Many Canadian funeral homes run on paper or digital forms. We turn those forms into a working software workflow — one form at a time, with AI doing the heavy lifting and humans owning the judgment calls.
A funeral home's forms are decades-old artifacts of actual practice. Every field exists because someone — a regulator, an insurer, an industry custom — decided it was worth capturing. That makes the forms a far better source of truth than our hypotheses about what funeral software should do.
Cataloguing Kearney's ~150 forms is the cheapest, fastest way to validate three things at once.
Our workflow modules are hypotheses. The forms tell us which ones are real, which are wrong, and which we missed entirely.
Matt cannot design intake screens without knowing what data is captured at each step. The forms answer that exactly.
What's universal versus tenant-specific can only be drawn from real data. The forms make the boundary visible.
Forms are documentary evidence of operational reality. The schema, modules, and product scope are hypotheses about how to support that reality. The intake exercise is how reality updates the hypotheses — without losing what's already been built.Project principle
Per-form work happens continuously, form by form. Cross-form analysis happens at checkpoints. The two are coupled but not gated — ingesting can continue while analyses still run on the accumulated batch.
Continuous · one form at a time
For each form: ingest, map to the database schema, map to the workflow modules, with a human review checkpoint after each AI step. Result: an approved form file ready for analysis.
At checkpoints · every ~20–30 forms
Synthesises per-form work into architectural decisions: catalog refinements, candidate new modules, schema additions, and the workflow composition for each case flow.
Each form passes through six steps, alternating AI work with human review. AI does the cataloguing, schema-matching, and module-matching; humans confirm, correct, and resolve any ambiguity before the next step runs.
AI reads the PDF and produces a structured form file: every field with its label, data type, whether it's required, whether it's PHI, whether it's handwritten, and proposed relationships to other forms already in the registry.
Zareef opens the form file and verifies the catalogue against the actual PDF. Open Questions get answered (or batched for a Kearney follow-up). Anything obviously wrong gets corrected in place.
AI takes every field and classifies its match against the existing database schema: exact match, semantic match with a different shape, partial match (lossy), or no match. For unmatched fields, it proposes a destination — a new column, a new table, the JSONB metadata column, or the workflow envelope.
Zareef confirms exact matches, ratifies proposed destinations for new fields, and resolves any "drift flags" — cases where the same field on a different form was mapped differently. Drift cannot be silently overwritten; it must be reconciled explicitly.
AI takes every field and classifies its producing source from a nine-value taxonomy: which module writes it, captures it via UI, pre-populates it from earlier case data, derives it, or — explicitly — that no producer can be identified (an orphan).
Zareef reviews producing-source assignments, decides whether orphans are genuinely orphan or a missed mapping, and surfaces catalog refinement candidates — cases where a workflow module's declared inputs and outputs appear incomplete.
The form file is now a structured, schema-mapped, module-mapped, human-reviewed artifact. It feeds the cross-form analysis cadence — but it is never frozen. Every prompt is idempotent: re-running them on the form is safe as the schema, catalog, or sibling-form mappings evolve.
AI handles the high-volume, pattern-matching work — extracting fields, scoring matches, proposing taxonomies. Humans handle judgment calls that compound across the registry: architectural decisions, semantic disambiguation, and anything where a wrong answer would silently corrupt future work.
One form tells you a little. Twenty forms together tell you whether the module catalog is right, whether the schema is right, whether the case-flow sequencing matches operational reality. Analysis runs at checkpoints — not after every form, and not only at the end.
After every 20–30 newly approved forms accumulate. Pattern strength grows with batch size.
When all forms for a case flow (standard burial, direct cremation) reach approved status.
When clusters of unmapped fields exceed ~10 with similar candidate-module hypotheses.
Before any catalog or schema change is contemplated, so the change is informed by the current cross-form view.
For each workflow module: does the catalog's declared inputs and outputs match what the form mappings actually imply?
Cluster the orphan fields. Multiple forms missing the same producer is strong evidence for a candidate new module.
Resolve semantic conflicts in proposed envelope keys across forms. Promote stable patterns; merge subtle duplicates.
Compare Kearney's actual case-flow sequence to our hypothesized starter templates. Revise the templates where reality diverges.
What stays paper, what becomes digital, what gets scanned and attached. Informs gated approval and signature flows.
For each case flow: the complete module pipeline, the fields produced by it, and the fields requiring manual UI entry. Runs after at least one flow is fully approved.
The forms registry is not a documentation exercise. Every output feeds a downstream decision — design work, schema migration, ADR proposals, or the customer-facing workflow configuration.
For each case flow, the complete module pipeline plus the list of UI inputs Matt needs to design. This is the customer-facing deliverable — what Kearney's workflow actually looks like.
Catalog refinements, new modules, envelope schema changes, schema migrations. All recorded as architecture decisions, not absorbed silently into code.
Concrete field lists for intake screens, prep room views, family-facing surfaces. Designed against real data, not assumed data.
Forms catalogued for Kearney are the starting point for funeral home #2, #3, #4. Subsequent onboarding of new funeral homes gets dramatically faster as the vocabulary accumulates.
Kearney is the first tenant, so its forms are the seed. Every form processed teaches the AI prompts more about what real funeral home data looks like. By tenant #2, most forms will match existing entries — and most workflow configurations will already be defined. The registry is the asset that compounds across tenants; Kearney's work is the cost of building it.
This is also the discovery moment for the product itself. The forms may reveal that some "MVP" features should slip to Phase 2 — or that some "Phase 2" features are actually mandatory. That's the exercise working correctly, not failing.