DoiT’s Cloud Accelerator transforms DaySmart’s data into scalable AI-driven insights
Meet DaySmart
DaySmart is a global vertical SaaS provider serving over 30,000 businesses across recreation and fitness, pet and animal care, beauty and wellness, automotive, healthcare, government and SMB markets. For over 20 years, the company has developed purpose-built business management platforms that enable organizations to manage appointments, staff, payments, client communication, schedule classes and facilities, and operate more efficiently.
Its product suite includes DaySmart Appointments, TeamUp, BoxMate, Sawyer, DaySmart Vet, DaySmart Pet, Time To Pet and industry-specific solutions supporting salons, spas, fitness operators, veterinary offices, training centers, youth activity providers and independent business owners.
The company’s approach is tailored, not generic. Every product category has unique operational, financial and scheduling patterns, and DaySmart invests deeply in designing systems that match real-world workflows. That philosophy extends to analytics and data enablement.
“When you serve so many specialized industries, assumptions don’t work,” said Tim Green, SVP of AI at DaySmart. “We have to deliver insights and functionality that understand each customer’s reality. AI is an opportunity to scale that without losing precision.”
To deliver on that, DaySmart runs a modern cloud architecture powered by AWS services including AWS Lambda, AWS Fargate for Amazon ECS, API Gateway, CloudWatch and AWS Cloud Development Kit. Cloud infrastructure is mission-critical. Reliability and scalability are table stakes.
The Challenge
DaySmart entered the partnership with DoiT during a pivotal transformation period. AI had shifted from an exploratory initiative to a core part of product strategy. However, operationalizing AI across multiple product lines proved to be complex. Different vertical SaaS platforms produce different data behaviors, metrics and KPIs.
For recreation businesses, attendance and membership cadence drive decisions. In veterinary operations, appointment turn-time, case types and repeat visit patterns matter. Across beauty and wellness, fill rates, cancellations and retention are critical. One AI model or one dashboard could not serve them uniformly. “Our challenge was generating insights that understand context,” said Tim Green. “Fitness data and veterinary practice data behave differently. And we needed to deliver value across all of them, reliably and at scale.”
DaySmart’s early experiments with ChatGPT and other LLM tools revealed limitations: while they could summarize data, they lacked integration with production pipelines, security controls, and automation.“We’d done tests,” said Green. “But experimentation only gets you so far. We needed a production-grade system. Customers expect accuracy, stability and clarity, not proofs of concept.”
At the same time, DaySmart’s infrastructure spanned multiple acquired products, each with its own cloud footprint. Consolidating visibility and cloud governance was becoming a priority as AI workloads increased.“Before DoiT, infrastructure lived inside different business functions,” said Green. “No unified system existed for cloud visibility, governance, or scalable decision-making. We needed a way to govern and guide cloud use across products without slowing innovation.”
The combination of challenges, multi-vertical product data, fragmented cloud ownership, readiness to operationalize AI, required a partner who could accelerate development and enforce governance at the same time.
The Solution
Driving clarity through collaborative discovery and use-case design
The partnership began with a structured discovery phase supported by DoiT’s Cloud Accelerators: GenAI, which aligned DaySmart’s strategic AI goals with executable technical plans. Rather than isolate use cases, DaySmart and DoiT co-developed a roadmap to embed intelligence across vertical product lines, aligning AI innovation with cost, security, and scalability. “We didn’t want AI experiments sitting on the side of the business,” said Tim Green, SVP of AI at DaySmart. “DoiT’s accelerator model helped turn ideas into action, fast and responsibly.”
This initial phase defined one north star objective: create scalable AI-driven business insights tailored to each of DaySmart’s industries, enabling thousands of businesses to understand performance trends, benchmark against peers, and unlock growth recommendations from their operational data.
From experiments to architecture: building a production AI system
DaySmart had validated the concept through early testing with ChatGPT, but needed a production-grade system. The DoiT Cloud Accelerators: GenAI engineering track provided hands-on solution architecture, implementation support, and technical enablement, compressing what would usually take months of internal engineering.
