How to Make an App Like Siri

Voice assistants have quietly become one of the most natural ways people interact with technology. Asking a phone for directions, dictating a message while cooking, or setting a reminder without unlocking a screen all feel ordinary now — but behind that simplicity sits a stack of speech, language, and orchestration technology working in milliseconds.

If you're planning to build an app like Siri, this guide walks through what such an assistant actually does, the architecture that powers it, the tech choices you'll face, and a realistic view of cost and timeline.

What an App Like Siri Actually Does

It helps to stop thinking of Siri as one product and start thinking of it as a pipeline. A voice assistant performs a chain of discrete jobs, and each one is a solvable engineering problem:

1. Wake word detection — always-on, low-power listening for a trigger phrase like "Hey Siri."
2. Speech-to-text (ASR) — converting captured audio into a text transcript.
3. Natural language understanding (NLU) — extracting the user's intent ("set_alarm") and entities ("7 a.m.", "tomorrow").
4. Dialogue management — tracking context across turns, asking follow-up questions when information is missing.
5. Action execution — calling an internal service, a third-party API, or a device function to fulfil the request.
6. Response generation — producing a natural-language reply.
7. Text-to-speech (TTS) — speaking that reply back in a human-sounding voice.

Every feature decision you make ultimately maps back to one of these seven stages.

Step 1: Define a Narrow, Valuable Scope

The most common mistake is trying to match Apple, Google, and Amazon on general knowledge. You won't win there, and you don't need to.

The assistants that succeed commercially are vertical assistants. Consider:

• A healthcare assistant that lets clinicians dictate notes and query patient records hands-free.
• A banking assistant that handles balance checks, transfers, and spending questions.
• A logistics assistant drivers can talk to without taking hands off the wheel.
• An in-app support assistant that answers product questions and performs account actions.

Pick 15–30 high-frequency intents, do them exceptionally well, and gracefully hand off everything else. Depth beats breadth.

Step 2: Choose Your Speech Stack

Speech-to-Text Options

Approach	Best For	Trade-offs
Cloud APIs (Google STT, Azure Speech, AWS Transcribe, Deepgram)	Fast time-to-market, broad language support	Per-minute cost, network dependency, data leaves device
OpenAI Whisper (self-hosted)	Strong accuracy, domain fine-tuning, data control	You manage GPU infrastructure and scaling
On-device (Apple Speech framework, Android SpeechRecognizer, Vosk)	Offline use, privacy, zero marginal cost	Lower accuracy, larger app bundle, device constraints

A hybrid pattern works well in practice: on-device recognition for short commands and offline fallback, cloud recognition for longer or more complex utterances.

Text-to-Speech Options

Neural TTS has changed user expectations dramatically. Options range from platform-native voices (free, instant, robotic-adjacent) to services like Amazon Polly and Azure Neural TTS, up to premium voice cloning providers such as ElevenLabs or PlayHT when brand voice matters. Watch latency closely — streaming TTS that begins speaking before the full sentence is synthesised feels dramatically more responsive.

Wake Word Detection

Don't build this from scratch. Purpose-built engines like Picovoice Porcupine or Snowboy-style models run efficiently on-device and can be trained on a custom phrase. Budget real effort for tuning the false-accept versus false-reject balance; an assistant that wakes up randomly is worse than one that occasionally misses.

Step 3: Build the Understanding Layer

You have two broad architectural paths, and increasingly the answer is both.

Classical Intent Classification

Frameworks like Rasa, Google Dialogflow CX, or Amazon Lex classify utterances into predefined intents and extract slots. This approach is predictable, cheap, fast, and testable — exactly what you want for transactional commands where a wrong action has consequences.

LLM-Based Understanding

Modern large language models handle open-ended phrasing, multi-step reasoning, and conversational context far better than intent classifiers. Paired with function calling (also called tool use), an LLM can read a user's request, decide which of your APIs to invoke, and populate the parameters itself.

User: "Move my dentist thing to Friday afternoon"

LLM tool call:
{
  "tool": "reschedule_event",
  "arguments": {
    "query": "dentist",
    "new_date": "2025-06-13",
    "time_window": "14:00-17:00"
  }
}

A Practical Hybrid

Route fast, high-confidence, safety-critical commands through deterministic intent matching. Send anything ambiguous, exploratory, or knowledge-based to the LLM. Layer in retrieval-augmented generation (RAG) over your own documentation and data so the assistant answers from your truth rather than from model memory.

Step 4: Handle Context and Dialogue State

Single-turn assistants feel like search boxes. Useful assistants remember.

Your dialogue manager needs to maintain:

• Session context — what was just discussed, so "move it to Friday" resolves correctly.
• Slot filling — knowing that "book a table" still needs a time and party size, and asking for exactly the missing piece.
• User profile and long-term memory — preferences, frequent contacts, home address, tone preferences.
• Confirmation policy — read-only queries can execute immediately; irreversible actions (payments, deletions, messages to other people) should require explicit confirmation.

Store session state in something fast like Redis, and persist long-term memory in your primary database with clear user-level deletion paths.

