Customer Story · Healthcare / Digital Health
From Cloud Foundations to AI-Enabled Care Operations
How Lumata Health is strengthening its cloud, data and product engineering foundation while scaling AI-enabled and agentic capabilities with Teqfocus and Claude.
About Lumata Health
Lumata Health is a healthcare technology company focused on supporting patients with chronic eye conditions between visits. Its model pairs data and technology with experienced Care Coaches, helping patients stay engaged with their care plans while reducing the administrative burden on eye-care providers.
Industry
Healthcare / Digital Health
Services
AI & Agentic Engineering · Cloud · Data · DevSecOps
Technology
Claude · AWS · Salesforce · Amazon QuickSight
The Challenge
Scaling a technology-enabled care model meant scaling the engineering behind it, all at once.
As Lumata expanded its technology-enabled care model, the engineering environment supporting that growth grew more complex along with it. Every new capability touched cloud infrastructure, data engineering, Salesforce, DevSecOps, analytics and application development at the same time, and each of those layers needed to move in step with the others.
Treating them as five separate technology initiatives would have meant coordinating investment across disconnected workstreams, with no shared foundation underneath any of them and no clear path to the AI-enabled and agentic capabilities Lumata wanted to build next.
Lumata needed an engineering model that connected those layers instead of isolating them: one built to move priorities from discovery to production with more speed and more consistency than a traditional multi-vendor approach allows.
The Approach
One connected engineering program, not five disconnected tracks.
Teqfocus partnered with Lumata through a multidisciplinary engineering model spanning cloud, data, Salesforce, DevSecOps, analytics and application development as a single program.
Instead of treating AI as a separate innovation track, the engagement was built to connect the layers an AI-enabled idea actually needs to reach dependable production use: the cloud architecture underneath it, the data feeding it, the integrations surrounding it, and the security and delivery discipline that keeps it reliable.
Claude in the Engineering Workflow
Claude went into production first with the data engineering team, ring-fenced while the approach proved itself. The performance gains it produced there made the case on their own: Teqfocus and Lumata extended Claude into a shared, day-to-day tool across the full engineering organization, full-stack engineers, BI engineers and Salesforce developers alike, for technical analysis, solution exploration, software development, debugging, documentation and test planning.
Forward Deployment Engineering
Teqfocus engineers work directly with Lumata’s product, customer-experience and engineering stakeholders. Requirements move quickly into design, implementation, validation and production-ready iteration through agile delivery cycles, spanning frontend, backend and integration work, including guided-care workflows and CRM modernization.
Building Toward an Agentic Platform
The next stage is not a list of isolated AI features. Teqfocus is helping Lumata connect product intent with the cloud, data, integration, security and engineering foundations required to operationalize AI-driven and increasingly agentic capabilities across workflows and teams.
What This Is Enabling
A foundation where cloud, data, CRM, product and AI evolve together.
14 → 6 days
Average ticket resolution cycle time
20%
Better-than-planned
delivery ROI
1 → 4 teams
Data Engineering only → Data, Full-Stack, BI & Salesforce Engineering all running on Claude in production
- Cutting ticket resolution time in half, and then some
Average cycle time for closing engineering tickets dropped from 14 days to 6. That freed the existing team to resolve more tickets with the same headcount and pull delivery ahead of the committed timeline, contributing to a 20% better-than-planned ROI on the engagement. - Faster iteration across product priorities
Embedded engineering teams translate Lumata’s stakeholder requirements and feedback into working software within sprint cycles, not quarterly release windows. - A stronger foundation for AI and agentic capabilities
Cloud, data, application and integration engineering are developed together, reducing the risk of AI becoming an isolated technology layer bolted onto the rest of the stack. - AI-assisted engineering, now running across the whole architecture
Claude started ring-fenced with the data engineering team. The performance held up, and it is now in daily production use across data engineering, full-stack engineering, BI engineering and Salesforce development, one consistent, AI-assisted workflow for analysis, development, troubleshooting and documentation instead of a pilot confined to a single team. - More reliable data and analytics
Ongoing investment in ingestion, transformation, pipelines and reporting strengthens the foundation for operational insight and the AI use cases still ahead. - Stronger engineering operations
DevSecOps, CI/CD, infrastructure-as-code, testing and security practices bring the discipline that faster, safer delivery depends on. - A scalable delivery model
Ongoing governance connects Lumata’s CTO office, product stakeholders and engineering teams around one shared roadmap and execution rhythm.
Why It Matters
For healthcare organizations, AI transformation isn’t a model-selection exercise. Useful AI capabilities depend on the quality of the data, applications, cloud architecture, integrations, security controls and engineering processes underneath them.
Lumata’s approach brings those elements forward together. By combining its healthcare expertise and product vision with Teqfocus’s cross-functional engineering capabilities, and building Claude into everyday engineering workflows, Lumata is building a foundation that supports today’s operational priorities while leaving room for more intelligent and agentic experiences over time.
Building Toward Healthcare AI That Scales
See what an engineering foundation built for AI can do for your organization.
Teqfocus helps healthcare technology companies connect AI, data, cloud, Salesforce and product engineering into one delivery model, from the first use case through production and scale.
