01
Repeatable delivery
At WOLF, releases stopped depending on specific engineers: automated builds, tests, code signing, and store delivery across 50+ branded apps meant anyone on the team could ship — and the team did, continuously.
The pipeline →
Daniel Dayto
I turn complex operations into scalable, revenue-producing systems.
Co-founder and technical lead at Playmaker. I work across product, engineering, AI, and operations to take ambiguous problems from customer discovery through production deployment, repeatable delivery, and measurable revenue impact.
Measured impact across revenue, conversion, and scale
I build systems that improve how companies acquire customers, convert demand, and deliver products at scale.
- Revenue~$20K/monthIncremental client revenue attributed to AI-booked appointments
- Conversion72% → 92%Inbound conversion after deploying an AI-assisted booking system
- Deployment80% fasterCustomer deployment through reusable platform infrastructure
- Scale1,000+ releasesDelivered through a multi-tenant product and release system
How I Work
The forward-deployed delivery loop
Every engagement follows the same loop: embed with the team that owns the workflow, turn their operation into a system, ship it to production, and keep improving the number that matters.
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
Operationalize
Measure the outcome, remove recurring failure modes, document the operating model, and leave behind a system the team can run and extend.
Outcome tracking · runbooks · repeatable onboarding
Selected Work
Case studies in production delivery
Systems deployed into live operations — voice agents on real phone lines, copilots inside live call workflows, and the infrastructure that ships software to 50+ customers.
Playmaker · Co-founder & Technical Lead
AI Voice Agents for Home-Service Call Handling
- Context
- Home-service call operations losing after-hours and overflow demand
- Intervention
- Production voice agents with CRM and dispatch integrations, human escalation, and outcome tracking
- Result
- ~$20K/month in client appointments attributed 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.
→Operating Leverage
What I leave behind
Implementations end; systems and processes keep working. The durable output of my work is infrastructure other people run.
02
Faster execution
Replacing engineer-dependent deployment steps with a deterministic CI/CD process raised release velocity by 35% — a before-and-after that is documented, not remembered.
Before and after →03
Scalable onboarding
At Playmaker, business rules, service areas, and integrations live in per-customer configuration — launching a new customer is configuration work, not a product fork.
The deployment model →Capabilities
Technical depth across the full deployment path
Production AI
- Conversational AI
- RAG
- LLM orchestration
- Agentic workflows
- OpenAI
- ElevenLabs
- Twilio
Platforms & Integrations
- Python
- FastAPI
- Node.js
- REST APIs
- Webhooks
- CRM & dispatch integrations
- Multi-tenant architecture
- PostgreSQL
- Redis
Product & Delivery
- Customer discovery
- React / Next.js / TypeScript
- React Native
- Operational dashboards
- AWS
- Docker
- GitHub Actions
- CI/CD
About
I work at the boundary between complex systems and real operations
Robotics data tooling, enterprise travel integrations, a 50-app multi-tenant platform, and now production AI for service businesses — the common thread is taking operationally messy problems and turning them into systems that hold up. 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.