JATJay Templeton Consulting
← Where AI Pays
legal · Intake · AugmentBuy it

AI client intake & qualification

01 — The scenario

A legal business where this lands in Sales.

The friction
intakequalificationresponse-timelead-response

02 — What is actually going wrong

Every enquiry gets the same treatment: a receptionist takes a message, a lawyer calls back a day or two later, and by then a third of the good matters have retained someone faster. Meanwhile fee earners spend hours on consultations for matters that were never going to convert.

How it works

Enquiries hit a structured intake that asks the qualifying questions a paralegal would — matter type, jurisdiction, timeline, other side — runs a conflict pre-check against your client list, and scores the matter. Strong ones get a calendar link immediately; weak ones get a courteous referral.

What good looks like

Time-to-first-response drops from days to minutes. Consultation calendars fill with matters that fit your practice, and the partners stop absorbing enquiries that were always going to be declined.

03 — The problem, in money

$117,000$30,651,563/ yr at stake
Modeled
  • New inquiries per week40 inquiries250 inquiriesPlaceholder — not a validated benchmarkStarting point
  • Lift in qualified inquiries0.2 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
  • Qualified-to-matter qualified_to_sale_conversion0.3 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
  • Average matter value2,500 USD25,150 USDPlaceholder — not a validated benchmarkStarting point

04 — The options

Buy$400–$900 / moOff-the-shelf tooling, live sooner.
Bin$0Do nothing — and accept the leak.

05 — The call

Buy it

Buy it. An off-the-shelf tool wins this one — it gets you live faster, and the math does not justify building from scratch.

06 — How this goes wrong

  • Anything that reads as legal advice. The intake must gather facts and never characterise a claim — that is unauthorised practice and a regulatory problem, not a UX one.
  • Conflict checking treated as done. A pre-check narrows the list; a human still clears it before you take the matter.
  • Confidential facts sitting in a vendor's training corpus. Get the data-processing terms in writing before a single enquiry goes through.
  • Over-tuned scoring that filters out the unusual high-value matter because it did not fit the template.

07 — How you would actually do it

  1. 01Write down what actually makes a matter worth taking in your practice — most firms have never made this explicit.
  2. 02Map the intake questions to those criteria, and stop at facts.
  3. 03Wire the conflict pre-check to your practice management system, read-only.
  4. 04Set the routing: auto-book above a score, human review in the middle, polite referral below.
  5. 05Run in parallel with the existing process for a month and compare what each would have accepted.
  6. 06Audit declined enquiries monthly — that is where the scoring errors hide.

08 — Tools worth looking at

Clio GrowIntake tied to the practice management system many small firms already run.
LawmaticsStronger marketing automation and matter scoring.
Smith.aiHuman-plus-AI intake if you want a person in the loop from day one.

Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.

09 — Run it on your numbers

Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.

Worth to your business
$117,000per year
Modeled — industry averages
Revenue$312,000Modeled
Cost savings$0Modeled
Hours / week0Modeled
Industry averages
40 inquiries
Industry avg: 40 inquiriesPlaceholder — not a validated benchmark
0.2 ratio
Industry avg: 0.2 ratioPlaceholder — not a validated benchmark
0.3 ratio
Industry avg: 0.3 ratioPlaceholder — not a validated benchmark
2,500 USD
Industry avg: 2,500 USDPlaceholder — not a validated benchmark

10 — The evidence · what others report

No hard numbers reported.

Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.

11 — Questions owners ask

Every figure is Modeled — computed from industry-average benchmarks against the scenario’s formula. Move the sliders to your own inputs and the numbers recalculate live. Nothing here is a promise; it is a starting estimate you can pressure-test.
The library is organized by department, outcome, effort and readiness so you can find what fits. Add scenarios to your plan, tune the inputs, and see the combined picture on your plan dashboard.
Want this to be a Projected number? That's what the Audit produces — my forecast on your real data, with my name on it.
Build means a custom system is the right move; Buy means an off-the-shelf tool wins; Bin means the math does not justify doing it at all. Binned scenarios can be favorited for reference but never enter a plan.

Is this your business?

Get your free AI Opportunity Score — the same first pass I run at the start of every Audit.

Get Your Free Score