Elite Voice Agents

You’ll learn how to search property listings using only your voice or phone by building a voice AI assistant powered by Vapi and Make.com. The assistant pulls dynamic property data from a database that auto-updates so you don’t have to manually maintain listings.

This piece walks you through pulling data from Airtable, creating an automatic knowledge base, and connecting services like Flowise, n8n, Render, Supabase and Pinecone to orchestrate the workflow. A clear demo and step-by-step setup for Make.com and Vapi are included, plus practical tips to help you avoid common integration mistakes.

Overview of Voice-Driven Property Search

A voice-driven property search system lets you find real estate listings, ask follow-up questions, and receive results entirely by speaking — whether over a phone call or through a mobile voice assistant. Instead of typing filters, you describe what you want (price range, number of bedrooms, neighborhood), and the system translates your speech into structured search parameters, queries a database, ranks results, and returns spoken summaries or follow-up actions like texting links or scheduling viewings.

What a voice-driven property search system accomplishes

You can use voice to express intent, refine results, and trigger workflows without touching a screen. The system accomplishes end-to-end tasks: capture audio, transcribe speech, extract parameters, query the property datastore, retrieve contextual info via an LLM-augmented knowledge layer, and respond via text-to-speech or another channel. It also tracks sessions, logs interactions, and updates indexes when property data changes so results stay current.

Primary user scenarios: phone call, voice assistant on mobile, hands-free search

You’ll commonly see three scenarios: a traditional phone call where a prospective buyer dials a number and interacts with an automated voice agent; a mobile voice assistant integration allowing hands-free searches while driving or walking; and in-car or smart-speaker interactions. Each scenario emphasizes low-friction access: short dialogs for quick lookups, longer conversational flows for deep discovery, and fallbacks to SMS or email when visual content is needed.

High-level architecture: voice interface, orchestration, data store, LLM/knowledge layer

At a high level, you’ll design four layers: a voice interface (telephony and STT/TTS), an orchestration layer (Make.com, n8n, or custom server) to handle logic and integrations, a data store (Airtable or Supabase with media storage) to hold properties, and an LLM/knowledge layer (Flowise plus a vector DB like Pinecone) to provide contextual, conversational responses and handle ambiguity via RAG (retrieval-augmented generation).

Benefits for agents and buyers: speed, accessibility, automation

You’ll speed up discovery and reduce friction: buyers can find matches while commuting, and agents can provide instant leads and automated callbacks. Accessibility improves for users with limited mobility or vision. Automation reduces manual updating and repetitive tasks (e.g., sending property summaries, scheduling viewings), freeing agents to focus on high-value interactions.

Core Technologies and Tools

Vapi: role and capabilities for phone/voice integration

Vapi is your telephony glue: it captures inbound call audio, triggers webhooks, and provides telephony controls like IVR menus, call recording, and media playback. You’ll use it to accept calls, stream audio to speech-to-text services, and receive events for call start/stop, DTMF presses, and call metadata — enabling real-time voice-driven interactions and seamless handoffs to backend logic.

Make.com and n8n: automation/orchestration platforms compared

Make.com provides a polished, drag-and-drop interface with many prebuilt connectors and robust enterprise features, ideal if you want a managed, fast-to-build solution. n8n offers open-source flexibility and self-hosting options, which is cost-efficient and gives you control over execution and privacy. You’ll choose Make.com for speed and fewer infra concerns, and n8n if you need custom nodes, self-hosting, or lower ongoing costs.

Airtable and Supabase: spreadsheet-style DB vs relational backend

Airtable is great for rapid prototyping: it feels like a spreadsheet, has attachments built-in, and is easy for non-technical users to manage property records. Supabase is a PostgreSQL-based backend that supports relational models, complex queries, roles, and real-time features; it’s better for scale and production needs. Use Airtable for early-stage MVPs and Supabase when you need structured relations, transaction guarantees, and deeper control.

Flowise and LLM tooling for conversational AI

Flowise helps you build conversational pipelines visually, including prompt templates, context management, and chaining retrieval steps. Combined with LLMs, you’ll craft dynamic, context-aware responses, implement guardrails, and integrate RAG flows to bring property data into the conversation without leaking sensitive system prompts.

