Connect your business systems without adding operational drag.

I help teams whose tools do not talk to each other connect them reliably, so data moves between systems and failures become visible before they cost a deal.

Where the problem shows up

Most teams do not buy bad software. They buy capable tools that were never designed around the exact way their business operates, so CRM data sits in one place, invoices in another and support activity somewhere else. The usual remedy is a person: someone exports, copies and reconciles, and reporting arrives after three spreadsheets have been merged.

That hidden manual work becomes expensive. Deals wait for updates, finance does not trust operational numbers, and customer handoffs depend on memory. The person who connects the systems becomes the integration layer of the business. What happens when that person is away, or when a field is renamed in one tool and nobody notices? And how many decisions rest on data that was correct when it was exported?

The better principle is to give each system a defined role, a clear data contract and a monitored path for information to move. That is the work I do.

What I have changed for others

60+ websites: one schema so results can be compared

Performance data from more than 60 websites was stored differently on each. I designed a shared analytics schema and centralised the data in BigQuery. Teams can now compare any property with any other. Read the case study.

Client reporting: five data sources in one funnel

Google Ads, call tracking, Google Business Profile, analytics and the CRM were each reported on separately. I built a Looker Studio system that connects them, so acquisition metrics sit beside lead stages and conversion outcomes in one dashboard per client. Read the case study.

26 ledgers: one intercompany procedure

Intercompany balances across 26 ledgers could not be explained consistently. I designed standardised categorisation rules and a formal intercompany procedure, so balances between entities can be compared on the same terms. Read the case study.

What changes for your business

Data arrives in the right system in the right format, with enough observability to trust it. Each important record has one authoritative source, and a failed transfer raises an alert and has a recovery path. The work can include mapping systems and ownership, data contracts, workflow integrations, monitoring and documentation.

Where this leads next

Integration is the mechanism behind data liquidity and the base for automation without fragile copy-and-paste steps. The detail of common integration paths and the implementation approach is below.

API and SaaS integrations

Connect CRM, ERP, finance, support, marketing, data warehouse, and internal tools through API-based workflows that match how the business actually works.

Data pipelines and transformations

Move records between systems with validation, transformation, deduplication, and ownership rules so downstream teams can trust the data they receive.

Event-driven workflows

Trigger follow-up actions when business events happen: new lead, paid invoice, failed sync, closed deal, support escalation, or renewal risk.

Monitoring and recovery paths

Make integration failures visible with logging, alerts, retry logic, and documented recovery steps instead of silent sync errors that surface weeks later.

Common integration paths

CRM to finance: clean handoff from quote, deal, invoice, and payment events.

Marketing to sales: lead routing, enrichment, lifecycle status, and campaign attribution.

Support to customer success: account health signals, escalation triggers, and renewal context.

Operational tools to reporting: trusted data pipelines feeding dashboards and decision workflows.

Automation handoff: integrations that support RPA without fragile copy-and-paste processes.

The implementation approach

First, define the business process and the source of truth for each important record. If every system is allowed to be authoritative, the integration will eventually create conflicts.

Second, design the data contract: required fields, transformation rules, IDs, duplicate handling, and permissions. This is where a clean integration saves future maintenance time.

Third, build the workflow with observability from the beginning. Logs, alerts, retries, and manual recovery paths are part of the product, not cleanup work at the end.

Finally, document the integration so future teams know what moves, why it moves, what can break, and how to change it without guessing.

What does an IPaaS service include?

Do we need to replace our current software?

How do you prevent integrations from silently failing?

Where should IPaaS fit with RPA?

Which two systems should talk to each other and do not?

Name the two systems and the handoff that still depends on a person. I will map the records involved, decide which system owns each one and show how a failure would be caught.

Understand the problem. Trace the system. Build the right solution.

Which part of your business runs on workarounds?

Tell me where work or information breaks today. I will trace how it moves and tell you honestly what I would change first, even when the answer is that no new software is needed.

API and SaaS integrations

Connect CRM, ERP, finance, support, marketing, data warehouse, and internal tools through API-based workflows that match how the business actually works.

Data pipelines and transformations

Move records between systems with validation, transformation, deduplication, and ownership rules so downstream teams can trust the data they receive.

Event-driven workflows

Trigger follow-up actions when business events happen: new lead, paid invoice, failed sync, closed deal, support escalation, or renewal risk.

Monitoring and recovery paths

Make integration failures visible with logging, alerts, retry logic, and documented recovery steps instead of silent sync errors that surface weeks later.

Common integration paths

CRM to finance: clean handoff from quote, deal, invoice, and payment events.

Marketing to sales: lead routing, enrichment, lifecycle status, and campaign attribution.

Support to customer success: account health signals, escalation triggers, and renewal context.

Operational tools to reporting: trusted data pipelines feeding dashboards and decision workflows.

Automation handoff: integrations that support RPA without fragile copy-and-paste processes.

The implementation approach

First, define the business process and the source of truth for each important record. If every system is allowed to be authoritative, the integration will eventually create conflicts.

Second, design the data contract: required fields, transformation rules, IDs, duplicate handling, and permissions. This is where a clean integration saves future maintenance time.

Third, build the workflow with observability from the beginning. Logs, alerts, retries, and manual recovery paths are part of the product, not cleanup work at the end.

Finally, document the integration so future teams know what moves, why it moves, what can break, and how to change it without guessing.

What does an IPaaS service include?

Do we need to replace our current software?

How do you prevent integrations from silently failing?

Where should IPaaS fit with RPA?

Which two systems should talk to each other and do not?

Name the two systems and the handoff that still depends on a person. I will map the records involved, decide which system owns each one and show how a failure would be caught.

Understand the problem. Trace the system. Build the right solution.

Which part of your business runs on workarounds?

Tell me where work or information breaks today. I will trace how it moves and tell you honestly what I would change first, even when the answer is that no new software is needed.

Connect your business systems without adding operational drag.

Where the problem shows up

What I have changed for others

60+ websites: one schema so results can be compared

Client reporting: five data sources in one funnel

26 ledgers: one intercompany procedure

What changes for your business

Where this leads next

Build the connections your operations depend on

API and SaaS integrations

Data pipelines and transformations

Event-driven workflows

Monitoring and recovery paths

Common integration paths

The implementation approach

IPaaS services FAQ

What does an IPaaS service include?

Do we need to replace our current software?

How do you prevent integrations from silently failing?

Where should IPaaS fit with RPA?

Which two systems should talk to each other and do not?

Build the connections your operations depend on

API and SaaS integrations

Data pipelines and transformations

Event-driven workflows

Monitoring and recovery paths

Common integration paths

The implementation approach

IPaaS services FAQ

What does an IPaaS service include?

Do we need to replace our current software?

How do you prevent integrations from silently failing?

Where should IPaaS fit with RPA?

Which two systems should talk to each other and do not?