[{"content":"I’m Paul Littlebury, a quality engineer and tester with more than 25 years of experience helping teams ship better software through smarter testing, stronger automation and a more human approach to quality.\nI believe quality is not something to add at the end of delivery. It is a mindset, a culture and a way of working that improves reliability, delivery confidence and the experience of the people using a product.\nWhat I do # I work across:\nTest engineering for web, mobile, APIs and performance DevOps and CI/CD Accessibility and inclusive UX Quality strategy and coaching Conversational AI testing I build automation frameworks, embed testing into pipelines, improve reliability, coach teams and explore systems deeply to uncover issues before users encounter them.\nCurrent work # I currently work as a Conversational AI Quality Engineer for Acorn Compliance, helping to ensure that a compliance-focused chatbot is:\nReliable Context-aware Accurate Accessible Clear and enjoyable to use My work includes designing and running automation suites with Playwright and Mocha/Chai, monitoring API health, assessing NLP behaviour, and evaluating tone, inclusiveness and multi-turn logic.\nA particular interest is neurodivergent accessibility: creating experiences that are clear, calm and supportive for a wider range of users.\nHow I work # My approach blends hands-on engineering with collaborative problem-solving:\nBuilding robust, maintainable test frameworks Integrating testing into CI/CD pipelines Coaching teams to take shared ownership of quality Encouraging user-centred thinking Using exploratory testing to find high-value issues Treating accessibility as a first-class quality concern I enjoy bringing order to complex systems and helping teams feel more confident in their releases.\nExperience # I have worked across healthcare, fintech, telecoms, public services and high-traffic consumer products. Highlights include:\nAcorn Compliance — conversational AI quality and automation bet365 — rebuilding mobile UI and API test frameworks for Android and iOS CGI — SDET Lead across four Scrum teams working on a major Flutter banking app Ceta Insurance — test strategy, UI and API automation, load testing and CI pipelines Tele2 — quality engineering for a custom AI chatbot DocComs, KPMG, Wowcher, TNT and many more across web, mobile and enterprise systems Every project has strengthened my belief that quality engineering works best when it is collaborative, proactive and built into delivery from the start.\nAccessibility services # Alongside broader quality engineering, I provide focused accessibility services for websites and mobile apps:\nAccessibility audits for structured, prioritised findings and remediation guidance Accessibility support for practical help during design, development, testing and release What drives me # Ultimately, I am motivated by making things better:\nBetter user experiences Better engineering practices Better communication between teams Better confidence in every release Whether I am building frameworks, shaping strategy or asking the right questions, I enjoy helping teams deliver software that feels reliable, accessible and genuinely useful.\nContact # To talk about quality engineering, testing or accessibility, connect with me on LinkedIn or book a call below.\n","date":"28 May 2026","externalUrl":null,"permalink":"/about/","section":"Accessibility Specialist \u0026 Audit Services","summary":"Engineering better software through smarter testing, stronger automation and a human-first approach to quality.","title":"About me","type":"page"},{"content":"The Accessibility Systemic Analyzer is a multi-tool analysis and reporting layer for accessibility scan output. It is designed for the point where a team has more findings than it can triage comfortably page by page, and needs a way to understand what is repeated, what is systemic, what is corroborated across tools, and which fixes are most likely to remove broad classes of issues across a site or estate.\nInstead of treating accessibility results only as isolated page-level defects, the analyzer normalizes data from multiple engines, deduplicates likely overlaps, maps findings to WCAG where possible, groups repeated issue patterns, and produces both a dashboard and workbook that are easier to use for remediation planning.\nCurrent state: the analyzer now supports a wider adapter set, richer WCAG handling, friendlier rule labels, stronger workbook and dashboard alignment, static dashboard builds, and drilldown search / pagination.\nWhy this exists # Most accessibility tools report results per page and per node. That is useful for debugging, but hard to use for planning. A single underlying component problem can create hundreds of repeated findings across many pages.\nPage Finding Home Button contrast issue Product page Button contrast issue Cart Button contrast issue Checkout Button contrast issue Traditional reports treat these as separate rows.\nThe underlying issue may really be “the shared button implementation has a contrast problem”.