Work

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Scheduled automation also maintains the quality of the dataset by refreshing sources and removing outdated opportunities instead of allowing stale listings to accumulate indefinitely.

A healthcare treatment workflow can contain many dependencies beyond the clinical appointment itself. Funding may need approval, documentation may be incomplete, fabrication or parts may be required, another appointment may need to be scheduled, or an external healthcare provider may need to supply information before treatment can continue.

Hodgson Group already had these processes inside ClickUp, but years of operational growth had created a structure that was difficult for staff to navigate and depended heavily on manual updates.

For healthcare professionals whose primary role is patient care rather than systems administration, the workflow needed to make the next required action immediately understandable.

The challenge was therefore not simply to reorganize tasks. The entire patient treatment workflow needed to be redesigned so clinical, administrative, funding and production activities could move through one consistent operating model.

ConsultEvo created a dedicated Patients & Treatments ClickUp space and rebuilt the hierarchy around Hodgson Group’s actual treatment process.

Patient treatment records progress through structured stages including Intake, Preparation, Initiation, Integration, Stabilization and Mature, with dedicated workflows for treatments, repairs and specialized cases.

The system captures the operational information required to move treatment forward, including treatment stage, clinician responsibility, funding status, payer approvals, appointments, fabrication progress, parts ordering, delivery, hospital coordination and follow-up actions.

More than 200 native ClickUp automations reduce manual administration by moving work through the workflow, updating operational fields, creating required actions and surfacing incomplete steps.

A role-based visual layer uses consistent color coding and action cues so clinical, administrative and technical staff can quickly identify the work relevant to them without needing to understand the underlying ClickUp architecture.

Existing patient and treatment records were migrated from the previous system into the rebuilt environment, allowing Hodgson Group to continue operating from the new workflow without maintaining two competing processes.

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.

Lead information was being handled across disconnected steps, with too much reliance on people manually checking, entering and updating records.

That created several operational problems:

The same lead or opportunity could be entered more than once.
Intake information was not always structured consistently.
Sales pipeline records required manual maintenance.
Missed calls and follow-up activity were difficult to manage reliably.
Reporting became less useful when CRM records were incomplete or duplicated.
Team members had to spend time checking whether a record already existed before deciding what to do next.

The problem was not simply getting information into HubSpot. The bigger issue was controlling what should be created, what should be updated and what should happen next.

The new workflow places Zapier between the intake process and the CRM.

Each submission enters a structured automation flow where identifying information is checked before records are created. Existing records can be matched instead of duplicated, while new submissions continue through the appropriate creation and association steps.

The resulting contact and opportunity data is then surfaced in HubSpot, where the sales team can manage pipeline stages, calls, follow-ups and outcomes without manually rebuilding the intake information.

The automation layer keeps the intake logic separate from the CRM interface, making the sales pipeline cleaner while preserving the flexibility to extend the workflow later.

The sales exports contained customer information, product information, transaction values, quantities and dates in a flat tabular format.

Inside HubSpot, however, that information belongs across several related record types.

A customer belongs to a Company. The transaction belongs to a Deal. Individual products sold are represented through Line Items, which reference records from HubSpot's Product Library.

The import therefore had to determine whether a Company or Product already existed, create it when it did not, create the Deal and Line Item records, and then preserve the correct associations between them.

The process also needed to handle returns, inconsistent CSV headers, invalid values, duplicate processing, missing records and API failures without silently losing data.

ConsultEvo built a custom import application around the HubSpot CRM API.

Each incoming transaction is validated before processing. The system searches HubSpot for the customer using a unique customer number and searches the Product Library using SKU.

When matching records already exist, they are reused. When they do not exist, the required Company or Product record can be created from the source data.

The system then creates the Deal, creates its Product-backed Line Item and builds the required HubSpot associations so the transaction becomes part of the customer's actual CRM history rather than an isolated imported record.

Positive transactions are routed into the completed sales workflow, while negative quantities or sales values are recognized as returns and routed separately.

The importer also includes progress tracking, retry handling, duplicate protection, header mapping, detailed processing logs and exception reporting so import problems can be identified and corrected rather than disappearing inside a bulk migration.

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.