Manahil S.

AI voice agents and automation systems that turn missed calls, slow follow-ups, cold leads, and scattered customer data into structured, measurable business workflows. I build inbound and outbound Voice AI systems, AI Receptionists, appointment-setting agents, and workflow automations that can qualify leads, capture customer details, book appointments, trigger follow-ups, update business systems, and create useful analytics from every interaction. My approach is tool-flexible. I choose the stack based on the business, existing systems, scale, compliance needs, and budget rather than forcing every project into one platform. Voice & Conversational AI: ElevenLabs, Retell AI, Synthflow, Twilio, OpenAI / GPT-based agents, Claude, Voiceflow Workflow Automation: n8n, Make, Zapier, custom Python workflows, webhook-driven automations, event-based workflows Cloud & Serverless: AWS Lambda, API Gateway, DynamoDB, S3, SES, EventBridge, Step Functions, CloudWatch, Secrets Manager, APIs & Integrations: REST APIs, webhooks, HMAC signature verification, third-party SaaS integrations, CRM integrations, calendar systems, custom backend services Data & Analytics: structured call data, conversation analytics, lead scoring, conversion tracking, appointment analytics, campaign performance, operational dashboards, reporting pipelines Databases & Storage: DynamoDB, PostgreSQL, MongoDB, Supabase, S3, depending on project needs CRM & Business Systems: HubSpot, GoHighLevel, Google Calendar, custom booking systems, spreadsheets, internal APIs, and other client-specific platforms For inbound Voice AI, workflows can include 24/7 call answering, lead qualification, FAQs, appointment booking, after-hours reception, customer information capture, call summaries, escalation, missed-call recovery, and owner notifications. For outbound Voice AI, workflows can include lead follow-up, appointment setting, lead reactivation, qualification campaigns, scheduled calls, retries for unanswered calls, callback handling, outcome tracking, campaign analytics, and automated next-step actions. The important part is what happens when the ideal flow does not happen. That means accounting for duplicate webhooks, failed calls, incomplete conversations, invalid requests, missing information, booking conflicts, retries, API failures, tenant-specific data, authentication, rate limits, fallback actions, and cases where the AI should stop and hand the interaction to a human. Voice AI also becomes much more valuable when paired with Data Analytics. Instead of leaving conversations as raw transcripts, the system can structure and analyze qualified vs unqualified leads, inbound vs outbound performance, appointment conversion rates, common customer intents, requested services, objections, drop-off points, follow-up opportunities, missed-call patterns, campaign performance, call duration, and lead-to-booking conversion. The goal is not to build automation around one specific tool. It is to build an adaptable system around the client’s actual workflow, where the voice provider, automation platform, CRM, database, cloud infrastructure, and analytics layer can be changed or extended as the business grows. Let’s talk about your business problem and build the right automation around it.