\nThe analyzer exists to help move from raw issue volume to reusable remediation targets.\nWhat the analyzer does # Detects and loads supported report formats. Normalizes source-specific findings into a shared row model. Maps rules to WCAG criteria, levels, and titles where possible. Normalizes page keys, selectors, severity, rule ids, and display labels. Infers likely component and component-group ownership. Deduplicates likely matching findings across tools on the same page. Builds clusters and pattern rollups. Computes metrics for overlap, confidence, systemicity, page spread, and prioritization. Exports analyst-friendly spreadsheets and static dashboard assets. Current supported tool families and adapters # Tool / Source Current notes axe-core Supports confirmed violations and incomplete/manual-review style findings, including WCAG extraction from tags. axe-scan Handles axe-derived report output and incomplete/manual-review style contrast findings. IBM Accessibility Checker Handles both violation and potentialviolation output and maps IBM rule ids to WCAG where possible. Lighthouse Reads failed accessibility audits and extracts WCAG tags from audit debug metadata. Oobee Crawler-style source with broader estate/page coverage. UUV Flow/run-based source with hardened metadata handling, severity normalization, and conformance metadata support. Pa11y Now treated as a single pa11y source with per-issue runner detection for mixed axe + HTMLCS runs. HTMLCS / html-sniffer Supported through HTMLCS-aware parsing and WCAG technique-style label handling. Some adapters preserve “needs review” style outputs rather than only confirmed failures. This is intentional.\nCurrent WCAG handling # rule-to-WCAG mapping tables message / heuristic fallback mapping WCAG title and level enrichment cleanup of WCAG-only charts and exports so that only real criterion codes appear in WCAG-specific views friendlier rule labels so bare WCAG codes are not reused unnecessarily as human rule names In practice, the analyzer tries to separate:\nRule: a human-readable issue label Rule Id: source-specific technical identifier WCAG: criterion code such as 1.4.3 WCAG Name: criterion title such as Contrast (Minimum) Important recent model changes # Pa11y mixed-run support # Pa11y is no longer treated as two fake tools when axe and HTMLCS findings come from the same Pa11y run. The analyzer now models one pa11y source and keeps the per-issue runner such as axe or htmlcs.\nFriendlier rule labels # Rule formatting has been tightened so that the analyzer no longer prefers a bare WCAG code or raw HTMLCS identifier when a friendlier label can be derived from rule_name, ruleId, WCAG title metadata, or technique-aware enrichment such as 1.4.6 [G17].\nWCAG chart cleanup # WCAG-specific dashboard and export views now accept only real WCAG codes. This prevents rule ids, messages, or fallback text from leaking into WCAG-only charts.\nNeeds-review preservation # The analyzer now more intentionally preserves incomplete/manual-review style findings from sources like axe-core, IBM, and HTMLCS-derived reports.\nDrilldown UX upgrade # The dashboard drilldown now supports search, page-size selection, and pagination.\nHow the pipeline works # 1. Adapters # Source-specific adapters detect report shapes and emit normalized rows with fields such as page/url, rule id, message, severity, selector/dom, source, and any tool-specific metadata worth keeping.\n2. Report loading and enrichment # Rows are enriched with WCAG fields, page keys, rule display fields, and related metadata. This is where much of the rule/WCAG cleanup happens.\n3. Processing # The processing layer infers component, component group, design-system or custom origin, ownership hints, issue scope, and ranking-related fields.\n4. Deduplication # Likely matching findings on the same page are merged into deduplicated rows. This is where tool overlap becomes visible and where consensus / confidence become meaningful.\n5. Clustering and metrics # Repeated issue families are rolled up into clusters and pattern-level summaries. Metrics such as overlap, confidence, page concentration, shared source rate, and fix prioritization are computed here.\n6. Exports # The final output can be written to analyst-friendly workbook tabs, Power BI-friendly sheets, dimension/fact sheets, and static dashboard payload files.\nWhat “systemic” means in this project # A systemic issue is any finding pattern that appears repeated enough that fixing it once may help many pages or instances. In practice that can mean a design-system component issue, a repeated template issue, a repeated content structure issue, or a crawler/flow-discovered issue affecting many pages.\nThe analyzer uses several related but distinct concepts:\ndesign_system_issue: stronger hint of a reusable shared source issue_scope: shared component, shared template/pattern, or page-specific systemic: repeated enough to matter as a shared remediation target Dashboard and static site support # The project now supports a static-build path suitable for documentation-style hosting. The static site includes dashboard cards and charts, a WCAG support coverage summary table, page inventory validation, fix prioritization, drilldown search and pagination, and a downloadable workbook file.