We identify, prioritise, and systematically eliminate technical debt — code quality analysis, architecture refactoring, dependency modernisation, and test coverage remediation — without halting your feature roadmap.
Technical Debt Services and Tooling — SonarQube, Snyk, Dependabot, Jest, Playwright, CodeClimate, ESLint, GitHub Actions
Technical debt is not a code quality problem — it is a business velocity problem. Every shortcut taken during hypergrowth, every test deferred under deadline pressure, every dependency left unupgraded for fear of breaking changes compounds over time into a tax on every new feature. Teams in high-debt codebases spend 20–40% of their engineering capacity servicing the debt rather than building new product. The codebase slows from velocity asset to velocity liability, and the cost of each feature shipped increases year over year.
Perimattic quantifies technical debt before touching a single line of code. We run static analysis tooling — SonarQube, CodeClimate, ESLint, Snyk — across the full codebase and cross-reference complexity metrics with git change frequency to identify hotspots: the modules that are both complex and frequently changed, where debt carries the highest daily servicing cost. The output is a debt scoring matrix that ranks every module by remediation effort and business impact, giving you a prioritised list grounded in data rather than engineering intuition.
Our remediation approach is parallel-track, not big-bang. We do not ask for a feature freeze — we integrate remediation work alongside active feature development using a dedicated remediation stream that targets the modules adjacent to current product work. Quality gates are introduced progressively into the CI/CD pipeline, enforcing new standards on new code while the team systematically cleans up the existing codebase. By the end of the engagement, teams see measurable velocity improvements, a reduced bug introduction rate, and an automated governance framework that prevents the debt from re-accumulating.
Development velocity
20–40% of engineering capacity consumed by rework, defect resolution, and working around legacy constraints
Development velocity
Measurable velocity uplift as high-complexity hotspots are systematically remediated sprint by sprint
Bug introduction rate
High change failure rate from untested modules and tightly coupled code with large blast radius
Bug introduction rate
Reduced bug introduction from quality gates, elevated test coverage, and decoupled architecture
Onboarding time
Weeks of ramp-up for new engineers navigating undocumented, complex, and inconsistent codebases
Onboarding time
Accelerated onboarding through documented APIs, clean module boundaries, and consistent code standards
Security exposure
End-of-life dependencies and unpatched CVEs left unresolved due to upgrade risk in brittle codebases
Security exposure
Automated dependency management with Dependabot and Snyk keeping the vulnerability surface current
Deployment confidence
Infrequent deployments with long manual verification cycles due to lack of automated test coverage
Deployment confidence
Continuous delivery enabled by quality gates, automated test suites, and decoupled service boundaries
The cost of unmanaged technical debt is not paid in a single event — it is paid incrementally, every sprint, as engineering teams work harder to ship less.
Seven specialist service lines covering every layer of your technical debt reduction programme.
Static analysis across your full codebase combined with git hotspot analysis to produce a debt score and prioritised remediation matrix. You know exactly where the debt is and what it costs before remediation begins.
Systematic decomposition of tightly coupled modules, god classes, and monolithic service boundaries — restoring independent deployability and reducing the blast radius of every change.
End-of-life library upgrades, CVE remediation, and licence compliance review across your full dependency tree. Automated dependency management configured to prevent future accumulation.
Unit, integration, and end-to-end test coverage raised to agreed quality-gate thresholds across all critical modules — protecting future feature delivery from undetected regressions.
Identification and safe removal of unreachable code paths, unused feature flags, and orphaned dependencies — reducing cognitive load on the engineering team and improving build times.
Modern build tooling, automated quality gates, and continuous integration pipelines configured to enforce code quality standards and prevent debt re-accumulation from day one.
Internal API documentation, OpenAPI specifications, and system behaviour documentation produced for undocumented codebases — reducing onboarding time and integration errors.
A structured six-stage process from free debt audit to systematic remediation and ongoing governance — without stopping feature delivery.
We run static analysis tooling and cross-reference complexity scores with git change frequency to identify the highest-cost debt hotspots across your codebase.
We produce a debt scoring matrix ranking every module by remediation effort and business impact — a data-driven prioritisation list before any code is changed.
We design a parallel-track remediation roadmap that integrates alongside the existing feature delivery schedule with no feature freeze required.
We execute the roadmap module by module: architecture decoupling, dead code removal, dependency upgrades, and complexity reduction — all with full test coverage.
We implement automated quality gates in the CI/CD pipeline and raise test coverage to agreed thresholds across all remediated modules.
