CasePro: rebuilding matter creation around what a client says, not what a form demands.

Intake was a text box

• The only place client-provided information lived inside a new matter was one freeform field: Notes or links to intake form.
• Nothing structured, nothing reusable, nothing a downstream workflow could act on.
• Each fact a client had already shared still had to be re-typed by hand, field by field.

One form, every practice area

• The same generic fields applied whether the matter was an auto accident, a slip-and-fall, or a commercial dispute.
• Firms couldn't tailor intake questions to their own region, county, or practice group.
• Questions that mattered for one case type showed up as clutter on every other case type.

Nothing carried forward

• If a client had already answered questions during intake, none of it flowed into the matter.
• Paralegals re-typed names, dates, and incident details a second time, by hand, from scratch.
• There was no signal for who filled in a field, or how confident anyone was that it was correct.

Polish the existing form

• Reorganize the same fields into cleaner steps, add validation, keep intake as a field inside Matter.
• Fastest to ship: no data-model change, no migration.

Rejected: a better-looking version of the same bug. Intake still couldn't be customized per firm, still couldn't be reused, still only existed inside a matter that already had to be created first.

One button, fully automatic

• A single "Generate Matter" action that always builds the record from whatever intake data exists. No manual path at all.
• The most "AI-forward" pitch, and the one stakeholders liked first.

Rejected: assumes every matter starts from a complete intake. Many don't: walk-in clients, a five-minute phone note. Forcing AI-only creation onto those cases meant either blocking the user or letting the model fill gaps in a legal record with no human in the loop.

Intake and Matter are separate, linked entities

Intake became its own object, with its own lifecycle, instead of a field inside a matter. That unlocked something the old model couldn't do: a firm can now define its own intake questions, such as the accident-type list for a state that regulates it differently, the statute-of-limitations triggers specific to a county, or the extra fields a personal-injury practice needs and a commercial-litigation practice doesn't. No engineer has to touch the matter schema, and no firm gets forced onto one generic questionnaire.

To reduce the time it takes a client's story to become usable data, I designed intake capture to be AI-assisted at the source: a voice agent can answer or join a client's call, transcribe it, and populate the structured intake fields, so a paralegal doesn't type rough notes into a blank box afterward and hope nothing was missed. Whether a field comes from that call, a web form the client filled in, or a paralegal's manual entry, it becomes a candidate the matter can pull from later.

Matter creation needs two paths, not one assumption

Not every matter starts with a complete intake. Some arrive from a thorough client call with every field captured; others start from a five-minute phone note or a walk-in client with almost nothing on file yet. A single "AI-generate the matter" button that assumes perfect intake data forces the user to bet on every case, whether or not the data supports it. So matter creation became a deliberate fork: let the person creating the matter tell the system how much is already known, and route them accordingly.

Create Manually

• Full control over every field, from a blank slate.
• The same guided six-step workflow as the AI path.
• Add details at the user's own pace, save a draft anytime.
• Still reviewed in full before the matter is created.

Field-level provenance

Each AI-touched field is tagged From Intake, AI Matched, or AI Suggested instead of appearing in one undifferentiated "auto-filled" block.

Confidence-based readiness

Intake completeness is scored per category before the AI path even starts drafting, so the user chooses a path with real information, not a guess.

Grouped cards over flat forms

Both paths replace the legacy's undifferentiated field list with labeled sections (Core Details, Classification & Tracking), so a long form still reads as a set of decisions.

Smart autofill & legal calculations

Selecting an incident address auto-fills city, state, and zip; the statute-of-limitations deadline auto-calculates from the incident state and date, with a manual override always available.

Round-robin staffing

Team-assignment roles surface the firm's own round-robin logic as a suggestion, so staffing a matter isn't starting from twelve empty dropdowns.

One review gate, no exceptions

Both paths converge on the identical grouped review screen before "Create Matter". AI assistance speeds up the draft and never skips the confirmation.

Intake specialists

The notes they capture on the client call are no longer a side note someone else re-types later; they are the record the matter is built from, structured to their firm's own questions from the start.

Paralegals

No more reconciling "what the client said" against "what the matter form wants." Either they confirm an AI-drafted record field by field with visible provenance, or they fill in one clean guided form. They never do both and never do it twice.

Attorneys & firm admins

A matter arrives at the same reviewable, grouped record no matter which button someone clicked to create it, so trust in the system doesn't depend on knowing which path was used. Firms can also reshape their own intake questions without opening an engineering ticket.