\nWorkbook export model # The workbook supports both analyst review and BI workflows. It includes human summary tabs, issue-detail tabs, systemic cluster tabs, fix prioritization tabs, flat Power BI-friendly tables, and fact/dimension tabs for relational reporting.\nInterpreting overlap and confidence # The analyzer includes fields such as sources, tool_count, tool_families, tool_family_count, consensus, and confidence. These help answer whether a finding was seen by multiple engines and how much evidence sits behind the pattern.\nInterpretation guidance and limits # Warning: Automated coverage remains partial. A wider tool stack improves breadth, overlap, and confidence, but it does not mean the project is capturing the full accessibility picture. Manual testing and human review are still necessary.\nExamples of areas that often still need human judgement include alt-text quality, meaningful link text in context, logical focus order, error recovery, semantic clarity, and cognitive accessibility.\nSuggested workflow # Run the scanners and collect report files. Build the normalized dashboard / workbook output. Check the page inventory panel first. Use the dashboard to identify top problem types, hot components, WCAG concentration, and fix-once opportunities. Use the workbook for filtered triage, handoff, BI, and reporting. Re-run after remediation to compare concentration, overlap, and systemic impact. Quick start # python -m venv venv source venv/bin/activate pip install -r requirements.txt uvicorn app.main:app --reload Related guides # Dashboard Guide explains the cards, panels, and drilldowns. Workbook Guide explains each spreadsheet tab and the main column families. ","date":"28 May 2026","externalUrl":null,"permalink":"/guides/accessibility-systemic-analyzer/","section":"Accessibility analyzer","summary":"Project overview covering the architecture, supported tools, workflow, workbook, and dashboard notes.","title":"Accessibility Systemic Analyzer","type":"guides"},{"content":"This guide explains the current dashboard cards, charts, wide panels, and drilldown behavior. It reflects the newer support table, stricter WCAG-only charting, friendlier rule labels, and drilldown search/paging.\nRecent dashboard changes: WCAG Support Coverage panel, WCAG chart cleanup, improved rule labels, better component label display, and drilldowns with search, page-size controls, and pagination.\nHow to read the dashboard overall # How much issue volume exists? Where is it concentrated? How much of it looks systemic or reusable? Which fixes could remove the most repeated issues first? The top cards provide a snapshot. The chart panels show pattern shape. The wide panels validate the dataset and help turn findings into action. The drilldown lets you inspect the findings behind a chart bar or fix row.\nTop metric cards # Violations: total processed findings. Pages Affected: count of unique normalized pages with at least one finding. WCAG Criteria Affected: count of distinct WCAG success criteria represented. Shared Pattern Impact: percentage of issue volume tied to shared patterns or reusable sources. Opportunity Score: estimated percentage of findings concentrated in the top remediation opportunities. Accessibility Debt Index: weighted burden score based on repetition, spread, and systemicity. Cross-Tool Overlap: counts of verified, likely, and single findings. Evidence Confidence: evidence strength summary. WCAG Levels: A / AA / AAA counts. Legacy Frames: frame/iframe-related counts and pages. Shared Source Rate: percentage tied to shared components, templates, or patterns. Top 5 Page Concentration: share of issue volume in the five most affected pages. Main chart and table panels # Tool Agreement Profile # Shows how often each tool’s findings agree across families, overlap only within the same family, or remain unique. This is a corroboration view, not a truth meter.\nWCAG Support Coverage # A summary table of high-level WCAG support breadth by tool framework. Treat it as a capability overview rather than a guarantee that every configured run is exercising every supported rule.\nTop Problem Types # Rolls component findings into broad issue areas such as Forms, Interactive, Navigation, Content, Structure, Media, ARIA, and Other.\nComponent Heatmap # Shows which inferred UI components generate the most findings. Labels are humanized for display, while drilldowns still use the raw component keys underneath.\nWCAG Rule Breakdown # Shows only valid WCAG criteria now. Non-WCAG rule ids and messages are intentionally excluded.\nIssues per Page # Highlights the most affected pages so you can spot concentration or template noise quickly.\nPage Inventory Check # Validates cross-tool page coverage before you trust the rest of the dashboard. This is one of the most important trust checks in the dashboard.