We measure delivery velocity against pre-remediation baselines and hand over a governance framework that prevents debt re-accumulation after the engagement.
We run static analysis tooling and cross-reference complexity scores with git change frequency to identify the highest-cost debt hotspots across your codebase.
We produce a debt scoring matrix ranking every module by remediation effort and business impact — a data-driven prioritisation list before any code is changed.
We design a parallel-track remediation roadmap that integrates alongside the existing feature delivery schedule with no feature freeze required.
We execute the roadmap module by module: architecture decoupling, dead code removal, dependency upgrades, and complexity reduction — all with full test coverage.
We implement automated quality gates in the CI/CD pipeline and raise test coverage to agreed thresholds across all remediated modules.
We measure delivery velocity against pre-remediation baselines and hand over a governance framework that prevents debt re-accumulation after the engagement.
Select an industry to see how we identify, prioritise, and eliminate technical debt to restore development velocity without halting feature delivery.
SaaS companies accumulate technical debt fastest during hypergrowth phases — when shipping speed outpaces code quality. Debt in a SaaS product manifests as slowing release cycles, rising on-call burden, and increasingly complex onboarding for new engineers. Perimattic helps SaaS teams quantify, prioritise, and systematically eliminate debt without halting the feature roadmap.
SaaS companies accumulate technical debt fastest during hypergrowth phases — when shipping speed outpaces code quality. Debt in a SaaS product manifests as slowing release cycles, rising on-call burden, and increasingly complex onboarding for new engineers. Perimattic helps SaaS teams quantify, prioritise, and systematically eliminate debt without halting the feature roadmap.
Financial services firms carry some of the heaviest technical debt burdens in any industry — legacy core banking systems, proprietary integration middleware, and multi-decade-old codebases that were never refactored. Debt in regulated environments carries additional risk: outdated dependencies introduce security vulnerabilities that directly threaten compliance posture under PCI-DSS, SOX, and FCA regulations.
Healthcare software carries patient safety obligations that make technical debt uniquely dangerous — a bug introduced by poorly understood legacy code in a clinical system is not just a quality problem, it is a risk management problem. Perimattic approaches healthcare debt remediation with full audit trail documentation and validated testing at every stage to meet HIPAA and FDA software validation requirements.
E-commerce platforms accumulate debt during seasonal scaling events — quick fixes shipped before Black Friday that become permanent. The debt is particularly costly because it directly impacts conversion: slow page loads from unoptimised code, checkout bugs from untested edge cases, and deployment failures from fragile build pipelines all translate into lost revenue. Perimattic prioritises e-commerce debt remediation by business impact.
Logistics platforms integrate with hundreds of carriers, warehouses, and ERP systems — and those integrations accumulate debt faster than the core platform. Undocumented API contracts, point-to-point integrations with no error handling, and legacy batch processing systems create operational fragility that shows up as missed SLAs and manual reconciliation overhead. Perimattic focuses logistics debt remediation on the integration layer.
Media platforms carry debt in their content delivery and publishing tooling — CMS platforms that have been heavily customised, video processing pipelines held together with bespoke scripts, and frontend codebases where every page template has diverged. The debt slows editorial workflow, increases publishing errors, and makes it expensive to launch new content formats or regions.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate a successful debt remediation engagement from a rewrite that never ships.
We produce a debt score and hotspot analysis before recommending any changes. Remediation is prioritised by business impact, not by what is easiest to fix — so every sprint delivers measurable velocity improvement.
A feature freeze is not a prerequisite for debt reduction. We use a parallel-track approach that integrates remediation alongside active feature development, so the product team never stops shipping.
We configure automated quality gates in your CI/CD pipeline so new code cannot introduce complexity, coverage gaps, or vulnerable dependencies below the agreed thresholds — making clean code the default.
The engagement ends with a knowledge transfer session and governance framework your team owns. We document the rationale for every quality gate so the team can maintain and evolve the standards independently.
“Technical debt does not announce itself as a crisis — it arrives as a gradual slowdown until the team is spending more time managing the past than building the future.”
Technical debt is the accumulated cost of shortcuts, suboptimal design decisions, and deferred maintenance in a software codebase. Every time a team ships code that is correct but not clean — duplicated logic, missing tests, an outdated dependency, tightly coupled modules — they take on a small amount of debt. Like financial debt, technical debt compounds: it becomes harder and more expensive to work with over time. Teams that do not actively manage it will eventually find that the majority of their engineering capacity is spent servicing the debt — fixing regressions, understanding undocumented code, working around architectural constraints — rather than building new features.
