes and counts are illustrative, not measured results — the exception rate most of all. How many items actually need a person is the number the parallel run measures on your own work, and it is the number that decides whether the hours hold.

Every document, not a sample

One hundred percent of the matter.
At the cost of your ten percent sample.

Taiso reads every document, checks each extracted term against the passage it came from, and routes only what it can't confirm to your reviewers. Their hours stop going into re-reading and start going into judgment.

Todayyou sample the matter
sampled by a reviewerdelivered unopened — your liability

Everything you didn't open, you still certify.

With Taisoevery document, checked
checked against its source, deliveredthe few nobody could confirm → your reviewers
in the evidence pack — the trail counsel can defend

Same review hours. Every document systematically checked. The machine reads all of it; your reviewers decide the few it couldn't confirm — and every decision lands in the pack.

Start with Taiso Verify on a single workflow — no migration, and nothing new for your team to learn. Scale to the Taiso Agent Control Platform — your whole review operation, verified.

Read the room

What your GC asks
before signing off on this.

PRIVILEGE
Matter content stays inside your walls
Run it in your own boundary and nothing leaves. On the hosted path it runs under a DPA with a no-training clause and retention you set. A term you sign, not a promise we make.
YOUR PLATFORM STAYS
We compose, not replace
Keep the review platform you already run. Their AI drafts and prioritises; nothing in that bundle checks a claim against its source or writes a record your client's counsel can defend.
YOUR REVIEWERS
The staffing model converts, it doesn't shrink
Reviewers stop reading everything and start adjudicating what the machine could not confirm — higher-judgment work, billable as premium QC rather than volume.

On certification: SOC 2 Type I is on the clock, and Type II is what most financial buyers gate on — we are not going to pretend otherwise. Closed work is still confidential data, so the pilot runs under NDA and a DPA, in your environment or ours, and goes through the same security review your clients require of any new processor. We will fill in the questionnaire.

How the gate works

Five checks
on every extracted term.

01
Intent
Does the extraction answer what the playbook actually asked?
asked for the renewal date, returned the execution date → mismatch
02
Evidence
Is the term in the document, or did the model infer it?
“$25,000 in aggregate” located on page 14 → supported
03
Policy
Does the handling follow your review protocol?
privileged document routed to the wrong review team → blocked
04
Risk
How much does this term cost you if it's wrong?
liability cap on a live matter → a reviewer decides, not the model
05
Audit
Is the decision recorded, sourced and replayable?
term, source passage, verdict, reviewer, timestamp → evidence pack

Five checks, one verdict — ALLOW — before the term reaches your client's deliverable.

What the arithmetic actually is

Your coverage today,
and the same hours under a gate.

10%
of the matter your QC samples
100%
checked against source, same reviewer hours
0
documents delivered unopened

Your sampling rate is the only number that changes. The reviewer hours do not.

What survives the matter

The proof compounds.
The next matter starts ahead.

EVIDENCE PACK
A trail counsel can defend
Every term, the passage it came from, the verdict, and the reviewer who decided it — exported with the matter, yours to hand to the client.
YOUR PLAYBOOK, EXECUTABLE
The wiki becomes the procedure
Your review protocol stops living in a document nobody opens and starts running as the thing that checks every extraction, the same way, every matter.
YOUR EXCEPTIONS
The judgment stays yours
Every call your reviewers adjudicate sharpens what gets escalated next time. The queue gets shorter while the record gets longer.

By day 3 you see what it would have caught on your own matter. By day 14 you have the evidence pack and the list of what your sample missed — yours to keep either way.

Sound familiar?

Where the margin goes
on a fixed-fee matter.

THE CLIENT ASKED
“How do you know the review was right?”
You have a sampling rate and a process document. They wanted a number, a source, and a trail.
THE TOOL DIDN'T ANSWER IT
You tried AI review and met the anxiety
Faster and cheaper, with no way to tell a good answer from a confident one. So it never left the pilot.
THE RFP DECIDED ON PRICE
You lost one on AI economics
Someone quoted work you can't match at your cost base — and matching it means delivering review you can't defend.

If two of these are true, the pilot below was built for you: one matter you have already closed, and nothing in production changes.

The offer

One closed matter.
Your own numbers.

Give us one matter you have already delivered, under NDA. We run it in parallel — your client never touches it, your production never changes — and we hand back what the pipeline caught, what your sample missed, and what we got wrong. Bounded defect rates with the sample size beside them, never a zero-miss promise.

Run one closed matter, verified →Read the docs
Taiso

The Agent Control Platform — the governance layer that makes AI agents reliable enough for production. Controls, not prompts.

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