\nFix Once, Benefit Many # A ranked shortlist of repeated patterns most worth fixing first. It is the most action-oriented panel in the dashboard.\nDrilldown behavior # Clicking a chart bar or fix row opens the drilldown panel for the current subset.\nSearch across the current drilldown result set Page size selection (10 / 25 / 50 / 100) Pagination for long result sets Typical fields shown include rule label, page, component, issue scope, pattern, source, DOM path / selector, fingerprint, and message.\nInterpreting overlap and coverage carefully # More overlap across tool families often suggests stronger corroboration. Same-family overlap can be sensitive to deduplication logic and selector/fingerprint strictness. A wider tool stack improves breadth and confidence, but automated coverage remains partial. Warning: Dashboard totals and overlap views should be interpreted as evidence organization, not as the final truth about accessibility quality.\nSuggested dashboard reading order # Check Page Inventory Check first. Use Top Problem Types and Component Heatmap to understand the shape of the issue space. Use WCAG Rule Breakdown for standards-focused reporting. Use Tool Agreement Profile to understand corroboration patterns. Use Fix Once, Benefit Many to build a remediation queue. Use drilldown search and paging to inspect the exact findings behind any bar or row. ","date":"28 May 2026","externalUrl":null,"permalink":"/guides/dashboard-charts-and-metrics-guide/","section":"Accessibility analyzer","summary":"Guide to the current dashboard cards, charts, panels, and drilldown behavior.","title":"Dashboard Charts and Metrics Guide","type":"guides"},{"content":"This workbook is the main analysis export of the accessibility framework. It combines processed findings, deduplicated and clustered views, pattern prioritization, and BI-friendly model tabs in one place.\nRecent workbook changes: friendlier rule labels, stricter separation of Rule vs WCAG fields, improved WCAG-only handling, preserved needs-review findings, and closer alignment with the current dashboard behavior.\nHow to read the workbook overall # The workbook has three broad layers:\n1. Human summary / analyst tabs # Summary Systemic Clusters Issue Details Fix Once Benefit Many Top Fixes 2. Flat reporting / BI tabs # Power BI Findings Power BI Patterns 3. Star-schema tabs # Fact Findings Dim Page Dim Rule Dim Component Dim Pattern Core field concepts used across tabs # Field family Meaning Rule Friendly display label for the issue Rule Id Underlying tool-specific identifier WCAG Criterion code such as 1.4.3 WCAG Name Criterion title such as Contrast (Minimum) Severity Normalized severity used for sorting and triage Consensus Overlap signal across merged tools Confidence Broader evidence judgement Issue Rank Score Numeric prioritization signal Pattern / Display Pattern Repeated issue family identifier and human label Sheet-by-sheet guide # 1) Summary # The executive overview sheet with values such as Violations, Pages Affected, Design System Impact, Accessibility Debt Index, and Accessibility Opportunity Score.\n2) Systemic Clusters # One of the most important analyst tabs because it moves from raw findings to repeated issue families. Typical columns include Rule, Rule Id, WCAG, WCAG Name, Level, Severity, Component, Display Pattern, Systemic, Pages, Count, Issue Rank Score, Owner Team, Root Cause, Message, and Sources.\nUse it when you want to answer which issues repeat across the estate, which look systemic, and which clusters should be triaged first.\n3) Issue Details # The detailed analyst tab: one row per processed finding with readable enriched fields such as Page, Rule, Rule Id, WCAG, WCAG Name, Severity, Component, Pattern, Consensus, Confidence, DOM context, Message, and Sources.\nThe workbook is now stricter about not using a bare WCAG code as the Rule label when that code already appears in the WCAG column.\n4) Power BI Findings # A flatter reporting table with stable-ish keys and display fields for downstream BI.\n5) Power BI Patterns # Pattern-level rollup for repeated issue families, page spread, ownership, severity, and systemicity.\n6) Fact Findings # The central fact-like table with one row per processed finding.\n7) Dim Page # Normalized page keys and display fields.\n8) Dim Rule # Rule display label, rule id, WCAG code, WCAG name, level, and sort fields.\n9) Dim Component # Component and ownership metadata used for grouping and reporting.\n10) Dim Pattern # Pattern-level dimension for repeated issue-family reporting.\n11) Fix Once Benefit Many # The pre-ranked shortlist behind the main remediation panel, including pattern, component, severity, findings count, affected pages count, owner team, and priority score.\nPriority score note: the numeric score is a ranking aid. Many top rows can tie if they share very similar severity and spread inputs.\n12) Top Fixes # A simpler root-cause summary used for concise management-style reporting.