AI integration services for the systems you already run

Common Questions About AI Integration Services

What do AI integration services actually involve?

Connecting AI to the systems your business already runs: building the connectors and pipelines that move data, the AI layer that reads and acts on it, and the governance around both. Delivered examples include 15+ marketing platforms unified into one analytics system, a WhatsApp agent wired into live order and delivery systems, and field operations integrated with billing for 2.5 million recovery cases.

Do we need to replace any of our existing systems?

No. Integration means your CRM, ERP, storefront, and document systems stay exactly where they are; we build the layer that connects AI to them. If a system genuinely blocks the goal, we will say so during the system mapping phase, before you have committed to a build.

Can you integrate with legacy or custom-built systems?

Yes, and that is usually where the value hides. The K-Electric project integrated a new field platform with existing billing systems, and our document projects routinely ingest from legacy repositories. Where no API exists, we build the access layer, with the rate limiting and failure handling production systems need.

How do you keep AI integrations secure?

Every connection gets role-based access controls, full audit logging of reads and writes, and encryption in transit and at rest. Source-system permissions are preserved in the AI layer, and we deploy in your cloud, VPC isolated, or on-premise where compliance requires it.

How long does AI integration take and what does it cost?

Two to four months and $50,000 to $125,000 fixed price, depending on how many systems are involved, data complexity, the depth of the AI layer, and governance requirements. The InsightsBot integration across Snowflake, BigQuery, and Postgres shipped in 4 months end to end.

What's the difference between AI integration services and intelligent process automation?

Integration is about connecting AI to your systems so it can read and act on real data instead of generic knowledge. Process automation is about running an entire workflow end to end, document checks, support replies, compliance reports, with exception handling built around it. Most automation projects need integration as a first step; integration on its own does not require building a full automated workflow on top.

How many systems can realistically be connected in one project?

It depends on the systems, not a fixed number. We unified 15+ marketing platforms into one analytics system on one project; other projects connect 3 to 5 systems, like a CRM, an order system, and a helpdesk. System count and variety is one of our pricing factors, and we scope it against your actual stack during system mapping, not a generic estimate.

What happens if one of the connected systems changes its API or goes down?

We engineer for this from the start: retries, rate limiting, and failure recovery are part of every integration build, not an afterthought. Monitoring dashboards and runbooks flag when a connection breaks so your team knows immediately, and the AI layer degrades gracefully rather than silently returning wrong answers.

Do you replace tools like Zapier or Workato, or work alongside them?

Generic iPaaS connectors are built for simple, high-volume syncs between popular SaaS tools; they don't handle your custom or legacy systems and they don't carry AI logic. We build the AI layer itself, with the integration logic built in rather than bolted onto a connector, and we specifically cover the legacy and custom systems those tools skip. If you already use an iPaaS tool for simple syncs, we can leave that in place and build the AI-specific connections around it.