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An in-house voice AI system can make callers hear a response sooner by routing each turn before retrieval, streaming the language model’s output into text-to-speech, and keeping service connections warm. In a September 11, 2026 DEV Community case study, software engineer Mehar Aziz reports reducing time-to-first-audio from roughly nine seconds to about 1.5 seconds on a typical warm knowledge question. That is one implementation’s account—not an independently validated benchmark—and it measures the start of spoken output, not completion of the answer.
What the 1.5-second result measures
Time-to-first-audio is the interval from a caller’s turn to the moment they hear the first part of the assistant’s reply. It does not mean the whole answer is ready or spoken in 1.5 seconds. Aziz’s reported figure applies to a typical warm knowledge turn: retrieval context is cached or already available, the language model streams its response, and text-to-speech starts after the first complete sentence is ready. The account does not provide a controlled test protocol, sample size, latency percentiles, or independent validation, so treat both the roughly nine-second initial delay and the roughly 1.5-second result as author-reported figures for that system (Mehar Aziz’s DEV Community case study, September 11, 2026).
The result should not be generalized to every caller turn, a cold tool-dependent request, or total answer completion. The author also reports query embedding taking roughly 10–30 milliseconds after moving it on-box; that is one component timing, not a complete latency budget.
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Aziz’s project began with a Vapi integration for automated onboarding calls. Requests for more customization, particularly a more expressive voice, prompted direct integration with Cartesia and eventually a move toward owning more of the platform layer. The case study describes Twilio for telephony, Deepgram for speech-to-text, Cartesia for text-to-speech and voice generation, and an in-house server coordinating the flow with a language model.
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On an incoming call, Twilio streams audio in both directions to the voice server over WebSockets. The server forwards caller audio for transcription, routes the resulting text, optionally retrieves company-specific information or calls a backend tool, and supplies relevant context to the language model. Generated speech then travels through Cartesia and Twilio to the caller. For company knowledge, the implementation stores document chunks and embeddings in Postgres with pgvector, associated with an assistant.
Why the original pipeline took longer
The initial flow performed most work in sequence: wait for the final transcript, create a query embedding, search a vector database, wait for the complete language-model response, send that response to text-to-speech, and only then start playback. Each stage could add waiting time before the caller heard anything. Aziz says this sequence produced roughly nine seconds of silence on a typical company-knowledge question in the implementation.
The redesign focused on overlapping independent work and skipping work a turn did not need. Instead of treating every utterance as a document-search request, the server first decides whether it is small talk, a knowledge question, or a tool request. When knowledge retrieval is needed, streaming lets speech generation begin before the language model has finished producing the entire answer.
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Changes that reduced the wait to first audio
Route the turn before searching
A fast router separates greetings and acknowledgements from knowledge questions and backend actions. A greeting does not need a vector search, and an appointment request should follow a tool path rather than automatically querying company documents. Retrieval becomes a conditional branch instead of a default step.
Stream complete sentences into speech
Once the necessary context is available, the language model streams its answer. The server buffers the output until it has a complete sentence, then sends that sentence to Cartesia while the model continues generating. The caller can begin hearing a response without waiting for the entire answer to be composed.
This approach requires care: the first spoken sentence may be incomplete or later need qualification. The case study describes keeping responses short and applying retrieval-confidence thresholds to reduce the chance of speaking weakly supported information.
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Keep service connections ready
The implementation reuses language-model sessions, maintains a persistent Cartesia WebSocket during a call, and keeps warm sockets available. The goal is to avoid paying the full connection setup delay when the caller is waiting for the assistant to begin speaking.
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Prepare query work before the turn ends
Where possible, partial transcripts are used to create a query embedding speculatively before end-of-turn confirmation. Aziz also describes removing filler words for caching and moving query embedding on-box. Local embeddings must stay aligned with the embeddings used during document ingestion; otherwise, vector search can become unreliable.
Keep retrieval small and relevant
The live answering path uses a similarity threshold and a small amount of retrieved context, with a faster model where appropriate. The author’s stated preference is to acknowledge missing detail or transfer to a person rather than force weak retrieval into a slow or potentially incorrect answer. Query-rewriting heuristics can help, but they need maintenance and may not generalize to every phrasing.
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- ONE-TAP RECORDING FOR REAL-LIFE MOMENTS: Capture meetings, phone calls, and in-person conversations instantly with a simple tap, no typing, no interruptions, just effortless note-taking anywhere you go.
- SMART AI INSIGHTS & ORGANIZATION: Pocket automatically turns recordings into clear summaries, key action items and structured conversation maps so you can quickly review what matters without digging through audio.
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Move document preparation out of the call
Parsing documents, splitting them into chunks, generating their embeddings, and storing them happen before a live call. The time-sensitive path then needs only to create or retrieve the query embedding, search the vector store, assemble a concise context block, and stream a response.
Make conversational behavior part of the real-time loop
A production voice system must do more than pass audio through a model. The case study describes end-of-turn detection, eager handling of turns, cancellation when a caller interrupts, distinguishing a backchannel from a true barge-in, tool calls, transfers, and short filler speech while backend work is underway. These behaviors affect whether a fast response feels natural and whether the system can recover when a caller changes direction.
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Why appointment booking can take longer
A warm knowledge question and a cold appointment request are different workloads. Booking may require checking availability, confirming details, calling backend services, and presenting options. The case study does not claim a 1.5-second time-to-first-audio for that path. Even if the assistant can speak a brief acknowledgement quickly, completing the task still depends on the external action and the information the caller must provide.
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What an in-house system makes your team responsible for
Owning the orchestration layer brings control over routing, context, voice behavior, and streaming, but it also shifts operational responsibility to the team. A managed platform may absorb some of the complexity; a custom system must handle the call’s edge cases and failure paths itself.
- Turn detection, interruption cancellation, and backchannel handling.
- Transfers, tool calls, and recovery when a backend service is slow or unavailable.
- Connection reuse and reconnection across telephony, speech recognition, language generation, and speech generation.
- Guardrails, recordings, transcripts, and the policies that govern how call data is handled.
- Retrieval quality, embedding consistency, and decisions about when to answer, ask for clarification, or hand off to a person.
That ownership is the central trade-off: latency and customization may improve, but the team is also responsible for the orchestration loop that makes the call dependable. The case study describes a cost-estimation exercise but gives no prices or totals, so it does not establish that a custom stack is cheaper.
How to evaluate a managed or custom voice AI approach
Compare approaches with the same workload and measurement boundaries. A first-audio result on a warm knowledge question should not be compared with end-to-end completion for a cold appointment booking. Assess:
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- Customization control over routing, voice, tools, and conversational behavior.
- Time-to-first-audio for the same caller turn, including warm and cold paths.
- End-to-end answer or task completion time, measured separately from first audio.
- Integration and operating effort, including ownership of call edge cases and guardrails.
- Total cost at the team’s actual traffic and staffing level; the case study provides no cost figures for either approach.
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