\nNeeds-review findings in the workbook # Some tools emit rows that are not hard failures but still matter operationally. Depending on the adapter, these may appear as incomplete, potentialviolation, or warning. These rows are preserved because they are useful for investigation, but they should not always be interpreted exactly the same way as confirmed failures.\nWhen the workbook and dashboard differ # The workbook and dashboard should usually align, but the workbook is often the better place to inspect the exact fields behind a chart. If something looks odd in the dashboard, check Issue Details, Dim Rule, and Systemic Clusters.\nTypical analyst workflow # Start with Summary and Systemic Clusters. Use Issue Details for filtered triage and handoff. Use Fix Once Benefit Many to build a remediation queue. Use Power BI Findings or the fact/dimension tabs for downstream reporting and modeling. ","date":"28 May 2026","externalUrl":null,"permalink":"/guides/workbook-guide/","section":"Accessibility analyzer","summary":"Reference guide for the exported spreadsheet tabs, columns, and analysis model.","title":"Spreadsheet Workbook Guide","type":"guides"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/categories/about/","section":"Categories","summary":"","title":"About","type":"categories"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/categories/accessibility/","section":"Categories","summary":"","title":"Accessibility","type":"categories"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/accessibility/","section":"Tags","summary":"","title":"Accessibility","type":"tags"},{"content":"The Accessibility Systemic Analyzer is a multi-tool analysis and reporting layer for accessibility scan output. It is built for the point where a website, application, or wider digital estate has more findings than a team can sensibly triage one page at a time.\nRather than treating every scanner result as an isolated defect, the analyzer brings reports together, normalises the data, identifies likely overlaps, groups repeated patterns, and highlights the fixes most likely to improve many pages or components at once.\nThe aim is not to produce a larger pile of findings. It is to make the findings easier to understand, prioritise, and act on.\nFrom issue volume to useful priorities # Automated tools are good at finding individual instances. They are less helpful when the same underlying problem appears dozens or hundreds of times across a site.\nA contrast failure on the home page, product page, basket, and checkout may not be four unrelated defects. It may be one shared button style that needs fixing once.\nThe analyzer is designed to surface that distinction. It helps answer questions such as:\nWhich issues are repeated across multiple pages? Which findings appear to come from a shared component, template, or pattern? Which problems are corroborated by more than one tool? Which pages or components carry the greatest concentration of accessibility debt? Which fixes are likely to remove the broadest class of issues first? What the analyzer does # The analyzer processes reports from multiple accessibility tools and turns them into a shared analysis model. It can:\ndetect and load supported report formats normalise findings into a consistent structure map rules to WCAG criteria, levels, and human-readable titles where possible preserve useful needs-review findings as well as confirmed violations deduplicate likely overlaps between tools on the same page infer likely components, component groups, templates, and shared patterns group repeated findings into systemic clusters calculate overlap, confidence, spread, and prioritisation metrics generate a static dashboard and an analyst-friendly workbook Why use more than one tool? # Different accessibility tools have different strengths, rule sets, and ways of representing results. Running several tools can improve breadth, but it can also create noise: duplicated findings, inconsistent labels, and results that are difficult to compare directly.\nThe analyzer is intended to make that wider evidence set usable. It currently supports report families including axe-core, axe-scan, IBM Accessibility Checker, Lighthouse, Oobee, UUV, Pa11y, and HTMLCS / html-sniffer.\nWhere tools appear to report the same underlying problem, the analyzer can bring those findings together. Where a tool reports an incomplete, potential, or manual-review style result, the analyzer can retain that distinction rather than presenting everything as a confirmed failure.\nDashboard: understand the shape of the problem # The dashboard provides a higher-level view of the analyzed estate. It is designed to help teams move quickly from raw volume to areas worth investigating.\nCurrent dashboard views include:\nissue volume and affected-page summaries WCAG criteria and conformance-level breakdowns cross-tool overlap and evidence confidence shared-pattern and shared-source metrics top problem types component heatmaps issues per page page inventory checks a Fix Once, Benefit Many prioritisation panel searchable, paginated drilldowns for inspecting the findings behind each chart or ranked item The Page Inventory Check is an important first step: it helps validate the coverage of the collected reports before the wider dashboard is interpreted.