Pinecone (or alternative vector DB) for embeddings and semantic search

A vector database like Pinecone stores embeddings and enables fast semantic search, letting you match user utterances to property descriptions, annotations, or FAQ answers. If you prefer other options, you can use similar vector stores; the key is fast nearest-neighbor search and efficient index updates for fresh data.

Hosting and runtime: Render, Docker, or serverless options

For hosting, you can run services on Render, containerize with Docker on any cloud VM, or use serverless functions for webhooks and short jobs. Render is convenient for full apps with minimal ops. Docker gives you portable, reproducible environments. Serverless offers auto-scaling for ephemeral workloads like webhook handlers but may require separate state management for longer sessions.

Data Sources and Database Setup

Designing an Airtable/Supabase schema for properties (fields to include)

You should include core fields: property_id, title, description, address (street, city, state, zip), latitude, longitude, price, bedrooms, bathrooms, sqft, property_type, status (active/under contract/sold), listing_date, agent_id, photos (array), virtual_tour_url, documents (PDF links), tags, and source. Add computed or metadata fields like price_per_sqft, days_on_market, and confidence_score for AI-based matches.

Normalizing property data: addresses, geolocation, images, documents

Normalize addresses into components to support geospatial queries and third-party integrations. Geocode addresses to store lat/long. Normalize image references to use consistent sizes and canonical URLs. Convert documents to indexed text (OCR transcriptions for PDFs) so the LLM and semantic search can reference them.

Handling attachments and media: storage strategy and URLs

Store media in a dedicated object store (S3-compatible) or use the attachment hosting provided by Airtable/Supabase storage. Always keep canonical, versioned URLs and create smaller derivative images for fast delivery. For phone responses, generate short audio snippets or concise summaries rather than streaming large media over voice.

Metadata and tags for filtering (price range, beds, property type, status)

Apply structured metadata to support filter-based voice queries: price brackets, neighborhood tags, property features (pool, parking), accessibility tags, and transaction status. Tags let you map fuzzy voice phrases (e.g., “starter home”) to well-defined filters in backend queries.

Versioning and audit fields to track updates and provenance

Include fields like last_updated_at, source_platform, last_synced_by, change_reason, and version_number. This helps you debug why a property changed and supports incremental re-indexing. Keep full change logs for compliance and to reconstruct indexing history when needed.

Building the Voice Interface

Selecting telephony and voice providers (Vapi, Twilio alternatives) and trade-offs

Choose providers based on coverage, pricing, real-time streaming support, and webhook flexibility. Vapi or Twilio are strong choices for rapid development. Consider trade-offs: Twilio has broad features and global reach but cost can scale; alternatives or specialized providers might save money or offer better privacy. Evaluate audio streaming latency, recording policies, and event richness.

Speech-to-text considerations: accuracy, language models, punctuation

Select an STT model that supports your target accents and noise levels. You’ll prefer models that produce punctuation and capitalization for easier parsing and entity extraction. Consider hybrid approaches: an initial fast transcription for real-time intent detection and a higher-accuracy batch pass for logging and indexing.

Text-to-speech considerations: voice selection, SSML for natural responses

Pick a natural-sounding voice aligned with your brand and user expectations. Use SSML to control prosody, pauses, emphasis, and to embed dynamic content like numbers or addresses cleanly. Keep utterances concise: complex property details are better summarized in voice and followed up with an SMS or email containing links and full details.

Designing voice UX: prompts, confirmations, disambiguation flows

Design friendly, concise prompts and confirm actions clearly. When users give ambiguous input (e.g., “near the park”), ask clarifying questions: “Which park do you mean, downtown or Riverside Park?” Use progressive disclosure: return short top results first, then offer to hear more. Offer quick options like “Email me these” or “Text the top three” to move to multimodal follow-ups.

Fallbacks and multi-modal options: SMS, email, or app deep-link when voice is insufficient

Always provide fallback channels for visual content. When voice reaches limits (floorplans, images), send SMS with short links or email full brochures. Offer app deep-links for authenticated users so they can continue the session visually. These fallbacks preserve continuity and reduce friction for tasks that require visuals.

