Your AI prototype works.
Now make it real.
WR Dev Labs helps founders, agencies, clinical research teams, and healthcare-adjacent product teams turn AI-built prototypes and early MVPs into secure, maintainable, production-minded applications.
Hands-on full-stack engineering leadership, AI integration, cloud architecture, and regulated-workflow experience for teams moving beyond the demo stage.
- Repo structureNeeds work
- Auth & permissionsRisk
- Data modelNeeds work
- Secrets & env varsRisk
- Deployment pipelineNeeds work
- Logging & errorsRisk
- Tests & docsRisk
- Production readinessNeeds work
AI made prototypes easy. Production is still hard.
AI tools can create impressive demos quickly. But before a prototype touches real users, customer data, clinical research workflows, sensitive health information, payments, or investor diligence, teams need engineering discipline: architecture, security, auth and access control, data handling, deployment, observability, documentation, maintainability, roadmap clarity, and auditability where it matters.
- One-off auth wired in for the screenshot
- Secrets pasted into client code
- Local DB, single environment
- No tests, no logging, no docs
- “Works on my machine” deploys
- Refactor / rebuild / extend? Unclear
- Real auth, roles, and access control
- Secrets managed and environment-scoped
- Deployable pipeline with multiple environments
- Logging, error handling, baseline tests
- Architecture notes + setup + runbooks
- Clear roadmap: what’s shipped, what’s next
AI-generated code can hide structural issues that only show up under real users or maintenance.
Demo-grade auth and rough data flows are common failure points the moment users sign up.
Working locally isn’t the same as deployed, observable, and ownable by another engineer.
An outside read on the foundation tells you which path actually saves time and money.
Diligence, pilots, and real conversations require evidence of an engineering plan.
Privacy, auditability, documentation, and AI safety can’t be retrofitted after launch without pain.
We turn promising prototypes into real software paths.
Bring us what you have — Lovable, Bolt, Replit, v0, Cursor, freelancer code, an internal hackathon — and we’ll meet it where it is.
Review your AI-built app, MVP, internal tool, or early codebase. Identify what’s solid, what’s risky, and what needs to change before launch.
Map the architecture, deployment path, security gaps, data model, and a refactor / rebuild / extend recommendation with a roadmap.
Refactor, harden, document, deploy, and stabilize the application so it can move toward real users or a production buildout.
When the prototype proves the idea but the foundation isn’t right, rebuild on production-grade architecture — or build a new system from scratch.
Optional ongoing hosting, monitoring, maintenance, and feature iteration after the sprint — with a senior engineer in the loop.
Built something fast and need to know if it can become real?
If you have a working prototype and a real next step — pilots, investors, customers, internal rollout — you’re in the right place.
- Founders who built MVPs with Lovable, Bolt, Replit, Cursor, v0, Base44, Claude Code, or similar tools
- Agencies that need a senior technical partner after the demo stage
- Clinical research vendors with workflow prototypes or internal tools
- Healthcare-adjacent startups exploring AI workflows
- Wellness or patient-engagement teams with sensitive-data concerns
- Product teams with internal hackathon prototypes
- Operators with workflow tools built in low-code or AI tools
- Executives or investors evaluating whether a prototype is technically viable
From promising demo to production path.
We inspect the prototype, repo, architecture, auth, data model, integrations, deployment, and risks.
We recommend whether to refactor, rebuild, extend, or preserve the current foundation — and why.
We harden the application: structure, deployment, documentation, security, reliability, and maintainability.
We hand off a documented production path or stay on as the buildout, hosting, and engineering partner.
Pick the entry point that fits where you are.
Starting ranges, not fixed quotes. Final scope is set after a short call so we’re quoting against the actual work.
- Codebase review
- Architecture assessment
- Auth & security review
- Data model review
- Deployment-readiness review
- Maintainability concerns
- Risk & assumption list
- Recommendation: refactor, rebuild, extend, or pause
- Production-readiness scorecard
- Target architecture
- Deployment plan
- Technical roadmap
- Backlog for next-phase build
- Security & data-flow notes
- Integration considerations
- Documentation needs
- Cost & timeline estimate
- Targeted refactoring or rebuild work
- Cleaner repo structure
- Environment & config cleanup
- Deployment pipeline setup or recommendations
- Documentation & handoff package
- Logging & error-handling improvements
- Production-readiness improvements
- Roadmap for continued buildout
A senior engineering partner after the sprint — feature iteration, hosting and monitoring, technical leadership, architecture support, production maintenance, and AI/product workflow refinement.
Custom scope or monthly retainer.
Ranges are starting points and reflect typical scope. Final pricing depends on codebase complexity, integrations, and production-readiness goals discussed during scoping.
Extra care for clinical research, healthcare-adjacent, and AI products.
For clinical research, healthcare-adjacent, wellness, AI, and sensitive-data products, the risk is not just whether the prototype “works.” The question is whether the architecture, data flow, access model, audit trail, documentation, and deployment approach can support the next stage.
We bring HIPAA-aware technical patterns, compliance-conscious architecture, and GCP-aware documentation and workflow thinking — the engineering side of regulated-workflow software.
WR Dev Labs does not provide legal, regulatory, or medical advice. We help teams identify technical, architectural, documentation, and production-readiness gaps that may need review by compliance, legal, security, or domain specialists.
- PHI or sensitive-data exposure points
- Audit logging needs
- Authentication & role-based access control
- Data retention & storage assumptions
- Workflow boundaries
- AI safety boundaries
- Vendor & hosting assumptions
- Documentation & handoff needs
- Regulated SDLC considerations
- Clinical research / GCP-aware workflow expectations
- Integration risks & unknowns
- Compliance-conscious architecture patterns
If a product requires FHIR, EHR, lab, payer, or third-party system integration, we help identify those requirements early, clarify the integration path, and scope whether the work should be implemented directly or supported by a specialist partner. Integration requirements vary by vendor and scope.
AI tools are great for proving ideas.
We help with the next step.
Examples are illustrative, non-diagnostic, and product/business oriented.
A clinical research vendor has an AI workflow prototype and needs a production-readiness review before piloting internally.
Founder built an MVP in an AI tool and needs to know whether to refactor, rebuild, or extend before talking to a partner.
Agency has a client-facing prototype that needs senior engineering review before handoff or buildout.
Product team built an internal operations tool over a sprint and needs deployment guidance to put it in front of staff.
Wellness company has an intake or education workflow demo and needs safer data-flow and AI-safety boundaries.
Startup has a working demo but needs architecture, documentation, and a production estimate to share with diligence.
Working prototype with vendor primitives — needs a more durable backend and deployment plan.
Hands-on engineering leadership for the post-prototype stage.
You get hands-on engineering leadership — paired with a small, disciplined team that uses modern AI-assisted development with judgment, not as a substitute for it.
A production-minded technical partner — not a freelancer, not a 50-person agency. You work with the people doing the work.
React, Next.js, Node, TypeScript, AWS, AI-assisted development. We move fast with AI while knowing exactly where its output gets risky.
Real production patterns for LLM features: prompt design, evaluation, guardrails, retrieval, and cost-aware architecture.
Clinical research software context, GCP-aware quality and documentation thinking, and HIPAA-aware technical patterns for healthcare-adjacent products.
Equally comfortable in a founder’s living room, a product team standup, an executive review, and an engineering deep-dive.
Architecture notes, runbooks, environment docs, and roadmaps written so your next engineer can actually own the system.
Have an AI-built prototype you’re not sure you can trust?
Book a short productionization call. We’ll talk through what you built, what you need it to do next, and whether an audit, blueprint, sprint, or rebuild makes sense.