Build path01
Internal platforms and portals
When the integration needs a human operating surface, we build the internal tool, admin portal, or product workflow around the connected systems.
AI-Ready Integration Architecture
Connect your systems. Give AI reliable context.
Build the APIs, data ownership rules and event flows that let people and agents work across CRM, ERP and internal tools without guessing which record is right.
- 20–30 minute review
- No preparation needed
- One integration mapped
AI IN THE WORKFLOW
An agent works from a verified customer record.
An example of what we can scope with your team. The tools and automation boundaries follow your workflow.
Resolve identity
Match the customer across systems using defined identifiers.
Assemble context
Expose only the fields and operations the workflow needs.
Verify the write
Validate payloads, handle retries and record the result.
What you take away
A data contract, ownership map and tested integration path.
Depending on fit: CRM & ERP APIs · webhooks · Power Platform · MCP
The example shows the technical approach. See scope, fit and ways to engage us. Scope and price are agreed before implementation.
Common Breakpoints
Systems don't fail because they disconnect. They disagree.
These can look like isolated bugs. Often the deeper problem is unclear ownership or coordination.
System says
CRM: "approved"
Reality
Support: "pending"
System says
Order: "shipped"
Reality
Warehouse: still packing
System says
Payment: "confirmed"
Reality
Finance: still reviewing
Data conflicts
The assumption01
"If systems are connected, they stay in sync"
In production
The order shows "shipped" in Shopify but still "pending" in ERP
Root cause
Each system updates state on its own schedule
The assumption02
"Matching fields means matching records"
In production
Someone edits records every morning to "fix" data across three systems
Root cause
Each system defines status, stages, and labels differently
Process conflicts
The assumption03
"If the API returned 200, it worked"
In production
A refund processes in Stripe but the invoice still shows "Paid" in accounting
Root cause
Later events invalidate earlier decisions silently
The assumption04
"More automation removes manual work"
In production
Two automations update the same field and overwrite each other every 10 minutes
Root cause
Triggers act without shared state awareness
Systems share data, not authority
The Model
Three forces that must stay coordinated
Use these three responsibilities to locate coordination gaps, alongside checks for access, data quality and implementation defects.
Tap a force to simulate its failure
Status: coordinated · decisions on current data · actions reaching every system · state in sync
Data Flow
The data flow model
Start with ownership and timing, then check data contracts, access and failure handling.
The pipeline · one change, five gates
01 / 05
Gate 01
Source of Truth
One system owns the truth
Every piece of data has exactly one authoritative system. This is where the record is created, updated, and owned. All other systems receive copies.
Ownership first · Then timing · Duplicates never
Real Scenarios
Same situation, two outcomes
Illustrative scenarios. Compare the failure with a designed response; sync targets depend on the systems and scope.
Situation
Customer places an order. Warehouse discovers the item is out of stock 2 hours later.
Without architecture · breaks
Customer receives order confirmation. ERP gets updated manually. Warehouse sees the order but can't fulfill it. Customer gets confused "shipped" email 3 days later, then cancellation email. No one knows who saw what when.
With architecture · holds
Order event is recorded → Warehouse checks availability → Confirmed status propagates to ERP and CRM → Customer receives an accurate update. Retries and reconciliation identify delayed or failed updates.
How This Becomes an Implementation
Integration architecture turns into platforms, API layers, AI-ready data flow, or production infrastructure depending on the operating constraint.
Build path02
AI-ready data flow
When AI needs context from multiple tools, the integration layer prepares source-of-truth access, enrichment, and decision handoff paths.
Build path03
Cloud integration infrastructure
Queues, workers, APIs, storage, identity, and deployment environments make cross-system workflows reliable under real load.
Build path04
Production delivery controls
Observability, retries, rollback paths, and release discipline keep data flow predictable once integrations become operationally critical.
Design Tradeoffs
Why these patterns, not others
Patterns to evaluate, not defaults for every project. Choose against latency, failure tolerance, data ownership and the operational cost your team can support.
ADR-001 · integration architecture
AcceptedEvent-driven over request-based
Decision
Systems announce changes via events
Rejected alternative
Systems ask each other for data directly
Rationale
Events suit asynchronous work that must tolerate a consumer outage. Durable queues and replay need explicit design. A direct request can be simpler when an immediate response is required.
