AI Receptionist

Executive summary

An always-on virtual receptionist that answers customer questions, books appointments, and captures leads by chat, email, or phone. It is grounded in the business's own documents so answers are accurate, and it hands off cleanly to staff when needed. The result is instant response around the clock without adding headcount.

Business problem

A business spends too much time answering the same questions, booking appointments, and collecting lead information. Calls and messages arrive after hours and go unanswered, slow responses lose prospects to faster competitors, and front-desk staff are pulled away from higher-value work.

Architecture

AWS services

Data flow

1. 1

   A customer opens the chat widget (served via CloudFront) or calls/emails in.

2. 2

   The request hits API Gateway, which authenticates and rate-limits it, then invokes Lambda.

3. 3

   Lambda classifies intent and, for knowledge questions, queries the Bedrock Knowledge Base, which retrieves the most relevant passages from the company documents in S3.

4. 4

   Lambda calls Amazon Bedrock with the question plus retrieved context to generate a grounded answer, or to drive a booking / lead-capture flow.

5. 5

   Appointments and leads are written to DynamoDB; confirmations go out via SES and escalations via SNS.

6. 6

   Every turn is logged to CloudWatch for monitoring and quality review.

Security considerations

• Private, least-privilege IAM roles per Lambda; no long-lived credentials.
• All data encrypted in transit (TLS) and at rest (S3, DynamoDB SSE).
• API Gateway throttling plus per-IP/session rate limits guard against abuse and runaway model cost.
• Customer data is isolated per business and never used to train public models.
• PII in conversation logs can be redacted; retention windows are configurable.

Cost considerations

• Serverless and pay-per-use: DynamoDB, Lambda, API Gateway, and S3 are effectively $0 at idle.
• The main variable cost is Bedrock inference (a few cents per conversation, model-dependent).
• A real Bedrock Knowledge Base adds a vector-store cost; a lightweight self-managed retriever avoids it for smaller document sets.
• Typical small-business running cost lands in the $30–150/month range at moderate volume.

Scalability

• Fully serverless — scales from a handful to thousands of conversations with no infrastructure changes.
• Concurrency is handled by Lambda; DynamoDB on-demand absorbs spiky traffic.
• Knowledge Base grows simply by adding documents to S3 and re-syncing.
• Multi-location or multi-brand support via a businessId partition key.

Deployment roadmap

Future enhancements

• Voice channel via Amazon Connect + transcription for phone answering.
• Two-way calendar sync (Google/Microsoft) for real-time availability.
• Outbound follow-ups and appointment reminders by SMS.
• CRM write-back so every lead lands in the client's system of record.