Salesforce and HubSpot projects, and what changed afterwards

The challenge

Quotes lived in spreadsheets, forecasting was a monthly email exercise, and no two regions defined a pipeline stage the same way.

What we did

• Single opportunity process with shared stage definitions and exit criteria
• Guided quoting with approval rules for discount thresholds
• Forecast and territory reporting for regional and group views
• Training for 140 users delivered before go-live, not after

Results

9 weeks

Discovery to go-live

96%

Weekly active users at day 60

-40%

Time to produce a quote

The challenge

Email, forms and the deal pipeline sat in separate platforms, so nobody could say which marketing activity produced revenue.

What we did

• Lifecycle stage model agreed between marketing and sales before build
• Property clean-up: 90 unused fields retired, naming convention documented
• Lead scoring, rotation and SLA-based follow-up workflows
• Attribution and pipeline dashboards for the leadership meeting

Results

6 weeks

Full rollout

3 → 1

Tools consolidated

2.1x

Marketing-qualified leads worked

The challenge

Marketing ran HubSpot, sales ran Salesforce, and the sync duplicated contacts while overwriting owner fields both ways.

What we did

• Field-by-field mapping with a documented system of record per object
• Deduplication and matching rules applied before the sync was re-enabled
• Closed-loop revenue attribution from campaign through to closed-won
• Error monitoring with a named owner for sync failures

Results

0

Duplicate records after cutover

100%

Closed-won deals attributed to source

5 days

Faster monthly reporting cycle

The challenge

Two client orgs needed to move over a million records across standard and custom objects, including Leads, Accounts, Contacts, Opportunities, Tasks, Files and Activities, without breaking a single relationship or losing data quality.

What we did

• End-to-end migration across Leads, Accounts, Contacts, Opportunities, Tasks, Files and Activities for both orgs
• High-volume imports through Salesforce Inspector and Data Loader with all object relationships maintained
• Data requirements, mapping and validation strategies defined and documented before any load
• Data cleansing, deduplication and QA through pre- and post-migration checks

Results

1M+

Records migrated across 2 orgs

99.99%

Migration accuracy

100%

Object relationships maintained

The challenge

Support requests arrived in a shared inbox, so response times were guesswork and recurring issues went untracked.

What we did

• Ticket pipelines with routing by product line and severity
• Knowledge base seeded from the 30 most repeated questions
• SLA automation with escalation to account owners
• Customer satisfaction surveys wired into the reporting dashboard

Results

-58%

First response time

34%

Tickets resolved by self-service

4.7/5

Customer satisfaction score

The challenge

Six years of accumulated workflow rules, process builders and Apex triggers caused daily save errors and unpredictable automation.

What we did

• Automation audit and consolidation into a single flow per object
• Unused custom code and fields retired under change control
• Regression test scripts handed to the internal admin team
• Release process and sandbox strategy documented

Results

-72%

Automation errors per week

1

Flow per object, down from 11 rules

In-house

Ongoing ownership after handover

The challenge

The client was moving everything out of one Salesforce instance into a brand new account, with accounts, contacts, opportunities, leads and live pipeline data all needing to arrive intact and working on day one.

What we did

• Mapped and extracted all five objects, keeping account, contact and opportunity relationships intact
• Trial loads into the new instance with field-level reconciliation before anything was committed
• Owners, record types and pipeline stages remapped so the new org mirrored how the team actually sells
• Documented cutover runbook with validation checks and post-migration support

Results

5

Core objects migrated end to end

100%

Pipeline history carried into the new instance

0

Records lost at cutover

The challenge

The client's go-to-market team needed a developer to build the backbone of their systems: a custom card view for contacts and other objects, SQL-to-HubSpot ETL pipelines to automate existing flows, and a recurring data cleaning routine to lift CRM-wide data quality.

