Improved platform reliability and delivery control
A B2B SaaS startup outgrew its original monolith as customer numbers climbed. We defined a cloud-native architecture roadmap, introduced CI/CD discipline and helped the team cut deployment cycles from two weeks to two days while improving platform reliability.
Project Overview
Industry
SaaS
Duration
22 weeks
Team Size
4 developers + architect
Client context
The company provides workflow software to mid-market operations teams. After seed funding they grew from 200 to 1,400 active accounts in under a year. The founding engineering team of six was shipping features quickly, but releases were manual, staging environments were unreliable and on-call incidents were increasing. Investors wanted evidence of scalable engineering practices before the next raise.
The Challenge
Technical debt had accumulated in a single deployable codebase with tightly coupled modules. Database migrations were risky, rollbacks were rare and there was no shared view of architecture direction. Feature work competed with firefighting. Leadership needed a pragmatic plan — not a full rewrite — that would stabilise delivery and clarify what to build versus what to refactor.
Our Solution
We produced an architecture roadmap that separated critical services incrementally, introduced automated testing gates in CI/CD and established lightweight delivery governance. Platform improvements were prioritised alongside revenue features so reliability work was visible in the roadmap, not hidden as side quests.
Our approach
- 1
Reviewed repository structure, deployment history and incident logs to identify the top three reliability and velocity bottlenecks.
- 2
Facilitated architecture workshops with engineering and product to agree boundaries for authentication, billing and core workflow domains.
- 3
Introduced trunk-based development, automated test pipelines and staged deployments with rollback procedures.
- 4
Extracted the highest-churn integration into a standalone service as a proof-of-concept for incremental decomposition.
- 5
Defined SLIs for API latency and error rates, with a simple on-call runbook and post-incident review template.
- 6
Coached the CTO on quarterly technical planning so platform work stayed funded alongside customer-facing delivery.
Platform reliability
Improved platform reliability and delivery control
Infrastructure cost vs baseline
Deployment cycle
Results achieved
- Reduced deployment cycle time from 14 days to 2 days with automated pipelines and clearer release criteria
- Lowered infrastructure spend by 35% through right-sizing, reserved capacity and removal of unused environments
- Improved platform reliability with fewer customer-impacting incidents during peak usage windows
- Gave leadership a 12-month technical roadmap linked to hiring and funding conversations
Technologies Used
Project Timeline
Architecture Design
Documented current-state architecture, agreed target boundaries and sequenced migration milestones with engineering leads.
Development
Built CI/CD pipelines, extracted the first service boundary and added monitoring dashboards for core APIs.
Migration & Launch
Migrated production traffic incrementally, validated performance under load and handed over runbooks to the internal team.
Key takeaways for SMEs
- Incremental decomposition beats a big-bang rewrite for most growth-stage SaaS teams.
- CI/CD and observability investments pay back quickly when release anxiety is slowing product delivery.
- Make platform work visible on the same roadmap as features — otherwise it never ships.
Related service
This case study reflects the kind of outcomes we pursue through our digital strategy & development work for UK SMEs.
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