Connecting Voice to Backend with Vapi

How Vapi captures call audio and converts to text or webhooks

Vapi streams live audio and emits events through webhooks to your orchestration service. You can either receive raw audio chunks to forward to an STT provider or use built-in transcription if available. The webhook includes metadata like phone number, call ID, and timestamps so your backend can process transcriptions and take action.

Setting up webhooks and endpoints to receive voice events

You’ll set up secure HTTPS endpoints to receive Vapi webhooks and validate signatures to prevent spoofing. Design endpoints for call start, interim transcription events, DTMF inputs, and call end. Keep responses fast; lengthy processing should be offloaded to asynchronous workers so webhooks remain responsive.

Session management and how to maintain conversational state across calls

Maintain session state keyed by call ID or caller phone number. Store conversation context in a short-lived session store (Redis or a lightweight DB) and persist key attributes (filters, clarifications, identifiers). For multi-call interactions, tie sessions to user accounts when known so you can continue conversations across calls.

Handling caller identification and authentication via phone number

Use Caller ID as a soft identifier and optionally implement verification (PIN via SMS) for sensitive actions like sharing confidential documents. Map phone numbers to user accounts in your database to surface saved preferences and previous searches. Respect privacy and opt-in rules when storing or using caller data.

Logging calls and storing transcripts for later indexing

Persist call metadata and transcripts for quality, compliance, and future indexing. Store both raw transcripts and cleaned, normalized text for embedding generation. Apply access controls to transcripts and consider retention policies to comply with privacy regulations.

Automation Orchestration with Make.com and n8n

When to use Make.com versus n8n: strengths and cost considerations

You’ll choose Make.com if you want fast development with managed hosting, rich connectors, and enterprise support — at a higher cost. Use n8n if you need open-source customization, self-hosting, and lower operational costs. Consider maintenance overhead: n8n self-hosting requires you to manage uptime, scaling, and security.

Building scenarios/flows that trigger on incoming voice requests

Create flows that trigger on Vapi webhooks, perform STT calls, extract intents, call the datastore for matching properties, consult the vector DB for RAG responses, and route replies to TTS or SMS. Keep flows modular: a transcription node, intent extraction node, search node, ranking node, and response node.

Querying Airtable/Supabase from Make.com: constructing filters and pagination

When querying Airtable, use filters constructed from extracted voice parameters and handle pagination for large result sets. With Supabase, write parameterized SQL or use the restful API with proper indexing for geospatial queries. Always sanitize inputs derived from voice to avoid injection or performance issues.

Error handling and retries inside automation flows

Implement retry strategies with exponential backoff on transient API errors, and fall back to queued processing for longer tasks. Log failures and present graceful voice messages like “I’m having trouble accessing listings right now — can I text you when it’s fixed?” to preserve user trust.

Rate limiting and concurrency controls to avoid hitting API limits

Throttle calls to third-party services and implement concurrency controls so bursts of traffic don’t exhaust API quotas. Use queued workers or rate-limited connectors in your orchestration flows. Monitor usage and set alerts before you hit hard limits.

LLM and Conversational AI with Flowise and Pinecone

Building a knowledge base from property data for retrieval-augmented generation (RAG)

Construct a knowledge base by extracting structured fields, descriptions, agent notes, and document transcriptions, then chunking long texts into coherent segments. You’ll store these chunks in a vector DB and use RAG to fetch relevant passages that the LLM can use to generate accurate, context-aware replies.

Generating embeddings and storing them in Pinecone for semantic search

Generate embeddings for each document chunk, property description, and FAQ item using a consistent embedding model. Store embeddings with metadata (property_id, chunk_id, source) in Pinecone so you can retrieve nearest neighbors by user query and merge semantic results with filter-based search.

Flowise pipelines: prompt templates, chunking, and context windows

In Flowise, design pipelines that (1) accept user intent and recent session context, (2) call the vector DB to retrieve supporting chunks, (3) assemble a concise context window honoring token limits, and (4) send a structured prompt to the LLM. Use prompt templates to standardize responses and include instructions for voice-friendly output.

Prompt engineering: examples, guardrails, and prompt templates for property queries

Craft prompts that tell the model to be concise, avoid hallucination, and cite data fields. Example template: “You are an assistant summarizing property results. Given these property fields, produce a 2–3 sentence spoken summary highlighting price, beds, baths, and unique features. If you’re uncertain, ask a clarifying question.” Use guardrails to prevent giving legal or mortgage advice.

