Talk through the job. Structured report appears in the system.
Voice capture → AI structured extraction → downstream system. Offline-capable. For field service, inspection, and healthcare ops.
Who this is for
Ops lead at a field-service, inspection, or healthcare organization where field staff hate writing reports and data loss between field and office is routine.
The pain today
- Field workers spending 30+ minutes post-job on paperwork
- Data loss — verbal info from the job never making it into the system
- Typing on phones in the field is slow and error-prone
- Handwritten reports lost, illegible, or delayed
- Report quality varying by field worker writing ability
The outcome you get
- Speak the report — AI extracts structured data into the system of record
- Works offline, syncs when connectivity returns
- Structured output matches your specific form or template
- Transcription time: minutes per job instead of 30+
- Data quality improves because workers report in-context, not from memory
On-device vs cloud transcription
Two paths with different trade-offs. On-device (Whisper.cpp, OpenAI local models): lower latency, offline-capable, privacy-preserving, constrained by device compute. Works well for field work where connectivity is unreliable or sensitive data shouldn't leave the device. Cloud (OpenAI Whisper API, Deepgram, AssemblyAI): higher accuracy, needs connectivity (or queue-and-sync pattern), faster for large recordings. Works well for healthcare, legal, and regulated transcription where accuracy is paramount. Hybrid: on-device for immediate feedback (low-latency draft), cloud for final processing (higher accuracy). I pick based on your use case — field service usually on-device, healthcare often cloud.
Structured extraction from unstructured speech
Transcription is the easy part. Structure is where value appears. Pipeline: transcript → LLM with structured-output prompt → JSON matching your form schema. For field service: job summary, work performed, parts used, customer concerns, follow-up needed. For inspection: findings per category, photos referenced, compliance status, next inspection date. For healthcare: chief complaint, findings, assessment, plan (SOAP format). LLM extracts into the structured fields; missing or ambiguous fields flagged for review. Field worker reviews the extracted form on phone, corrects typos, submits. The 10-minute typing task becomes a 30-second voice note plus 1-minute review.
Offline and poor-connectivity patterns
Field work happens in basements, rural sites, and concrete buildings. Connectivity is not a given. Architecture: local recording (indexed in SQLite), local transcription (on-device Whisper for immediate draft), queue for cloud processing if needed (higher accuracy + structured extraction runs when connectivity returns), sync to system of record on connectivity. Photo attachments queue the same way. Visual progress indicators so field workers know what's synced vs pending. This is standard mobile-first architecture applied to voice capture; getting it right is tedious but well-understood.
Integration with system of record
Extracted reports flow into existing systems. Field service: ServiceTitan, Housecall Pro, or custom field service app via API. Inspection: inspection management platform or custom database. Healthcare: EHR via HL7 FHIR or vendor-specific API (Epic, Cerner, Athena). Each integration scoped per target system's API capabilities. Data flows as drafts requiring field worker approval on phone, or for regulated workflows, supervisor approval before hitting system of record. Audit log captures full chain: audio file, transcript, extracted structure, reviewer, approval, final record.
Pricing
Voice-to-text field reports fit the AI Automation retainer at $3,000/mo. First-version timeline: 5–6 weeks to wire capture, transcription, extraction, and system integration. Retainer continues through form-schema refinement and new field scenarios. 14-day money-back, cancel anytime, Work Made for Hire. Transcription costs (cloud) typically $50–500/mo depending on recording volume; on-device incurs no per-recording cost.
Accuracy and what to expect
Transcription accuracy on clear recordings: 95%+. Field recordings with background noise, accents, or technical jargon: 85–92%. Structured extraction accuracy: 90%+ on well-formatted speech, lower when speech is rambling or incomplete. Real-world accuracy improves significantly when field workers are trained on simple speech patterns (start with 'Job summary:', structure observations clearly). This isn't because AI is strict — it's because structured speech produces structured output more reliably. A 10-minute training session pays back significantly in accuracy.
Frequently asked questions
The questions prospects ask before they book.
- Does it work without internet?
- Yes for on-device transcription. Recordings and basic transcripts work offline. Cloud-based higher-accuracy transcription and structured extraction queue until connectivity returns, usually within minutes of leaving a dead zone.
- How does it handle technical jargon?
- Custom vocabulary per engagement — technical terms, product names, medical terminology added to the transcription model. Typical vocabulary customization adds 1–2 weeks to initial setup. Accuracy on jargon-heavy domains (medical, engineering) lifts substantially with custom vocabulary.
- What about HIPAA for healthcare?
- HIPAA-eligible infrastructure — Azure Speech Services with BAA or self-hosted Whisper on HIPAA-compliant infrastructure. PHI handled with same care as any healthcare data pipeline. Security review questionnaire answers ready for HIPAA compliance officer review.
- Can the form schema change per job type?
- Yes — different job types use different forms. AC maintenance has different fields than AC install has different fields than furnace service. System selects appropriate form schema based on job type from dispatch, or field worker selects manually. Multiple form schemas supported out of the box.
- Does it replace typing entirely?
- No — typing remains the backup. Voice is the primary input, but field workers can always edit the extracted form or type directly if voice isn't appropriate (crowded area, sensitive info). The system augments; it doesn't force a specific workflow.
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