Xano + Make.com Specialist Needed

An expert Xano + Make.com Specialist is required to finalize, optimize, and fully automate an existing backend content-validation SaaS engine. We are not building from scratch. We already have a live, functioning prototype workspace with active paragraph text-slicing regex loops. You will be handed an explicit v2.5 Master Execution Prompt, an operational Process Accountability Map (PAM), and real-world benchmark datasets to configure the remaining backend automation layers. We require a developer who takes pride in solving backend logic using native Xano visual filters and expression blocks, maintaining absolute platform independence without leaning on custom JavaScript blocks or custom Python overrides. Core Scope of Work across 3 Capped, Fixed-Price Milestones: - Milestone 1 (20% of Budget): Schema Upgrade, State Gates & Intake Router - Deliverable: Database schemas fully updated to support v2.5 tracking columns (including specialized Enum tracks for method_disclosure_tier and segmentation_version_tag). Single-door intake question runs a quick AI decomposability test to automatically route and flag rows as Guided or General. Implement a strict 'Lock-Gate' state management function; downstream sentence processing must be programmatically blocked until an explicit human authorization transitions the engagement status to 'Locked Status'. - Milestone 2 (50% of Budget): Core Function Stack Refactoring, Automated Reconciliation & IER Math Logic - Deliverable: Slicing loop successfully flattens nested arrays and writes string rows cleanly. Build a background mathematical validation module to execute Programmatic Reconciliation at Step 3, verifying that all absolute IDs from S1 to Sn contain zero gaps or duplicates prior to grounding. Configure a dedicated validation loop for Evidence Citations to stamp them with one of four explicit integrity states (Verifiable, Unlocatable, Misattributed, Artifact) while cleanly dropping them from the IER denominator. The final IER calculator function must programmatically subtract 'Not-applicable' rows from the denominator sum prior to division, running math while enforcing zero-denominator exception guardrails. Build a backend data aggregation filter to monitor interested-party tag density; if weak claims mapped to a single corporate entity exceed a 30% concentration threshold, output an aggregate clustering alert. - Milestone 3 (30% of Budget): Make.com Automation, Multi-Pattern Charting & Export Compliance - Deliverable: Make.com scenarios fully mapped using data-type safety filters. Set up an array compression module that sorts synthesis results by a numeric materiality score and limits outputs to exactly the top 4 rows. If an engagement contains = 8 locked elements, the summary template must programmatically filter out all 'Addressed' rows, printing ONLY 'Not addressed' rows and their text pointers to prevent exceeding the layout ceiling. Configure the chart canvas module to execute a Three-Fill Treatment on the Grounding Donut, natively rendering Solid fills for external proof, Diagonal hatching for internal data, and a distinct Dotted fill pattern for Grounded-self-defining rows. Ensure 'Unattributed' slices render in Caution Amber-Yellow #F0C420 (not grey) to expose evidence gaps visually, while 'Not-applicable' rows recede cleanly in Light Grey #C7CDD4. Hardcode the required multi-pattern decoding legend directly inside the image caption box canvas. The required footer disclaimer must be programmatically burned into every single export format (.docx, .html, and .pdf) of all reader-facing reports without exception. This milestone is cleared when our pre-formatted 424-claim master sample report dataset successfully processes through your live pipelines with 100% data accuracy. REQUIRED TECHNICAL STACK: - Deep expertise with the Xano Function Stack, XanoScript filters, variable manipulation, and relational database logic. (Xano Certification or active community footprint is a massive plus). - Advanced Make.com Advanced Architect skills (nested looping, JSON data aggregation, error-handling directives). - Clear understanding of implementing "Structured JSON Output Mode" inside enterprise LLM endpoints (Claude, OpenAI, Gemini).