Firms we have rebuilt.

01

Singapore · Solo + 2 · Transformation: 9 weeks

AI-Native Transformation

A solo practitioner with a paralegal, drowning in visa-refusal appeals. We built a matter container, published fixed pricing, and put a first-pass drafting agent on every appeal.

Turnaround

5 days → 18 hours

Matters / month

12 → 34

Effective rate

+3.1×

02

India · 2 partners + 4 · Transformation: 10 weeks

AI-Native Transformation

Prosecution work bottlenecked on citation checks and prior-art review. We rebuilt evidence as a graph of anchors and turned research into a compounding stock rather than a folder of memos.

Citation errors

→ 0 in QA

Prior-art hours

−72%

Repeat questions

answered from stock

03

UAE / India · Solo · Transformation: 6 weeks

AI-Native Transformation

One lawyer running arbitrations across three jurisdictions. Deadlines lived in a diary and a prayer. We built structured dates, per-action duration data, and shifted the practice to fixed engagement fees.

Missed dates

→ 0

Pricing model

Hourly → Fixed

Client NPS

+41 pts

04

India · 3 lawyers · Transformation: 9 weeks

AI-Native Transformation

A three-lawyer firm where each partner ran their matters their own way. We extracted the shared action library and defined doer/reviewer roles per action - the firm ran the same playbook by the sixth week.

Onboarding time

3 mo → 2 wks

Handoff failures

−88%

Concurrent matters

+2.4×

05

Singapore · Solo + 1 · Transformation: 8 weeks

AI-Native Transformation

A generalist counsel practice serving early-stage founders, buried in near-identical incorporation, cap-table, and SAFE-note matters priced by the hour despite being highly repeatable.

Time to engagement

1 week → same day

Matters / month

8 → 22

Weekend hours

−90%

06

UAE · Solo + 2 · Transformation: 10 weeks

AI-Native Transformation

A sensitive, high-touch practice area where the founder worried automation would feel cold to clients. We built the operating model behind the scenes, leaving the client-facing relationship untouched.

Research hours

−65%

Founder client-time

+40%

Pricing model

Hourly → Fixed

07

Singapore · 9-lawyer department · Transformation: 8 weeks

AI Adoption

A nine-lawyer department absorbing every legal question in a fast-growing company, through five uncontrolled channels. We closed the front door to one, classified the requests, and deflected the routine third before it ever reached a lawyer.

Median cycle time

6 days → 1.5 days

Routine requests deflected

34% of volume

Headcount added

0

08

India · Public-sector legal directorate · Transformation: 10 weeks

AI Adoption

A public legal directorate with a fixed budget, unbounded demand, and a nine-month procurement cycle. We ran the naming work in parallel with the approval track and installed automation inside the statutory sign-off chain rather than around it.

Advice backlog

−58%

Repeat questions

answered from stock

Discretionary decisions automated

0, by design

09

India · 60 lawyers, 11 partners · Transformation: 10 weeks

AI Adoption

Sixty lawyers, an equity partnership, and a billing model that punishes exactly what automation does. We named the track honestly in week two and installed a systematic AI layer inside the leverage model rather than pretending it could be dismantled.

First-draft time

−61% on top 2 types

Process variance

3 processes → 1

Realisation rate

+9pts

10

India · 14 caseworkers, 4 lawyers · Transformation: 7 weeks

AI-Native Transformation

More need than capacity, permanently, with no billing model to defend and nothing structural in the way. We rebuilt intake and first-pass drafting from zero - the case where recovered capacity converts most directly into people served.

Applicants served / month

+34%

Intake assessment

3 days → same day

Eligibility refusals automated

0, by design

11

India · Solo, one paralegal · Transformation: 6 weeks

AI-Native Transformation

One advocate running a full civil and commercial litigation docket alone, with a single paralegal, no associate, and no systems beyond memory, a diary, and a shared drive nobody could search. Six weeks later the practice runs on a named container, a compounding research stock, and two production agents - and the advocate bills for judgment, not typing.

First-draft time (top matter type)

−68%

Missed-deadline near-misses

2 → 0

Matters quotable as fixed fee

0 → 3 types

12

UAE · 4 partners + 6 associates · Transformation: 8 weeks

AI-Native Transformation

Four partners, six associates, and four genuinely different practices running under one roof with almost no shared process. Eight weeks later the firm runs one action library per matter type, one evidence standard applied to every partner without exception, and three production agents - and the partners, for the first time, can say with confidence what 'the firm's way' of doing something actually is.

Action sequences for top matter type

4 → 1

Contract-review first-draft time

−54%

Cross-team handoffs with a named owner

0% → 100%

13

India · 6-lawyer department · Transformation: 8 weeks

AI Adoption

A six-lawyer department inside a manufacturing group serving eleven business units, absorbing legal requests through whatever channel a business stakeholder happened to reach for. Eight weeks later the department runs one front door, a classified request taxonomy, a deflection layer answering routine questions automatically, and a contract-review agent inside the company's existing procurement-approved tooling - all installed within a corporate architecture the department itself had no authority to change.

Requests resolved without a lawyer

0% → 31%

Standard NDA review time

−58%

Request channels

5 → 1

14

India · State regulatory legal directorate · Transformation: 10 weeks

AI Adoption

A state-level regulatory agency's legal directorate, answering advice requests across a dozen departments under a fixed budget, an unbounded caseload, and a procurement cycle measured in quarters. Ten weeks later the directorate runs a dated, anchored advice stock covering its eleven most common statutory questions, a first-pass drafting layer bounded to explicitly non-discretionary categories, and a provenance record on every AI-assisted output built to survive the audit and public-records scrutiny the private sector rarely has to plan for.

Inconsistent advice incidents

→ 0 on named questions

Advice backlog

−46%

Discretionary decisions automated

0, by design

15

Singapore · 22-person company, in-house legal team of 2 · Transformation: 6 weeks

AI-Native Transformation

A twenty-two-person contract-lifecycle software vendor whose own two-person legal team ran on the same ad hoc habits as the customers they sold software to - and whose customer-support team fielded AI questions from law firm buyers they were not equipped to answer credibly. Six weeks later the company's own legal function runs the MATTER Method internally, and the company can demonstrably say it uses its own operating model before selling anyone else on adopting theirs.

Sales calls stalled on AI-governance questions

→ 0 since

Internal contract review time

−47%

Support answers routed to accurate reference

0% → 100%