Case studyLegal10 min read

DocketDesk: making sure a litigation firm never misses a hearing again

A litigation practice handled thousands of matters across district courts, high courts and tribunals, tracking hearing dates from daily cause lists by hand. One missed listing could mean an adverse order. We built DocketDesk: automated cause-list tracking, a matter calendar, AI summaries of orders, a client update portal and court-wise work allocation — so every advocate knows where they need to be, every morning.

20
Features shipped
3
Apps & platforms
6
Team members
18
Weeks to rollout
DocketDesk hearing calendar and advocate app
DocketDesk: every hearing, every court, every morning.
01

The matter that was listed and missed

DocketDesk's senior partner described the night he learned a matter had been listed that morning in a court the firm did not check that day. Nobody appeared, and the court passed an adverse order that took weeks to set aside. The firm checked cause lists every evening by hand — dozens of courts, thousands of matters, a team of clerks with printouts and highlighters.

“In litigation, a missed date isn't an inconvenience. It's a lost case.”

— DocketDesk's senior partner
02

An evening with the clerks

We sat with the clerks while they checked the next day's cause lists. Each court published its list differently; case numbers were formatted inconsistently; some lists were revised late at night. Matching thousands of firm matters against them by eye was slow and error-prone, and the result lived in a spreadsheet emailed to advocates.

What made tracking hard
  • Every court publishes lists in its own format
  • Case numbers written inconsistently
  • Late revisions to next-day lists
  • Orders downloaded and read manually
Hands signing documents
Evening cause-list checks: printouts, highlighters and a lot of risk.
03

Matching lists to matters, automatically

DocketDesk ingests cause lists from every court the firm appears in, normalises case numbers and matches them to the firm's matters. Every listing appears on the firm calendar, assigned to the advocate on the matter, with a morning summary on their phone. Late revisions trigger alerts.

When orders are uploaded by courts, DocketDesk fetches them and produces a short AI summary — next date, directions, compliance required — for the advocate to confirm. Clients see updates in their portal once confirmed.

04

The team

Court data ingestion was the core risk, so a data engineer focused on it from day one.

1
Engagement lead

Partner workshops and parallel-run oversight.

1
Product designer

Calendar, advocate app and client portal.

1
Data engineer

Cause-list ingestion and case-number matching.

2
Full-stack engineers

Matters, calendar, allocation and portal.

1
AI engineer

Order summarisation and evaluation.

6 people in total, working as one team.

05

Decisions we made

Agreed with the senior partners and office manager.

01

Manual checks or automated matching?

  • Better spreadsheets for clerks
  • Automated ingestion and matching

Our call: Automated ingestion and matching. Thousands of matters against dozens of lists daily is exactly where humans miss things.

02

How to switch over?

  • Immediate switch
  • A month in parallel with clerks

Our call: A month in parallel with clerks. A missed listing is costly. Parallel running proved reliability first.

03

Should clients see AI summaries directly?

  • Automatic client updates
  • Advocate confirms before sharing

Our call: Advocate confirms before sharing. Legal information sent to clients must be accurate and considered.

06

Every feature, module by module

Everything that shipped for advocates, clerks, partners and clients.

Tracking
Never miss a listing.
  • 01Cause-list ingestion

    Lists from every court the firm appears in.

  • 02Case-number normalisation

    Matches however numbers are formatted.

  • 03Automatic listing matches

    Listings linked to firm matters.

  • 04Late revision alerts

    Alerts when lists change overnight.

  • 05Live board status

    Current item in court where available.

Advocates
Where to be, and why.
  • 06Firm calendar

    Hearings by court, date and advocate.

  • 07Morning briefings

    Today's matters on the advocate's phone.

  • 08Court-wise allocation

    Assign appearances efficiently.

  • 09Matter notes

    Notes and documents per matter.

Orders
Read, summarise, act.
  • 10Order fetching

    Orders downloaded when published.

  • 11AI order summaries

    Next date, directions and compliance.

  • 12Compliance tasks

    Deadlines from orders tracked.

  • 13Advocate confirmation

    Summaries confirmed before sharing.

Clients and firm
Informed clients, organised firm.
  • 14Client portal

    Matter status and confirmed updates.

  • 15WhatsApp updates

    Hearing outcomes to clients.

  • 16Matter intake

    New matters with parties and courts.

  • 17Billing by appearance

    Fees recorded per hearing.

  • 18Partner dashboard

    Workload and upcoming critical dates.

  • 19Role-based access

    Partners, advocates, clerks and clients.

  • 20Audit trail

    Every change recorded.

DocketDesk AI order summary
Order summaries: next date and directions, confirmed by the advocate.
07

Launch

DocketDesk ran alongside the clerks' manual checks for a month, and every discrepancy was investigated. Once the automated matching consistently caught everything the clerks did — and some they did not — the manual process was retired and clerks moved to preparing briefs.

  1. Weeks 1–2
    Discovery

    Evenings with clerks; courts catalogued.

  2. Weeks 3–4
    Design

    Calendar and advocate app prototyped.

  3. Weeks 5–13
    Build

    Ingestion, matching, orders and portal.

  4. Weeks 14–17
    Parallel run

    A month alongside manual checks.

  5. Week 18
    Launch

    Manual process retired.

08

What we learned

Automate the high-stakes, repetitive work first. Cause-list matching was tedious and critical — perfect for software.

Keep lawyers in the loop on anything clients see. Confirmed summaries built trust with both advocates and clients.

Built with
  • Next.js
  • React Native
  • Python / FastAPI
  • PostgreSQL
  • Court data ingestion pipelines
  • LLM order summarisation
  • WhatsApp Business API
  • Private cloud
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