Case studies — decisions, constraints, results

Pretty logos don’t teach you how we work. These write-ups cover constraints, the bets we made, and what moved.

Built for: For buyers evaluating Jythu who want substance before a sales call.

Works best when: You value decision-quality evidence and understand that some client names remain confidential under NDA.

Long-form cases across SaaS, commerce, mobile, AI

Jythu product engineering case study interface
Work log

Constraints → bets → outcomes

What changes when this lands

Problem framed honestly

What was broken — product, growth, or ops.

Constraints listed

Time, compliance, politics, legacy.

Decisions explained

Why this stack / cut / sequence.

Results without theater

Metrics we can stand behind; ranges when NDA binds.

Roles named. Steps clear.

01

Read a case near your problem

SaaS, Shopify, MVP, AI, redesign.

02

Note constraints

Where you’re similar / different.

03

Bring that to the call

We scope faster with context.

04

Optional deep dive

Screen share under NDA.

05

Proposal

Phases and owners.

06

Kickoff

Same cadence as the cases describe.

One constrained engagement

How to read these

Context

Most client brands stay masked. Patterns transfer.

Constraint

We won’t fake Fortune-500 logos for vanity.

Decisions

Publish process + directional metrics; share specifics privately.

  • Faster qualified sales calls
  • Fewer mismatched briefs

SaaS analytics platform rebuild

Problem: Fragile legacy stack, spreadsheet billing, dashboards nobody trusted.

Constraint: Board demo in four months; couldn’t spike churn during cutover.

Decision: Remix app shell, design system, Stripe self-serve, live usage metrics.

  • Revenue and renewal baselines defined before the rebuild
  • Failed-renewal causes surfaced for the product team
  • Core-route performance monitored after launch

Shopify theme + SEO program

Problem: App-heavy theme eating Core Web Vitals; soft organic growth.

Constraint: Peak season — no long outage window.

Decision: Liquid rebuild with asset discipline; commercial content clusters.

  • Load performance measured before and after the theme work
  • Organic performance tracked across the programme window

React Native MVP to dual stores

Problem: Need iOS+Android narrative with AI assist for a demo day.

Constraint: Eight-week hard stop.

Decision: Aggressive cut list; RN; invest in crash hygiene and listing.

  • Both store release paths rehearsed and documented
  • Early feedback and stability data ready for the next release

In-product LLM assistant

Problem: Docs sprawl; tier-1 tickets scaling linearly with seats.

Constraint: Must cite sources; hard weekly model cost cap.

Decision: RAG over CMS + DB; tool calls with confirmation on account actions.

  • Deflected how-to volume
  • Predictable cost per 1k queries

B2B marketing site redesign

Problem: Dated visual system; blog still ranked.

Constraint: Couldn’t torch organic for a vanity launch.

Decision: Template redesign, programmatic content migrate, redirect workbook.

  • Stable non-brand organic post-cutover
  • Higher contact CTR on hubs

Dedicated React pod (staff-aug)

Problem: US SaaS FE bandwidth maxed; release train slipping.

Constraint: SOC2 vendor review; PT morning overlap.

Decision: Named FE/BE + shared PM; trial sprint then quarterly renewals.

  • Cleared blocked releases in month one
  • Pod retained across quarters

What you actually get

  • →Public case narratives (below)
  • →Private deeper reviews under NDA when needed

Objection killers

Why so many NDAs?

B2B buyers often require them. Ask us what’s shareable for your category.

Can we speak to a reference?

When a client agrees — we request introductions after mutual fit.

Do you show code?

Under NDA in diligence, for serious engagements.

Ready to talk scope?

Bring constraints. We’ll bring a timeline and a cut list. Free discovery call — response within 24 hours.

Also exploring? Case studies · Retainers