Joser Jewellery: Migration from Tilda to 1C-Bitrix, Catalog, Cart, and Pixel-Perfect Layout
Industry
Jewelry e-commerce
Period
2025-2026
Role
Team formation and full-cycle delivery
Tech stack
1C-Bitrix, PHP, MySQL, Figma-to-code
Problem
Challenge: maintain visual quality while enhancing the manageability of e-commerce functions.
When I joined "Joser Jewellery: Migration from Tilda to 1C-Bitrix, Catalog, Cart, and Pixel-Perfect Layout", the pattern was familiar: local fixes existed, but there was no shared model connecting business goals to technical execution. That gap kept incidents recurring and manual overhead growing.
I decomposed the issue into controllable layers: input signals, decision rules, handoff points and post-release quality control. This immediately clarified where performance was being lost and why previous fixes did not hold.
Approach and solution
Assembled a role-based team, developed a phased migration plan, and delivered functionality on schedule.
Instead of patching symptoms, I implemented a phased model: acceptance criteria first, minimum viable core second, and scale expansion only after stability was proven. This created measurable progress at each stage.
Operational governance was part of the implementation itself: ownership boundaries, deviation handling and explicit escalation logic. That made the outcome repeatable rather than person-dependent.
Architecture
1C-Bitrix component architecture, modular cart, templates for e-commerce scenarios, process-driven QA.
Architecturally, the key principle was "observability before complexity". It allowed the team to see real impact of each change and keep control while scaling.
The stack (1C-Bitrix, PHP, MySQL, Figma-to-code) was treated as an enabler, not a goal: every decision was evaluated by impact on delivery speed, stability and support cost.
Outcome
The project established a stable foundation for assortment growth and marketing activities.
Business impact was not limited to isolated metric gains. The team received a practical operating model with clearer priorities, faster decisions and lower regression risk.
I documented outcomes in a before/after format tied to practical KPIs, so leadership could directly map engineering work to commercial value.
Metrics
- Migration from Tilda to 1C-Bitrix.
- Launch of catalog and checkout.
- Exact adherence to Figma designs.
- Team response speed to deviations and incidents.
- Manual overhead share before vs after rollout.
- Stability of critical user flow under load.
- Release predictability and regression frequency.
- Input quality: less noise, higher useful outcome.
Deliverables
- Team and responsibility matrix.
- Catalog architecture.
- Cart and order module.
- Target architecture map with implementation priorities.
- Phased rollout plan with acceptance criteria.
- Operational runbook and escalation model.
- Post-release quality checklists.
- 30/60-day optimization backlog.
Unique solution in this case
In this case, the differentiator was component-driven Bitrix domain model, AI workflow with safe rollout and quality validation. The delivery was not a one-off patch: architecture constraints were fixed first, then a production workflow was rolled out so the team can scale without losing control.
Comparison: before vs after systems rollout
| Aspect | Before | After |
|---|---|---|
| Delivery model | Local fixes without unified architecture | Systems-first rollout with clear architecture logic |
| Operational control | Manual and context-dependent execution | Transparent rules, checklists and quality control |
| Business impact | Required migration from a site builder to a managed platform without compromising interface quality. | Team assembled and migration completed: catalog, cart, checkout, interactive features, and pixel-perfect layout. |
How-to: how to replicate this result in your project
- Define business objective and success metric before implementation.
- Map current flow and identify losses in data, time and quality.
- Scope minimum viable rollout with explicit acceptance criteria.
- Launch phased rollout with observability and trace logging.
- Lock support, escalation and iteration workflow.
Practical implementation checklist
- Baseline metrics captured before rollout.
- Integration points and data contracts verified.
- Failure modes and fallback scenarios tested.
- Post-launch quality controls enabled.
- Operational runbook prepared for the team.
- 30/60-day optimization plan documented.
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