Make

Job seekers often depend on major job boards, but companies also publish roles directly through their own career pages and applicant tracking systems.

Discovering those opportunities manually requires repeatedly visiting employer websites, checking whether positions are still active and determining whether individual roles are remote, relevant and still accepting applications.

The technical challenge was equally important. As the dataset grew, the original Google Sheets implementation became too slow for reliable searching and synchronization. Airtable improved the structure but still introduced performance limitations as the number of jobs and automated updates increased.

The platform needed a faster data layer capable of supporting continuous collection, cleanup, classification and frontend search without slowing down as the database expanded.

Google Apps Script automation monitors employer career sources and applicant tracking systems, retrieves available roles and transforms the source information into a consistent job structure.

Job records are normalized before being synchronized into Supabase, including information such as employer, title, location, remote status, category and original application URL.

Supabase replaced earlier Google Sheets and Airtable implementations as the primary data platform because it provided significantly faster querying and a more reliable foundation for a growing automated dataset.

WordPress provides the public discovery layer. Job seekers can search by keyword, browse categories, sort opportunities and open each role directly on the employer’s original career page.

Scheduled automation also maintains the quality of the dataset by refreshing sources and removing outdated opportunities instead of allowing stale listings to accumulate indefinitely.

Important finance, sales, procurement, warehouse and supply chain information lived across separate systems including Xero, Shopify, ShipHero, Zoho, ClickUp and spreadsheets. Teams often had to retrieve, compare and update information manually, while external stakeholders such as factories and logistics partners also needed to contribute data throughout operational processes.

The challenge was therefore larger than creating dashboards. The underlying workflows, data relationships and responsibilities first had to be structured so the company could manage purchase orders, B2B sales, shipments, reporting and operational coordination from consistent information as the business continued to grow.

ConsultEvo designed an architecture in which each source platform remains responsible for its core business function while shared automation, operational and data layers connect them.

Make, Google Apps Script, APIs and webhooks handle synchronization, orchestration and external data collection. ClickUp acts as the operational layer for processes including B2B sales, purchase orders, manufacturing, shipments and supply chain coordination. Supabase provides the centralized structured data layer used by reporting applications, dashboards and other internal tools.

Custom WordPress interfaces provide management dashboards, mobile-friendly reporting, factory and logistics tools and visual operational applications without requiring every user or external stakeholder to access the underlying systems directly.

The same architecture also supports AI interfaces that let staff query operational information in natural language, making business data easier to access and helping new employees understand information that would otherwise require knowledge of multiple spreadsheets and systems.

Speed was important, but the site was being built specifically to grow through organic search. That meant we could not treat technical SEO as something to clean up later.

A publishing site creates complexity quickly.

Every new article introduces another URL, title, description, canonical, set of internal links, structured data, breadcrumbs, social metadata and sitemap entry. Hundreds of pages make problems that are trivial on a ten-page site much harder to see.

The challenge was therefore larger than simply getting Remodel Atlas online.

We needed to build a lightweight custom website that could support calculators and hundreds of content pages while keeping its technical structure consistent as the site expanded.

We also wanted to learn from the process rather than hide complexity behind a CMS or a collection of SEO plugins.

That created a second requirement: we needed our own way to continuously inspect what search engines and AI crawlers were actually receiving from the site.

Remodel Atlas was built as a connected publishing product rather than a collection of isolated pages.

The public website gives homeowners several ways into the content. Cost calculators provide immediate planning ranges based on project type, size, finish level and local cost assumptions. Editorial guides cover broader remodeling decisions, while the Knowledge Base handles detailed technical and long-tail questions.

Behind the public experience, we are building the operational systems required to manage the product at scale.

The first major internal implementation is the SEO + AI Visibility Audit. It discovers the complete public site, crawls every URL and evaluates the technical signals behind search and AI discoverability.

Instead of relying on a third-party score, the auditor exposes the underlying evidence: HTTP behavior, sitemap coverage, robots rules, metadata, canonicals, schema, breadcrumbs, FAQ visibility, internal links, images, social metadata, AI-facing files and cross-page conflicts.

The system also exports completed audits as PDF, CSV and JSON so findings can be reviewed, validated and used in the next round of improvements.

This internal tooling approach is intentional. As Remodel Atlas grows, repetitive operational work becomes a candidate for its own implementation rather than another manual process.

DataCose needed a Business Analyst who met specific qualification requirements while remaining within the available hiring budget.

Searching internationally expanded the available talent pool, but it also introduced more candidate evaluation, communication and coordination across the recruitment process.

Without a structured operating workflow, candidate information, status changes and stakeholder decisions can quickly become fragmented across messages and individual follow-ups.

The challenge was therefore twofold: identify high-quality international talent that genuinely matched the role, and create a recruitment process that allowed DataCose to clearly see where every candidate stood and what needed to happen next.

ConsultEvo built a dedicated ClickUp recruitment workflow around the DataCose hiring process.

Candidates were organized through defined recruitment stages so the team could see progress across the pipeline rather than relying on individual updates or disconnected communication.

The ClickUp environment provided shared visibility into candidate status and supported the communication and status-update processes required to keep hiring moving.

Native ClickUp automations reduced repetitive administrative work by helping update recruitment records and communicate changes as candidates progressed through the hiring workflow.

This created one operational layer for candidate tracking, recruiter activity and stakeholder visibility while ConsultEvo continued managing the actual international talent search and qualification process.