Step 5: Design for Voice, Not for Screens

Voice UX has its own rules:

• Keep replies short. Aim for one or two sentences spoken aloud, with detail available visually if a screen exists.
• Confirm by restating. "Alarm set for 7 a.m. tomorrow" closes the loop without a follow-up question.
• Fail helpfully. Replace "I didn't understand" with "I can help with orders, returns, or delivery times — which one?"
• Always offer barge-in. Let users interrupt a spoken response and start a new request.
• Show live feedback. A waveform, pulsing orb, or streaming transcript reassures users the system is listening.
• Provide a text fallback. Noisy environments, accessibility needs, and private settings all demand a typing option.

Step 6: Architect the System

A typical production architecture looks like this:

Client layer (iOS / Android / Flutter / React Native) Audio capture, wake word engine, voice activity detection, streaming upload over WebSockets, audio playback, and local caching.

API gateway Authentication, rate limiting, session routing.

Orchestration service The brain: receives transcripts, manages dialogue state, decides between deterministic intent handling and LLM reasoning, invokes tools, assembles responses.

AI services ASR, NLU/LLM inference, embeddings and vector search for RAG, TTS.

Integration layer Connectors to calendars, CRMs, payment systems, IoT devices, internal microservices — whatever your assistant needs to actually do things.

Data layer Postgres for structured data, Redis for sessions, a vector database (pgvector, Pinecone, Weaviate) for retrieval, object storage for audio artefacts.

Observability Transcript logging, intent-accuracy dashboards, latency traces per pipeline stage, and a human review queue for failed interactions.

Step 7: Optimise Latency Relentlessly

Perceived speed is the single biggest driver of whether people keep using a voice assistant. Target under 1.5 seconds from end of speech to start of audio response.

Techniques that matter:

• Stream everything. Stream audio up, stream transcripts back, stream LLM tokens into streaming TTS.
• Use endpointing well. Detect end-of-speech aggressively rather than waiting on fixed silence timers.
• Warm your models. Cold starts on GPU inference are brutal; keep instances hot.
• Cache aggressively. Common queries, TTS audio for stock phrases, and embedding lookups can all be cached.
• Pick the right model size. A smaller, faster model that handles 80% of traffic, with escalation to a larger model when needed, usually beats always using the biggest model.
• Deploy close to users. Regional inference endpoints cut meaningful round-trip time.

Step 8: Take Privacy and Security Seriously

Voice data is biometric data, and users know it.

• Be explicit about when the microphone is active, and make it visually obvious.
• Process on-device wherever feasible, especially wake word detection.
• Encrypt audio in transit and at rest; set short retention windows by default.
• Redact PII from transcripts before they hit logs or analytics.
• Obtain opt-in consent before using recordings for model training.
• Map your obligations under GDPR, CCPA, BIPA, and — for health or finance verticals — HIPAA or PCI DSS.
• Add guardrails on LLM outputs: prompt-injection defences, tool-permission scoping, and content filtering.

Tech Stack Summary

• Mobile: Swift/SwiftUI, Kotlin/Jetpack Compose, or Flutter / React Native for cross-platform
• Wake word: Picovoice Porcupine
• ASR: Deepgram, Google STT, Azure Speech, or self-hosted Whisper
• NLU/LLM: GPT-class or Claude-class models with function calling; Rasa for deterministic intents
• RAG: pgvector, Pinecone, or Weaviate with an embedding model
• TTS: Azure Neural TTS, Amazon Polly, or ElevenLabs
• Backend: Python (FastAPI) or Node.js, WebSockets for streaming
• Infra: Kubernetes or serverless GPU, Redis, Postgres, S3-compatible storage

Development Timeline and Cost

Rough guide for a focused vertical assistant:

Phase	Duration	Focus
Discovery and intent design	2–3 weeks	Scope, intent taxonomy, voice UX flows
Prototype	3–4 weeks	Single-path voice loop, 5 core intents
MVP build	10–14 weeks	Full pipeline, integrations, dialogue state, one platform
Testing and tuning	4–6 weeks	Accuracy tuning, accents, noise, latency
Launch and iterate	Ongoing	Analytics-driven intent expansion

A credible MVP typically lands in the $60,000–$150,000 range, with full-featured multi-platform assistants running higher. Ongoing costs are dominated by inference: expect per-minute ASR fees, per-token LLM charges, and per-character TTS charges, so build cost telemetry in from day one.

Metrics to Track After Launch

• Wake word accuracy — false accepts and false rejects per day
• Word error rate (WER) — segmented by accent, language, and noise condition
• Intent accuracy — correct intent classification rate
• Task completion rate — the metric that actually reflects user value
• Time to first audio — your latency north star
• Fallback rate — how often the assistant says it can't help
• Repeat usage — voice assistants live or die on habit formation

Final Thoughts

Building an app like Siri is no longer a moonshot project reserved for trillion-dollar platform companies. The speech and language components are available as APIs or open models, and the real differentiation has shifted to product judgment: choosing the right narrow domain, designing conversations that feel natural, wiring the assistant into systems that let it do things, and obsessing over latency and trust.

Start with a tight set of intents that solve a genuine daily friction for a specific audience. Ship a fast, reliable loop. Then let real usage data tell you which intent to add next.

Have a project in mind? Contact Sodio Technologies to discuss your requirements and explore the right technology solution for your business.