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The project existed because A single eyewear order can involve 15–20 configurable lens properties, plan-specific coverage, copay/deductible interactions, compatibility constraints, and reimbursement sequencing. Carriers use inconsistent terminology and logic. Before the platform, the workflow was manual and depended on senior opticians with years of institutional knowledge; error rates ran ~35%, about half of which led to office-funded remakes. The constraint was building deterministic pricing on top of genuinely non-deterministic source data. The source summary is: Built, solo, a multi-tenant SaaS that turns messy optical insurance and reimbursement logic into a rules engine opticians can follow. The real problem was domain complexity, not code: insurance carriers describe equivalent benefits differently, and even plans within one carrier apply different rules, so the work was modeling that inconsistency into something deterministic — while learning the domain in parallel. The role was: Sole architect, lead developer, database and rules-engine designer, product owner, and infrastructure engineer. Worked with an experienced office manager for domain input and a designer for UI. Learned the optical reimbursement domain while building the platform from first principles. The work sat squarely inside the existing business, so the goal was never to add complexity for its own sake.
Operating flow
- Map the current system and the constraint first.
- Choose the smallest change that can hold the load.
- Build against the real workflow instead of a toy case.
- Roll it out with enough monitoring to catch the edge cases.
This series follows the build in the order it happened: discovery, the solution direction, the implementation steps, and the operational result. Each post stays on one decision or one build step so the reader can see how the system moved from the initial constraint to a working result.
The details come from the project files and the company context, not from a generic template. That keeps the story grounded in the mechanics of the work: what was built, what it replaced, and what changed when it shipped.
The implementation stayed close to PHP/CodeIgniter, PostgreSQL, Nginx, Python (OCR) because the new system still had to live inside the same operating environment as the old one. That kept the work from drifting into a clean-room exercise that would look better on paper than it would in production. The practical question was always whether the implementation could hold up under the real workflow and the real users. If it could not do that, it was not finished.
The constraint behind the step was that A single eyewear order can involve 15–20 configurable lens properties, plan-specific coverage, copay/deductible interactions, compatibility constraints, and reimbursement sequencing. That is why the work had to trade one kind of cost for another instead of trying to eliminate cost altogether. In almost every case, the useful move was to spend a little more effort on clarity, validation, or control so the business would spend less effort on repeated manual work later. That is the pattern the project files keep pointing to.
The role in the work was Sole architect, lead developer, database and rules-engine designer, product owner, and infrastructure engineer. That meant the implementation could not stop at the code boundary because the operating model, handoff, and support path were part of the outcome. The relevant outcome was Cut optician onboarding from years of experiential learning to ~2 weeks. The build only earns its place if the new result is visible in the way the business works after launch.
The specific step in this article was Built, solo, a multi-tenant SaaS that turns messy optical insurance and reimbursement logic into a rules engine opticians can follow. That is the piece that moves the story from analysis into execution. It is also the part that shows the difference between a conceptual fix and a system people can actually use. That distinction matters more than style or novelty.
The point is to show how the system works, not to turn the project into a slogan or a summary stub.
When the architecture changes, the real question is what the new system allows the business to do that the old one could not. That shows up here in throughput, reliability, operating cost, turnaround time, and how much manual work disappears once the workflow is redesigned.