i am trying to build Saas AI voice agent for radiology businesses what do i need in order to build and can i build with livekit and what tech stack is the best

Thanks for reaching out, abdul. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

yes please next step

Great — I’ll draft a concrete next-step plan. Quick questions to tailor it: 1. Will the agent be used via phone (PSTN/SIP), in-app/web, or both? 2. Do you need Live interactions only, or also asynchronous (voicemail/transcription) workflows? 3. Will the agent access PHI (radiology reports, images, patient identifiers)? 4. Expected scale (calls/conversations per hour) and geographic regions you must host in? 5. Preferred backend language (Node.js/TypeScript or Python) or open to recommendations? Answer these and I’ll return a concise MVP architecture, component list, compliance checklist, implementation milestones, and starter repo/CLI commands. , with interactive elements

The goal of this product is to make build AI receptionist for radiology businesses, um, and it will... since we cannot integrate with some of the radiology softwares, we need to have our own dashboard that they can easily copy after the customer's information is being captured, and then it easily should Capture everything that requires, and then you should prepare it. The second thing is since we already have a dashboard, every single radiology business will have separate, which is multi tenant. And... but what we need is actually, you know... and we're using Twilio, left Twilio, which is the... for the phone. And, um, yeah, um, it is going to be... and the the customer called the business number, and then the overflow goes to our agent or after hours, uh, or it goes to directly to our agent, and then our agent talks to the customer and then captures all the information. Um, I don't know. What do you mean p h i? And I don't have that. I don't know what is that. Why do I need? It's gonna be only asking questions that are required to... for example, what type of scan, if they Male or female? if they have any pregnant things like that. And then it captures that information, and then it puts to the dashboard. Nothing else. So And we might expect because every single side side will have... might have twenty, thirty calls a day, and we might just answer it some of the days, most of them. Some of the days may be less. Some some of the days may be all of them. So... and, um, yeah. So I want you to consider the scale. I'm already using the Shape plan? In live kit, ship plan. And I need your recommendations of everything. If you have any question, you can ask me.

Thanks — this is very helpful. I’ll outline a concrete MVP plan tailored to a multi-tenant AI receptionist for radiology that uses Twilio for telephony, captures required fields into your dashboard, and considers scale and compliance. Quick clarification before I finalize: do you already have separate tenant authentication and billing per customer in your dashboard, or should I include multi-tenant auth/billing design in the MVP? If you want it included, I’ll add concrete DB schema suggestions and tenant isolation patterns. , with interactive elements