We use a combination of static analysis tooling and manual engineering review to produce a debt score. Static analysis tools — SonarQube, CodeClimate, ESLint, Pylint — measure cyclomatic complexity, code duplication, test coverage gaps, and dependency vulnerability counts. We overlay these measurements with a hotspot analysis that cross-references high-complexity modules with change frequency from the git history: code that is both complex and frequently changed carries the highest debt servicing cost. The output is a debt scoring matrix that ranks every module by estimated remediation effort and business impact, giving the team a prioritised list before a single line of code is changed.
Not all technical debt should be eliminated — some of it was incurred deliberately and represents an acceptable trade-off. The decision framework we use is: if the debt is in a module that is frequently changed and is causing demonstrable slowdowns (high bug introduction rate, long review cycles, slow build times), it should be remediated. If the debt is in a stable module that is rarely touched and poses no security risk, it may be deferred indefinitely. The goal is not a perfect codebase — it is a codebase where the highest-traffic, highest-change areas are clean, well-tested, and easy to reason about.
Research from McKinsey and academic studies of large codebases consistently shows that teams in high-debt codebases spend 20–40% of their engineering capacity on rework, defect resolution, and working around architectural constraints rather than building new features. The compounding effect is significant: a codebase that starts at 20% overhead will reach 40% overhead within two to three years if debt is not actively managed. Beyond raw velocity, technical debt increases onboarding time for new engineers, raises on-call burden, and increases the blast radius of every deployment — all of which raise the real cost of each feature shipped.
The timeline depends on the size and age of the codebase and the depth of debt. A focused remediation engagement covering the highest-priority modules typically runs eight to sixteen weeks. A comprehensive programme covering the full codebase — including test coverage remediation, dependency upgrade, dead code removal, and architecture decoupling — typically runs sixteen to twenty-four weeks. We sequence the work to deliver measurable velocity improvements early in the engagement, so teams see concrete outcomes before the full programme is complete.
Yes — this is a core principle of how we work. A feature freeze is not a prerequisite for debt remediation. We integrate remediation work alongside the existing feature roadmap using a parallel-track approach: a dedicated remediation stream works on debt reduction while the product team continues feature development. We prioritise remediation in the modules adjacent to active feature work, so the product team immediately benefits from cleaner code in the areas they are working in. Quality gates are introduced progressively, so teams are not blocked by new requirements applied retroactively to existing code.
Our toolset depends on the technology stack, but typically includes: SonarQube or SonarCloud for static code analysis and quality gate configuration; CodeClimate for maintainability trends over time; ESLint and Pylint for language-specific linting and complexity analysis; Dependabot, Renovate, and Snyk for dependency vulnerability scanning; OWASP Dependency-Check for open-source licence and CVE analysis; Jest, Playwright, Cypress, JUnit 5, and PyTest for test coverage measurement; and Datadog or Prometheus for production observability metrics that complement the static analysis picture.
We establish baseline metrics before starting remediation and track them throughout the engagement. The primary metrics are: change failure rate (bugs introduced per deployment), lead time for changes (time from code commit to production), mean time to recovery from production incidents, test coverage percentage in critical modules, dependency vulnerability count, and cyclomatic complexity of high-traffic modules. We produce a velocity measurement report at the end of each sprint that shows progress against these baselines, so teams have concrete evidence of the value delivered rather than relying on subjective assessment.
Remediation without governance is temporary. We implement quality gates in the CI/CD pipeline — automated checks that fail the build if new code violates the agreed quality thresholds for complexity, test coverage, or dependency health. We configure SonarQube quality profiles tuned to the team's specific codebase, establish a debt tracking process that makes new debt visible before it accumulates silently, and deliver an engineering governance framework that the team owns after the engagement. We also run a post-remediation knowledge transfer session so the team understands why each quality gate exists and how to maintain it.
The engagement starts with a free discovery call where you tell us about your codebase — its age, technology stack, team size, and the specific problems you are experiencing. If there is a clear fit, we proceed to a paid Technical Debt Audit and Hotspot Analysis, typically delivered within two weeks, that produces the debt scoring matrix and a prioritised remediation roadmap. The audit gives you a complete picture of your debt before committing to a full remediation engagement, so you can make an informed decision about scope, timeline, and investment.
Tell us about your codebase — its age, team size, and the quality problems slowing delivery — and we will produce a debt audit and remediation roadmap in a free discovery call.