Every phone call is a data source. But in most organizations, that data never makes it anywhere useful. Calls are recorded, stored in a system no one checks, and eventually deleted. The insights they contain are lost.
The FYD pipeline exists to change that.
Stage 1: Ingestion
The pipeline begins at the call itself. Calls routed through FYD's telephony infrastructure are captured as audio streams in real time. Each stream is tagged at the moment of capture with campaign or workflow identifier, respondent identifier, call timestamp and duration, and channel metadata.
This metadata envelope travels with the audio through every subsequent stage.
Stage 2: Transcription
Audio streams are fed into a transcription layer that produces time-aligned transcripts — text output where every word is tagged with the timestamp at which it was spoken.
Time alignment matters for two reasons. First, speaker diarization — identifying who is speaking at any given moment — requires a temporal map of speech segments. Second, some extraction tasks require temporal context, not just text.
The pipeline includes an audio quality assessment step that routes low-confidence transcripts for human review rather than allowing noisy data into the structured output.
Stage 3: Entity Extraction
With a clean transcript in hand, the extraction layer identifies and pulls out the specific data fields defined by the campaign schema. For a campaign designed to collect field visit confirmations, the schema might define visit status, site name, issues encountered, and estimated completion date.
The extraction model handles the fact that a respondent might say "yeah I was there this morning, finished everything except the east wing" rather than cleanly answering structured questions.
Extracted values are scored with confidence levels. High-confidence extractions pass through automatically. Low-confidence extractions are flagged for review.
Stage 4: Validation and Normalization
Raw extractions need normalization before they're useful as data. Dates might be extracted as "last Tuesday," "March 15," or "the 15th" — all valid, none directly comparable without normalization.
The validation layer normalizes dates and times to ISO formats, maps categorical responses to canonical enum values, cross-validates related fields, and applies business rules specific to the campaign schema.
Stage 5: Storage and Surfacing
Validated records are written to the target data store. Alongside the structured record, the pipeline stores the original audio, the transcript with speaker diarization, extraction confidence scores for each field, and the full audit trail from ingestion through validation.
Every structured data point can be traced back to the exact moment in the call where it was captured.
The Business Value
Organizations that implement the FYD pipeline typically see three immediate changes:
- Latency collapse — Data that previously took 24-48 hours to encode is available within minutes of call completion
- Coverage improvement — Calls that previously went unencoded due to time constraints are now processed automatically
- Quality consistency — Structured records are produced to a uniform standard, regardless of who handled the call
The pipeline does not replace human judgment. It removes the human from tasks that do not require judgment, freeing them for the cases that do.