Repeatable delivery
50+ branded applications shipped through one standardized release pipeline — builds, tests, signing, and store delivery, so shipping never depended on a specific engineer.
The pipeline →
Daniel Dayto
Designing operational AI systems for complex, high-value workflows.
AI product and engineering leader. I lead the product and engineering work behind AI systems for sales, service, and operations — from workflow design and system integration through production deployment and performance measurement.
Production results
AI voice agents deployed for a home-services operator — handling inbound calls, qualifying customers, and booking appointments directly into ServiceTitan.
- Calls handled606Inbound calls answered end-to-end by the AI
- Appointments booked83AI completed the booking workflow directly
- Completed revenue$50K+Reconciled downstream in ServiceTitan ($50,283)
- After-hours job value$25KBooked while the business was closed
Revenue attribution
- Call
- AI conversation
- Appointment
- Completed job
- Revenue
One production deployment, shown as two related views: booking value on the operator's dashboard and completed revenue reconciled downstream in ServiceTitan. Figures are reconciled from real jobs, not projected — attribution shows the AI booked the job, not that every dollar was net-new.
How I work
I start with the workflow, find where automation creates meaningful leverage, then design, integrate, deploy, and measure the system in production.
Discover
Embed with customer teams, map workflows, identify constraints, and define success metrics.
CSR interviews · dispatch shadowing · SOP extraction
Design
Translate operational requirements into system architecture, agent behavior, integrations, and implementation plans.
Solution architecture · agent design · data models
Integrate
Connect AI systems to telephony, CRMs, dispatch systems, scheduling platforms, APIs, and webhooks.
Twilio · ServiceTitan · FieldRoutes · REST · webhooks
Deploy
Ship production systems using AWS, Docker, CI/CD, multi-tenant infrastructure, and controlled rollout processes.
AWS · Docker · GitHub Actions · multi-tenant rollout
Optimize
Measure the outcome, remove recurring failure modes, and keep improving the number that matters.
Outcome tracking · evaluation · regression fixes
Selected work
Systems running in live operations — voice agents on real phone lines, a copilot inside live call workflows, and the platform behind 50+ customer apps.
Playmaker · Co-founder & Technical Lead
AI Voice Agents for Home-Service Call Handling
- Context
- Home-service call operations losing after-hours and overflow demand
- Solution
- AI voice agents integrated with ServiceTitan, scheduling, routing, and human escalation
- Result
- 606 calls handled, 83 appointments booked, and $50K+ in completed revenue traced to AI-booked jobs
02
Playmaker · Co-founder & Technical Lead
RAG Copilot for CSR & Sales Teams
Reps lacked consistent access to pricing, membership, and booking rules mid-call. A retrieval copilot embedded in the booking workflow took the client's inbound close rate from 72% to 92%.
03WOLF · Lead Mobile Developer
Multi-Tenant Deployment Infrastructure for 50+ Customer Apps
Releases depended on manual, engineer-specific steps. A standardized multi-tenant architecture and deterministic delivery pipeline lifted release velocity 35% across 50+ branded apps.
04Emirates Skywards · Senior Full-Stack Developer
High-Scale Travel Booking Platform
Rail booking had to work inside a live airline loyalty platform. SSO identity, multi-provider rail APIs, and distributed booking orchestration shipped under enterprise review — supporting millions of monthly searches.
Systems that scale
I design systems so execution becomes repeatable, deployment gets faster, and growth needs less bespoke engineering.
Faster execution
Automated deployment workflows raised release velocity by 35% — a documented before-and-after, not a remembered one.
Before and after →Scalable onboarding
Customer-specific behavior — rules, service areas, integrations — moved into configuration, reducing the need for product forks.
The deployment model →What I specialize in
The useful unit of AI is a completed business workflow — not a model response. I work these problems end to end, on the engineering foundation below.
Capture & convert demand
- AI voice agents
- Lead qualification
- Appointment booking
- Sales follow-up
- 24/7 inbound coverage
Automate manual workflows
- Agent orchestration
- Workflow automation
- Human-in-the-loop
- Exception & failure handling
- Conversational AI
Augment employees with AI
- CSR & employee copilots
- RAG
- Knowledge retrieval
- Live-call assistance
Integrate into operational systems
- ServiceTitan
- FieldRoutes
- Twilio
- CRM & dispatch
- REST APIs & webhooks
- Multi-tenant configuration
Measure & optimize outcomes
- Evaluation & QA
- Observability
- Revenue attribution
- Regression detection
- Operational dashboards
Engineering foundation
- Python
- FastAPI
- Node.js
- TypeScript / React / Next.js
- PostgreSQL
- Redis
- AWS
- Docker
- GitHub Actions · CI/CD
About
Turning messy operations into systems that hold up
Robotics data tooling, enterprise travel integrations, a 50-app multi-tenant platform, and now production AI for service businesses — the same work each time: understand a real operation well enough to build the system that runs it. Co-founder and technical lead at Playmaker.
Contact
Deploying AI into a real operation?
Hiring for a senior role
Senior AI, Forward Deployed Engineering, product engineering, platform, and technical leadership roles.
Need a problem solved
Selected contracting and advisory work involving production AI deployments, integration builds, platform architecture and audits, and technically difficult product initiatives.
Run a home-service business and want AI answering your phones? That's Playmaker.