\nWorkbook: inspect, filter, hand off, and report # The exported workbook provides the more detailed analysis layer behind the dashboard. It is intended both for hands-on accessibility review and for wider reporting workflows.\nIt includes:\nsummary and management-style views detailed issue rows with enriched rule and WCAG fields systemic cluster analysis Fix Once, Benefit Many and top-fix shortlists flat Power BI-friendly tables fact and dimension sheets for relational reporting This makes it possible to move from an executive overview into filtered triage, technical handoff, ownership discussions, and downstream BI analysis without losing the evidence behind the headline metrics.\nWhat “systemic” means here # A systemic issue is a repeated finding pattern where one remediation change may improve many instances. That may be a design-system component issue, a repeated template problem, a recurring content structure issue, or another shared implementation pattern.\nThe analyzer does not assume that every repeated issue has the same cause. Instead, it helps surface likely reusable remediation targets so that teams can investigate and prioritise them more efficiently.\nA practical workflow # Run the accessibility scanners and collect their report files. Build the normalised dashboard and workbook output. Check the page inventory to confirm the expected coverage. Review the dashboard for concentrated problem areas, repeated patterns, WCAG hotspots, and high-value fixes. Use the workbook for deeper triage, handoff, reporting, and BI workflows. Re-run after remediation to compare changes in volume, concentration, overlap, and systemic impact. Important limitation # Automated coverage remains partial. A broader tool stack improves breadth, corroboration, and confidence, but it does not replace manual accessibility testing or human judgement.\nAreas such as alt-text quality, meaningful link wording in context, logical focus order, error recovery, semantic clarity, and cognitive accessibility still need human review.\nExplore the analyzer # Accessibility Systemic Analyzer overview Dashboard charts and metrics guide Spreadsheet workbook guide View analyzer jobs board ","date":"28 May 2026","externalUrl":null,"permalink":"/guides/","section":"Accessibility analyzer","summary":"A multi-tool analysis and reporting layer for finding repeated accessibility barriers, prioritising systemic fixes, and turning scan output into a practical remediation plan.","title":"Accessibility analyzer","type":"guides"},{"content":"An accessibility audit should help a team act. The aim is not simply to record issues, but to explain what matters, why it matters and how to improve it.\nI provide accessibility audits for websites and mobile apps, from focused reviews of key journeys to broader analysis across larger digital estates.\nWhat an audit can cover # WCAG-informed review of key pages, screens, journeys or flows Keyboard, focus and navigation behaviour Touch targets, gestures and mobile interaction patterns Forms, validation and error recovery Structure, semantics, headings and landmarks Link text, buttons, controls and interaction clarity Contrast, zoom and visual readability Screen reader and assistive technology testing where appropriate Automated scan review, with manual judgement layered on top Orientation, reflow and responsive behaviour where relevant What you get # Prioritised findings Plain-English impact notes Reproduction steps where useful Suggested fixes and practical examples Clear separation between confirmed issues and items requiring human judgement Re-test support after remediation A draft accessibility statement where appropriate Test automation reports and supporting code where useful Larger sites and repeated issues # For larger websites, apps and digital estates, the same underlying problem can appear across many pages or screens. This is where systemic analysis helps.\nRepeated issues can be grouped by pattern, component, template or WCAG concentration, allowing teams to prioritise shared causes rather than chase every individual row one by one.\nRead about the Accessibility Systemic Analyzer\nRecent clients # M\u0026amp;S JamUp Asclepius Leva Clinic Adora Dawa Innovations Coloplast DigiSafe HealthwaveHub IMS Maxims MediaLogix ZoomDoc TympaHealth Infervision Book a call # To discuss the scope of an accessibility audit, book a call below.\n","date":"28 May 2026","externalUrl":null,"permalink":"/audit-services/","section":"Accessibility Specialist \u0026 Audit Services","summary":"Practical accessibility audits for websites and mobile apps, with clear findings, actionable fixes and systemic analysis.","title":"Accessibility audits","type":"page"},{"content":" Accessibility support\nClear accessibility work. Practical fixes. Less theatre. I help teams understand barriers, prioritise what matters, and turn accessibility findings into changes that improve real products.