DoiT designed a modern, serverless architecture built on AWS Lambda, ECS Fargate, API Gateway, CloudWatch, IAM, Secrets Manager, and CDK, unified by an MCP-based data access layer. This created scalable pathways to ingest large datasets, transform them through LLM-driven intelligence, and return actionable insights. “We didn’t just build a tool, we built an intelligent platform,” Green said. “DoiT gave us the architecture, the guardrails, and the ability to scale.”
Solving the data depth vs. data overload challenge
DaySmart operates multiple products, each with unique data behavior and vertical-specific KPIs. DoiT and DaySmart implemented a hybrid data approach, retrieving summarized monthly information and selectively diving deeper into specific periods when needed. This reduced noise, preserved accuracy, and enabled true business-contextual insights. “The agent retrieves aggregated trends, then chooses when to go deeper,” said Green. “It’s smart, efficient, and respects how our customers really measure success.”
Governing cloud and cost while scaling experimentation
DoiT supported DaySmart in embedding governance and aligning to AWS Well-Architected practices. Infrastructure is auditable, secure and scalable. Workloads scale automatically based on demand. Cost exposure from AI loops and usage spikes is detected through CloudWatch and DoiT Cloud Intelligence. “DoiT helped us move from reactive to proactive,” Green said. “When an AI function looped unexpectedly and usage spiked, their Cloud Intelligence surfaced it instantly. We fixed the issue before it became a real problem.”
DoiT enabled DaySmart to build better going forward. “Instead of us hiring a whole AI innovation unit, DoiT became that function for us,” Green said. “They lift you up and shorten the path from idea to execution.”
Zero-engineering-lift execution for proof of concept
A key achievement of the accelerator program was enabling DaySmart to launch a functional AI experience without diverting internal engineering resources. DoiT engineered, deployed, and stabilized the initial production system while DaySmart’s own engineers continued executing their existing roadmap. “We shipped an AI capability in ninety days with zero engineering lift,” Green said. “Without the Cloud Accelerator, that would’ve required four to six engineers for a full quarter.”
The Results
Faster time-to-value and accelerated AI delivery
DaySmart moved from ideation to shipping an AI-powered capability in ninety days, a timeline made possible by DoiT’s Gen AI Cloud Accelerator.. The internal lift was effectively zero, preserving engineering focus while enabling a foundational intelligence layer to go live. “That acceleration is hard to overstate,” Green said. “We didn’t slow down our roadmap, we expanded it.”
Unified cloud visibility and spend governance across acquired product lines
Centralizing cloud oversight was a parallel victory. Cloud Intelligence unified disparate environments and allowed leadership to see trends, anomalies and efficiency opportunities across business units. “For the first time, we could look across the whole portfolio and make platform decisions instead of siloed ones,” Green said. “That has changed how we plan, how we scale and how we budget.”
Operational efficiency without compromising innovation
The engagement reduced senior leadership time spent managing infrastructure. Instead, leaders could evaluate strategy and focus on AI programs with confidence that cloud efficiency and security were under control. “Every hour we’re not fixing infrastructure is an hour we invest into product acceleration,” Green said. “That’s ROI most dashboards don’t measure, but it’s the most real.”
Future-proof AI platform for vertical intelligence
DaySmart’s new architecture goes further than powering a single AI feature. It establishes a future-proof foundation for vertical intelligence across all of its product lines, enabling context-aware insight generation tuned to each industry’s operating reality. With this platform, customers gain self-serve analytics, conversational interfaces to explore their data, and flexible reporting that can run on demand or on schedule, backed by a scalable LLM execution pipeline designed for growth. As Tim Green put it, “We didn’t build a single feature. We built a framework for dozens.”
Cultural shift toward governed experimentation
DoiT encourages safe innovation. Policies, guardrails and automation ensured AI rollouts would not risk stability or cost. “That balance, move fast and stay responsible, is why DoiT works so well for us,” Green said.
What's Next?
DaySmart plans to expand conversational AI capabilities, embed proactive business coaching into the platform, accelerate international deployments and democratize AI internally so non-technical teams can build on the data platform.
“We’re now building AI inside the business, not alongside it,” Green said. “The next stage is scaling intelligence across every product, every region and every team.”
The company expects to extend its proof-of-concept into a widely deployed customer-facing intelligence layer and continue partnering with DoiT for cloud guidance, FinOps maturity and model orchestration.