DodoForm

DodoForm handles intake scenarios where the person submitting data cannot or will not fill out a conventional field-by-field form. Voice memos, uploaded photos, résumé files, and unstructured text notes go in; structured, schema-matched records come out. The AI extraction layer is constrained — it maps content to a predefined schema rather than reasoning freely — which keeps output predictable and auditable. Teams configure a schema, publish a form endpoint, and receive submissions in a normalized format regardless of how the respondent chose to provide the information.

The differentiating feature for compliance-heavy workflows is versioned schema snapshots. Each submission is linked to the exact schema version active at the time of collection, creating an audit trail that answers the question: ‘What rules governed this record when it was created?’ That provenance layer is uncommon in conventional form tools, and it is what makes DodoForm usable in legal intake, healthcare onboarding, and regulated consulting workflows where schema drift creates liability.

The tool fits tightly into the front end of a data pipeline — sales intake, lead qualification from unstructured contact data, NPS collection from voice and photo, job application processing, event registration, and contract collection. It does not fit when the downstream action needs to branch based on extracted content: DodoForm delivers the structured record, but deciding what to do with it — route to one team versus another, trigger one workflow versus another — requires a separate automation layer. Teams with complex post-submission logic consistently report adding a tool like Zapier or a custom API consumer to handle routing. An API is available, and a self-hosted deployment option exists for organizations that cannot send submission data to a third-party cloud.