\nSpecialist support Audit services Accessibility specialist\nPractical accessibility support for teams building web products WCAG-informed support across design, development and testing, with a focus on real user journeys, keyboard behaviour, semantics, forms, errors and clear next steps.\nSpecialist support Audit services\nClear accessibility reviews without the theatre Focused accessibility reviews that combine automated checks, manual investigation and practical remediation guidance for developers, designers, testers and product teams.\nAudit services Accessibility Systemic Analyzer\nFor when accessibility reports get too big to triage page by page. The analyzer groups multi-tool output into repeated patterns, cross-tool evidence and fix-once benefit-many views, helping teams focus on shared causes instead of drowning in page-level findings.\nAccessibility analyzer guides ","date":"28 May 2026","externalUrl":null,"permalink":"/","section":"Accessibility Specialist \u0026 Audit Services","summary":"Practical accessibility support, audit services, and systemic analysis for clearer findings and better fixes.","title":"Accessibility Specialist \u0026 Audit Services","type":"page"},{"content":"Accessibility work is most useful when it helps a team make better product decisions, not when it simply produces a long list of defects.\nI provide practical accessibility support for websites and mobile apps across design, development, testing and release. The focus is on evidence, user impact and realistic remediation: helping teams understand what matters, make informed decisions and improve the experience they deliver.\nWhere I can help # Reviewing journeys, screens, components and patterns before they become expensive to change Checking keyboard access, focus behaviour, navigation, forms and error handling Reviewing touch targets, gestures and mobile interaction patterns where relevant Carrying out screen reader and assistive technology checks Combining automated checks with manual testing and human judgement Translating accessibility findings into plain-English product and engineering decisions Helping teams understand WCAG without turning it into a box-ticking exercise Supporting re-testing and verification after fixes are made Typical outputs # Clear notes on user impact and risk Prioritised recommendations Developer-friendly remediation guidance Examples of better patterns where useful Re-test notes after changes Good fit # This service is useful when you need accessibility judgement alongside delivery work: reviewing a feature, unblocking a team, sense-checking fixes, improving a design system, preparing for release or deciding whether a fuller audit is needed.\nFor a more structured review with prioritised findings, see accessibility audits.\nBook a call # To discuss where accessibility support would be most useful, book a call below.\n","date":"28 May 2026","externalUrl":null,"permalink":"/accessibility-specialist/","section":"Accessibility Specialist \u0026 Audit Services","summary":"Practical accessibility support for websites and mobile apps across design, development, testing and remediation.","title":"Accessibility support","type":"page"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/analysis/","section":"Tags","summary":"","title":"Analysis","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/audit/","section":"Tags","summary":"","title":"Audit","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/automation/","section":"Tags","summary":"","title":"Automation","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/dashboard/","section":"Tags","summary":"","title":"Dashboard","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/manual-testing/","section":"Tags","summary":"","title":"Manual Testing","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/metrics/","section":"Tags","summary":"","title":"Metrics","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/power-bi/","section":"Tags","summary":"","title":"Power-Bi","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/quality-engineering/","section":"Tags","summary":"","title":"Quality Engineering","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/reporting/","section":"Tags","summary":"","title":"Reporting","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/specialist-support/","section":"Tags","summary":"","title":"Specialist Support","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/systemic-analysis/","section":"Tags","summary":"","title":"Systemic Analysis","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/categories/testing/","section":"Categories","summary":"","title":"Testing","type":"categories"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/testing/","section":"Tags","summary":"","title":"Testing","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/wcag/","section":"Tags","summary":"","title":"WCAG","type":"tags"},{"content":"","date":"28 May 2026","externalUrl":null,"permalink":"/tags/workbook/","section":"Tags","summary":"","title":"Workbook","type":"tags"}]