Cost accepted
Tracing crosses consumers and needs correlation IDs. Queues, replay and reconciliation add operational cost; use them when the resilience benefit justifies it.
Integration-Backed Case Studies
Named platform and automation implementations where data flow, synchronization, and integration reliability changed how the operation worked day to day.
Who this is forCTO, Head of Business Systems, VP Engineering, or COO — anyone accountable for systems that have to agree with each other.
Flagship result · RetailMax Holdings
99.7% inventory accuracy; 45% fewer stock-outs
Real-time inventory platform across 127 locations
GiveFlow
Recurring donation operations platform
Donation workflows, donor visibility, and reporting were rebuilt into a platform instead of spreadsheet-driven coordination.
Measured outcome
$500K recurring revenue and 85% donor retention.
Go Boon
Referral and hiring platform
Referral submission, rewards, and tracking were turned into a structured SaaS workflow for hiring teams.
Measured outcome
Referral rate grew from 2% to 11% and cost per hire dropped by 42%.
Capability Map
The architecture across capabilities
Integration is one part of the system. Here is how it connects to everything else.
Solutions built on this capability
The capability stack · five disciplines, one system
- 01
Automation
Handles execution
Runs the defined processes — triggers, decisions, actions, and verifications.
Explore - 02
AI Systems
Handles judgment
Evaluates situations and chooses actions based on patterns, data, and confidence.
Explore - 03You are here
Integration & Platforms
Handles synchronization
Keeps systems consistent — so decisions are based on current data and actions reach every affected system.
- 04
Product Engineering
Builds the product
When connected systems need their own operating surface — portals, platforms, and internal tools.
Explore - 05
Reliability
Keeps it running
Error handling, monitoring, logging, and escalation that keeps everything running.
Explore
One team runs all five
See how the five capabilities work as oneScope & fit
Start with the work you need done.
We check API access, vendor limits, record identifiers, data quality and a named owner for each system. Missing access or disputed ownership needs resolving before the build.
CRM/ERP Integration
One agreed data flow, with explicit ownership and a verifiable result.
A bounded workflow may fit Automation Sprint. Multi-system programmes need discovery and a quote covering contracts, reconciliation and exception handling.
Explore CRM/ERP IntegrationSomething already broken? See System Rescue. Repairs are scoped to the existing failure.
BEFORE YOU GET STARTED
Integration architecture questions
Scope, delivery and what happens next. The details that help you decide.
Can event-driven systems guarantee instant consistency?
No. Distributed systems exchange messages asynchronously — there is always a window, milliseconds to seconds, where two systems show different states. Strong consistency requires locking all systems simultaneously, which kills availability and throughput. Instead, we define acceptable consistency windows per data type: customer-facing data may need sub-5-second convergence, while internal reporting can tolerate minutes.
Do integration pipelines validate the data they move?
The event bus guarantees delivery and ordering. It does not check whether the data makes business sense — a pipeline will happily sync an invoice with a negative amount if the source system allows it. Each system validates its own inputs at the application boundary: integration moves data reliably; applications judge data correctness.
What happens when two systems produce conflicting updates?
Within the same consistency window, automated rules handle simple cases — last-write-wins, version comparison, timestamp ordering. But business-critical conflicts require judgment that rules cannot encode, so we design escalation paths for conflicts automation can't resolve: the system flags ambiguity, and a human makes the call.
What does AI-powered mean for this service?
Build the APIs, data ownership rules and event flows that let people and agents work across CRM, ERP and internal tools without guessing which record is right.
Will AI make changes without our approval?
We agree the permissions and execution boundaries before implementation. Routine actions can be automated within those limits; sensitive changes require the agreed approval step. Access controls, tests and activity records remain part of the system.
Can you work with our current tools and data?
We review your existing stack, access and data requirements first. Tool selection depends on fit, licensing and deployment constraints. We agree what data can be sent to a model and where it can be processed before connecting it.
What will we receive, and how is it priced?
A data contract, ownership map and tested integration path. Scope, acceptance checks and price are agreed before implementation. Model usage, cloud costs and third-party subscriptions are identified separately. Ongoing monitoring and support are included only when agreed.
Start here
If your systems disagree about what's true
The patterns on this page explain why. The next step is mapping them to your specific systems.
Most companies reach this point after months of patching integrations. CRM / ERP integration · Operations automation · Automation architecture