Managing token limits and context relevance for LLM responses

Limit the amount of context you send to the model by prioritizing high-signal chunks (most relevant and recent). For longer dialogs, summarize prior exchanges into short tokens. If context grows too large, consider multi-step flows: extract filters first, do a short RAG search, then expand details on selected properties.

Integrating Search Logic and Ranking Properties

Implementing filter-based search (price, beds, location) from voice parameters

Map extracted voice parameters to structured filters and run deterministic queries against your database. Translate vague ranges (“around 500k”) into sensible bounds and confirm with the user if needed. Combine filters with semantic matches to catch properties that match descriptive terms not captured in structured fields.

Geospatial search: radius queries and distance calculations

Use latitude/longitude and Haversine or DB-native geospatial capabilities to perform radius searches (e.g., within 5 miles). Convert spoken place names to coordinates via geocoding and allow phrases like “near downtown” to map to a predefined geofence for consistent results.

Ranking strategies: recency, relevance, personalization and business rules

Rank by a mix of recency, semantic relevance, agent priorities, and personalization. Boost recently listed or price-reduced properties, apply personalization if you know the user’s preferences or viewing history, and integrate business rules (e.g., highlight exclusive listings). Keep ranking transparent and tweak weights with analytics.

Handling ambiguous or partial voice input and asking clarifying questions

If input is ambiguous, ask one clarifying question at a time: “Do you prefer apartments or houses?” Avoid long lists of confirmations. Use progressive filtration: ask the highest-impact clarifier first, then refine results iteratively.

Returning results in voice-friendly formats and when to send follow-up links

When speaking results, keep summaries short: “Three-bedroom townhouse in Midtown, $520k, two baths, 1,450 sqft. Would you like the top three sent to your phone?” Offer to SMS or email full listings, photos, or a link to book a showing if the user wants more detail.

Real-Time Updates and Syncing

Using Airtable webhooks or Supabase real-time features to push updates

Use Airtable webhooks or Supabase’s real-time features to get notified when records change. These notifications trigger re-indexing or update jobs so the vector DB and search indexes reflect fresh availability and price changes in near-real-time.

Designing delta syncs to minimize API calls and keep indexes fresh

Implement delta syncs that only fetch changed records since the last sync timestamp instead of full dataset pulls. This reduces API usage, speeds up updates, and keeps your vector DB in sync cost-effectively.

Automated re-indexing of changed properties into vector DB

When a property changes, queue a re-index job: re-extract text, generate new embeddings for affected chunks, and update or upsert entries in Pinecone. Maintain idempotency to avoid duplication and keep metadata current.

Conflict resolution strategies when concurrent updates occur

Use last-write-wins for simple cases, but prefer merging strategies for multi-field edits. Track change provenance and present conflicts for manual review when high-impact fields (price, status) change rapidly. Locking is possible for critical sections if necessary.

Testing sync behavior during bulk imports and frequent updates

Test with bulk imports and simulation of rapid updates to verify queuing, rate limiting, and re-indexing stability. Validate that search results reflect updates within acceptable SLA and that failed jobs retry gracefully.

Conclusion

Recap of core components and workflow to search properties via voice

You’ve seen the core pieces: a voice interface (Vapi or equivalent) to capture calls, an orchestration layer (Make.com or n8n) to handle logic and integrations, a property datastore (Airtable or Supabase) for records and media, and an LLM + vector DB (Flowise + Pinecone) to enable conversationally rich, contextual responses. Sessions, webhooks, and automation glue everything together to let you search properties via voice end-to-end.

Key next steps to build an MVP and iterate toward production

Start by defining an MVP flow: inbound call → STT → extract filters → query Airtable → voice summary → SMS follow-up. Use Airtable for quick iteration, Vapi for telephony, and Make.com for orchestration. Add RAG and vector search later, then migrate to Supabase and self-hosted n8n/Flowise as you scale. Focus on robust session handling, fallback channels, and testing with real users to refine prompts and ranking.

Tag: property search

How to Search Properties Using Just Your Voice with Vapi and Make.com