What we did

• Designed, built and maintained custom HubSpot cards and custom objects with a 99.9% uptime target and documented rollback plans
• Built SQL-to-HubSpot ETL pipelines from the client's existing tables to automate their current flow
• Reviewed how the existing pipeline was designed and suggested improvements to the implementation
• Created a batch data cleaning process to run across the entire CRM, using HubSpot APIs, webhooks, private apps, OAuth and custom-coded workflow actions

Results

99.9%

Uptime target met on custom cards and objects

20 to 40

Hours per week, scaling with outcomes

Weeks

Of troubleshooting saved across the ecosystem

The challenge

The client runs a SaaS analytics platform with its own login, which can deep-link to a contact or account through URL parameters. They wanted that data to appear next to leads and accounts inside Salesforce Lightning, without reps having to sign in to the second system, and without a custom API authentication layer.

What we did

• Proposed the technical approach and implemented single sign-on between the SaaS platform and Salesforce, so identity is handled by SSO instead of custom API code
• Embedded the app on lead, contact and account record pages through Lightning record pages and custom tabs, as an alternative to Visualforce pages
• Passed the Salesforce record ID into the app as a URL parameter, so opening a lead or account loads exactly that record's data
• Documented the setup and rollback so the client's own admins can extend it to further objects

Results

0

Extra logins for sales reps

3

Objects with the app embedded in context

1

Seamless surface for leads and accounts

The challenge

This was an existing project that was semi-functional in production, not a build from zero. The client needed ecommerce data flowing from Elevate LMS and WooCommerce into Salesforce, with every order synced to multiple Salesforce tables and kept up to date as orders change, syncing up to once per hour. The existing implementation, hosted on MS Azure, was producing duplicate entries in Salesforce, updates that stopped appearing after the initial sync, and missing or incorrect fields.

What we did

• Took over and revised existing production code rather than starting from zero, auditing the integration to find the root causes of duplicates, stale updates and wrong fields
• Rebuilt the order sync so updates propagate to every related Salesforce table, not just on first creation
• Deduplication and matching rules so hourly runs cannot create repeat records
• Field-level mapping corrected and scheduled the sync on the Azure codebase for reliable hourly runs

Results

0

Duplicate entries after the rebuild

Hourly

Sync frequency with updates propagating

2

Sources unified into Salesforce

The challenge

New leads synced into Salesforce from the data platform Clay needed checking against every existing Contact and Lead before the sales team touched them. The client had outgrown a Python script that processed CSV exports and took up to a week per run, and an automated Python and AWS attempt had proved unreliable. They wanted a robust, scalable solution running inside Salesforce itself, built to stay within governor limits and to handle fuzzy matching across several fields.

What we did

• Built an Apex trigger and batch automation that checks every newly created lead against all existing Leads and Contacts the moment it arrives from Clay
• Matching logic across Full Name (fuzzy and phonetic, including Soundex), full email and domain-level comparisons, website domains, LinkedIn URL IDs and company name
• Populated each record with the matched Lead or Contact ID, a High, Medium or Low confidence score, the matched fields and the matched values
• Designed for large datasets with governor-safe batch processing, plus error handling and logging so performance can be monitored and issues traced

Results

Seconds

From lead created to duplicate flagged

5

Fields matched per new record

0

External scripts left in the process

The challenge

The client had generated synthetic data for Leads, Contacts, Accounts, Opportunities and Campaigns to train AI models for marketing and sales analysis. Accounts, Contacts, Opportunities and Leads were already loaded, but the remaining datasets were still outside the CRM, and the relationships between all five objects had to be preserved and verified. They also wanted the door open to adding more synthetic data, including transaction-level marketing data, because the more detail captured at the customer and account level, the better the AI analysis could be.

What we did

• Completed ingestion of the remaining synthetic datasets that had not yet reached the CRM
• Maintained and verified relationship integrity across Leads, Contacts, Accounts, Opportunities and Campaigns so every record links correctly to its parent and child records
• Structured the loads so additional synthetic data, including transaction-level marketing data, could be added later without breaking existing relationships
• Optimised the data model for AI training use, maximising detail captured at the customer and account level

Results

5

Objects fully loaded and related

100%

Relationship integrity verified

AI-ready

Training data at customer and account level