AI Services
AI Integration for Enterprise Systems
Most enterprise AI integration projects fail not because of the AI, but because of the integration layer. The model performs fine in testing. It breaks when it meets your actual data: inconsistent field formats, missing values, systems that were never designed to talk to each other, and business logic that exists only in the heads of the people who built the original platform.
We have built AI integrations on top of SAP, NetSuite, Salesforce, HubSpot, custom PHP platforms, WooCommerce, and combinations of all of the above. The work is not glamorous. It involves mapping data schemas, writing transformation logic, handling errors that surface only under real load, and building monitoring so you know when something breaks before your operations team does.
100+
Production systems
API-first
Integration approach
Zero migration
Platform replacement required
Technology
Tools and platforms we work with
We select the right stack for your project. These are the platforms and technologies relevant to this service.
OpenAI
Claude
Python
REST
GraphQL
SAP
NetSuite
Salesforce
HubSpot
PostgreSQL
AWS
Docker
What we deliver
Core capabilities
API and protocol integration
Connect AI to ERP, CRM, and operational platforms via REST, GraphQL, SOAP, webhooks, or direct database access.
Data transformation layer
Map inconsistent schemas, handle missing values, and normalize data before it reaches the AI model.
Production error handling
Rollback logic, retry patterns, and monitoring so integrations fail safely instead of silently.
Operational monitoring
Alerting when outputs fall outside expected ranges, before your operations team finds out from customers.
How we work
Our process
1
Discovery
Map systems, data flows, and edge cases. Produce a specification before any code is written.
2
Integration build
Develop the highest-value data flow first, then expand to additional systems and use cases.
3
Testing
Validate against real production data patterns, not just clean test datasets.
4
Go-live and monitor
Deploy with alerting in place so anomalies are caught before they become production problems.
Ready to connect AI to your enterprise stack?
Tell us which systems you need to integrate. We will respond with a realistic scope and timeline.
Deep dive
How this service works in detail
What "integration" actually means
Adding an AI layer to an existing enterprise system means three things. First, the AI needs to read from your systems, pulling order data, customer records, inventory levels, or whatever the model needs to do its job. This requires either reading directly from databases or consuming API endpoints, and it requires understanding which data is reliable and which is not.
Second, the AI needs to write back, updating records, triggering workflows, or sending notifications based on what it processes. This is where most integrations break, because writing to a production system requires exactly the kind of error handling and rollback logic that prototypes skip.
Third, the whole thing needs to run in production without supervision. That means monitoring, alerting when outputs fall outside expected ranges, and a clear process for handling cases the AI cannot confidently resolve.
Systems we integrate with
We have built AI layers on top of: ERP systems (SAP, NetSuite, custom builds), CRM platforms (Salesforce, HubSpot, custom CRMs), eCommerce platforms (Shopify, WooCommerce, custom), point-of-sale systems, procurement and inventory systems, attendance and payroll platforms, and combinations where data needs to flow across multiple systems simultaneously.
For each integration, the first question we answer is: which system is the source of truth for each data type? That decision shapes every technical choice that follows.
What a project looks like
Discovery: we review your current systems, map the data flows, and identify where the integration points are and what edge cases exist. This is usually two to four weeks and produces a specification before any code is written.
Build: we develop the integration layer, starting with the highest-value data flow and expanding from there. We do not try to automate everything at once.
Monitoring: before go-live, we set up alerting so that when the integration encounters something unexpected, someone is notified before it becomes a production problem.
FAQ
Frequently asked questions
How long does an enterprise AI integration project take?
Discovery and specification: two to four weeks. Build and testing: four to twelve weeks depending on the number of systems and the complexity of edge cases. Total: three to four months for a well-defined scope.
Do we need to replace any of our existing systems?
No. The integration layer sits on top of your existing stack and reads from or writes to it via API or direct database connection. We do not require you to migrate data or replace platforms as a condition of the project.
What happens when the integration produces a wrong output?
Every integration we build includes a review layer for uncertain cases and a monitoring system that flags anomalies. We do not build integrations that operate without any human oversight for consequential decisions.
What data formats and protocols do you work with?
REST, SOAP, GraphQL, direct SQL, webhooks, flat file exchange (CSV/XML), and custom proprietary protocols. The protocol is rarely the constraint. The data quality and schema consistency usually are.
How do you handle data privacy for sensitive enterprise data?
Data handling depends on what the AI model requires. Where possible, we process data within your existing infrastructure. Where external API calls are needed, we review data minimization options and work within your data governance requirements before the build begins.
Ready to scope your project?
Tell us about your systems, workflows, or site. We will respond with a realistic plan and timeline.
Request a consultation