In April 2024 we sunset Casetrack, the production case-management product AppRocket had been building and operating for 18 months. I want to tell that story here, plainly, because it shapes everything we do next.
What Casetrack was
Casetrack was a case-management product with AI built into the core: intake routing, conflict-check resolution, doc-review assist, and matter-lifecycle tracking. Real attorneys at small and mid-market firms used it. The firms that paid for it got value. I learned more about shipping AI to attorneys in 18 months running Casetrack than I had in the five years of consulting before that.
Why we sunset it
Three things became clear over those 18 months.
First, the part of Casetrack that attorneys actually paid for was not the AI features. It was the discipline behind them: the evaluation harness that meant the AI did not embarrass them in front of a client. The same firms paying for Casetrack would have paid more for that harness applied to the case-management system they already ran. We were selling a product when the market wanted the methodology.
Second, the integration tax of replacing a firm's case-management system was enormous. Even with Casetrack working better than the incumbents on the dimensions we measured, the migration (data import, attorney retraining, billing reconciliation, conflicts-database normalization) routinely doubled the perceived total cost of ownership.
Third, the services revenue we generated alongside the SaaS subscription was higher-margin and faster-compounding than the SaaS itself. We were spending most of our energy on the smaller business.
So we made the call: sunset Casetrack, and transition customers either back to their prior system with our evaluation framework deployed on top, or into a custom services engagement applying the Casetrack-developed work to a system they already owned. No customer was forced to migrate without a working alternative. The sunset communications ran on a 12-month tail.
What this unlocks
AppRocket now builds and operates AI for mid-market law firms inside whatever case-management system the firm already uses. The same evaluation discipline. The same four templates we ran in Casetrack: intake triage, doc-review assist, conflict-check, billing reconciliation. The work happens faster, costs less, and reaches more attorneys per quarter than Casetrack ever could have.
The detailed retrospective (what broke first in production, what patterns transferred across firms, what I would do differently) is published as primary-source research at /research/lessons-from-operating-production-legal-ai. It includes the buyer-side checklist I now recommend mid-market firms use to evaluate any AI vendor.
What I am telling firms now
If you are a managing partner or director of legal innovation evaluating AI right now, the playbook the Casetrack experience validates is straightforward.
- Brand and discoverability before AI. Nothing else compounds without inbound. The ABS & Co. case study walks through this sequencing in detail.
- Data hygiene before AI features. Spend the unglamorous weeks on matter taxonomy, intake routing, and conflict-data hygiene. The AI deployments that follow will succeed because of it.
- Evaluation rigor from day one. Pick AI features where the evaluation is testable and the cost of error is bounded. Skip the vendor-pitched cases that demo well and fail in production.
- Sequence by phases. Phase 1 funds phase 2; phase 2 makes phase 3 possible. The firms whose AI deployments stall are almost always the ones that tried to do all three at once.
The starting point for every firm we now talk to is the AI Readiness Audit: two weeks, $15,000, vendor-neutral output. It is the same engagement that scoped and sequenced Casetrack's customers, and the same engagement that scoped ABS & Co.'s implementation.
On sunsetting publicly
I will close by saying this. The legal-tech industry talks about "we shipped this" enough and about "we sunset this" almost never. Sunsetting publicly is a credibility upgrade, not a credibility cost. If you are a vendor whose product is not working, the longer you keep it on the market, the more attorneys you waste. Killing what is not working, and writing down what you learned, is the move I wish more vendors would make.
Casetrack taught us how to ship legal AI at production quality. That work is now in the hands of a services firm that can apply it to dozens of mid-market firms at once. Same lessons, more attorneys reached, better economics. That is the call we made, and we stand behind it.
If you want to talk about what your firm should do next, book a call. And if you want the long version of the operational lessons, the research retrospective is up.