do everything and remeber we are in 2026

Thanks — great details. Below is a compact, actionable MVP plan tailored to a multi-tenant AI receptionist for radiology businesses (2026), using Twilio for telephony and LiveKit (Scale plan). I cover architecture, tech stack, PHI/compliance explanation and checklist, Twilio + LiveKit integration, scaling/ops, data model & tenant isolation, milestones with rough effort, and starter links you can use. If anything should change (like self‑hosting vs LiveKit Cloud, or stricter region requirements), tell me and I’ll adapt the plan. 1) What is PHI and why it matters (short) • PHI = Protected Health Information (patient identifiers + health data). If you capture patient name, phone, scan type, pregnancy status, or other info tied to a person, that is PHI. Handling PHI requires regulatory controls (HIPAA in the U.S.), a signed BAA with vendors, secure storage, and auditability. 2) High-level MVP goals • Inbound calls (Twilio PSTN) route to your system; your AI agent answers live or after‑hours/overflow. Agent collects required fields (multi‑choice and free text), confirms with caller, and stores structured data in each tenant’s dashboard record. Multi-tenant SaaS with per-tenant isolation, auth, and billing. 20–100 calls/day per tenant supported initially. 3) Recommended architecture (components & flow) • Telephony ingress: ◦ Twilio handles PSTN termination. Use SIP or Twilio Voice webhooks to forward calls to your agent service. • Media & real-time: ◦ LiveKit (Scale plan) as media plane and agent runner. LiveKit Agents/Inferences connect STT→LLM→TTS. • Agent pipeline: ◦ STT (HIPAA‑eligible model) → LLM (dialogue + slot-filling + validation) → TTS for spoken replies. ◦ Use deterministic slot schema to capture required fields and a short confirmation turn. • Orchestration backend: ◦ Node.js (TypeScript) or Python service to manage calls, sessions, tenant logic, storage, and Twilio interactions. • Dashboard & API: ◦ Multi-tenant web dashboard (React/TypeScript) talking to backend via REST/GraphQL for viewing/assigning leads, editing, and exports. • Data store: ◦ Primary DB: PostgreSQL with row-level tenant_id and row‑level encryption for PHI columns. ◦ Optional secure object storage (S3) for recordings (encrypted at rest). • Auth & Billing: ◦ Auth: OAuth2 + JWT per tenant + RBAC for users. ◦ Billing: Stripe (per-tenant metering, seats, or usage). • Observability & security: ◦ Centralized logging (redact PHI), metrics, alerting, and audit trails for agent interactions. 4) Tech stack (recommended) • Frontend: React + TypeScript, LiveKit JS SDK for any real-time web audio UI. • Backend: Node.js (TypeScript) or Python (FastAPI); Node recommended if your team uses TypeScript across stack. • DB: PostgreSQL (with pgcrypto for column encryption or use DB‑level TDE via cloud provider). • Object storage: AWS S3 (with SSE-KMS) or equivalent region‑pinned storage. • Telephony: Twilio for PSTN; use SIP or Twilio Voice webhooks to your backend. • LLM/STT/TTS: Use LiveKit Inference with HIPAA‑eligible providers (Deepgram, Cartesia, AssemblyAI, or other U.S.-hosted models). Keep models hosted in the region required. • Infrastructure: Docker + Kubernetes (EKS/GKE) or managed app service; LiveKit Cloud for agent hosting or self-host for strict residency. • CI/CD: GitHub Actions, IaC with Terraform. 5) Twilio + LiveKit integration pattern • Option A (recommended, simpler): Twilio collects call and forwards to your backend via webhook. Backend creates a LiveKit Room and a server agent participant. Use Twilio Media Streams or SIP to bridge live audio into LiveKit if needed. • Option B (SIP trunk): Use Twilio SIP to directly SIP‑pin into LiveKit’s SIP endpoint if supported for lower-latency media path. • Flow: a. Caller dials business number → Twilio receives call. b. Twilio webhook to your backend or SIP->LiveKit bridge. c. Backend creates LiveKit Room, starts agent (LiveKit Agents) with the tenant’s configured model & slot schema. d. LiveKit runs STT→LLM→TTS; agent captures fields and emits structured JSON back to backend. e. Backend writes record to tenant DB and pushes to dashboard; optionally notify staff via webhook/email/SMS. 6) Multi-tenant design & data model (core) • Tenant isolation: ◦ Use a tenant_id column on all tenant data. ◦ Enforce row-level security policies in Postgres (RLS) and per-tenant encryption keys. ◦ Separate S3 prefixes/buckets per tenant or use encryption keys per tenant. • Minimal schema (example fields): ◦ tenants(id, name, region, billing_info, compliance_flags, encryption_key_id) ◦ users(id, tenant_id, role, email, password_hash) ◦ calls(id, tenant_id, caller_number, called_number, timestamp, status, recording_url_encrypted, transcription_id) ◦ leads(id, tenant_id, call_id, patient_name, phone, scan_type, gender, pregnancy_status, notes, captured_by_agent, created_at) ◦ audits(id, tenant_id, user_id, action, metadata, timestamp) • Access control: RLS + backend checks + signed JWT with tenant scoping. 7) Compliance checklist (HIPAA-focused) • Sign BAA with LiveKit and any model/STT/TTS vendors before handling PHI. • Ensure LiveKit Inference models and storage are in required region or self-host. • Encrypt PHI at rest and in transit (TLS everywhere, SSE-KMS for S3). • Retention policy for recordings/transcripts; allow tenants to delete/export data. • Audit logging and access logging; store minimal logs and redact PHI from logs. • Least privilege for services, rotate keys, use KMS for tenant keys. • Business processes: incident response plan, staff training, and documented consent/disclosure flows. • Disable or control agent observability storing PHI if residency policies require. 8) Scaling & ops (for ~20–100 calls/day per tenant) • Use horizontal autoscaling for agent workers and inference concurrency. • LiveKit Scale plan provides region pinning and enterprise throughput — verify concurrent call limits and model quota with LiveKit. • Architect call concurrency based on expected concurrent calls (not total daily calls). Example: 50 tenants × peak 10 concurrent calls = 500 concurrent rooms; size cluster and model quotas accordingly. • Use pre-warmed agents for peak hours to reduce cold-start latency. • Store recordings asynchronously and stream transcription results back to backend. 9) Security & privacy best practices (quick) • Do not log full transcriptions to general logs; store in encrypted DB with strict access. • Role-based UI with audit trail for data access. • Optional: redact or token‑mask sensitive fields in UI previews. 10) Implementation milestones (MVP) — rough order & estimate 1. Requirements & compliance setup (1 week) ◦ Finalize required fields, consent text, and sign BAAs with LiveKit & model vendors. 1. Basic backend + tenant model (1–2 weeks) ◦ PostgreSQL with RLS, tenant CRUD, auth (JWT), Stripe integration stub. 1. Twilio inbound flow + webhook (1 week) ◦ Receive calls, forward to backend, create call record. 1. LiveKit agent prototype (1–2 weeks) ◦ Create LiveKit Room, run simple STT→TTS flow, capture demo slot values. 1. End-to-end capture flow (2 weeks) ◦ LLM-based slot filling, confirmation prompts, persist to leads table, dashboard UI to view leads. 1. Multi-tenant hardening & security (1–2 weeks) ◦ RLS enforcement, encryption, audit logs, per-tenant config. 1. Scale & reliability (2 weeks) ◦ Autoscaling, pre-warm agents, monitoring. 1. QA, compliance review & production rollout (1–2 weeks) ◦ Pen test or security review, tenant onboarding docs. Total MVP time: ~8–12 weeks (single small team). Adjust if you self-host or need deeper compliance work. 11) Operational decisions you must confirm • Will you store voice recordings long-term, or delete after transcription? (affects storage & compliance) • Region requirements (U.S. only, or other countries)? • Do you want transcripts stored as PHI or treated as ephemeral? • Preferred backend language (Node.js vs Python)? 12) Starter resources • LiveKit HIPAA and compliance pages (for BAA and HIPAA-eligible providers). • LiveKit voice-AI quickstart & Agents docs for starter repos and CLI. (If you want, I’ll paste the exact links and starter CLI commands.) 13) Next immediate actions I can take for you • Produce a concrete DB schema and API endpoints for the MVP. • Generate sample agent slot schema + example dialog flows for radiology intake. • Draft tenant onboarding checklists and BAA/vendor checklist. • Provide starter code snippets (Node.js or Python) to wire Twilio → LiveKit Agent → DB. Which one do you want first: the DB schema & API endpoints, or the agent slot schema and dialog flows? , with interactive elements