AI Integration Services

This is the integration problem InTech solves. We connect your systems so your teams have one source of truth, your workflows run automatically, and your AI capabilities actually work.

Why Integration Matters Now

The complexity of your software stack is not a bug. It's the operating environment. The average company uses between 106 and 275 SaaS applications. Most of those applications are not talking to each other. Salesforce data suggests that only 29% of the software tools a business runs are actually integrated.

This fragmentation creates a compound cost:

• Manual work happens because systems don't exchange data automatically. Your customer success team looks up a customer in the CRM, then logs into the accounting system to check their invoice history, then navigates to support to see tickets. That's three systems, three contexts, three minutes per lookup.
• Data quality suffers when information is re-entered across systems. One inconsistency becomes a cascading problem.
• AI capabilities that depend on clean, connected data never get built, because the data isn't there.

Companies with well-integrated data are 23 times more likely to acquire customers, 6 times more likely to retain them, and 19 times more likely to be profitable. That's not a technology outcome. That's a business outcome.

How InTech Approaches Integration

We don't start with connectors. We start with the data layer.

Clean, connected, authoritative data is the prerequisite for everything else: for workflows, for AI, for visibility. Once your data layer is sound, the rest follows.

Our approach:

Data layer first. We map your authoritative data sources: what's the single source of truth for customers, orders, inventory, financial records, etc. If you have multiple sources saying different things, we establish hierarchy and reconciliation rules.

API-first where possible. If your systems have good APIs, we build connectors that sync data automatically. This is the cleanest path because you're using the systems as designed.

Event-driven for real-time flows. When data needs to move immediately (an order placed triggers fulfillment and invoicing), we build event-driven architectures so systems react to state changes in real time rather than polling for changes.

ETL and data pipelines where batch sync is appropriate. Not everything needs to move in real time. Some data is best synchronized nightly or on a schedule. We design pipelines that are transparent, idempotent, and easy to debug.

Custom middleware when off-the-shelf connectors don't fit. Integration tools like Zapier and n8n cover the common cases well. But your workflow is probably not common. We build custom logic that bridges the specific gaps in your stack.

Common Integration Patterns We Build

CRM to accounting. Sales closes a deal in the CRM. That data needs to flow to your accounting system to create an invoice and update revenue records. We eliminate manual entry and get your finance team a single authoritative source for customer contracts and ARR.

Customer communication to operations. Support tickets, customer emails, and call logs live in a communication platform. But the operations team in fulfillment or field service needs context about what the customer actually needs. We connect support systems to operational databases so that every team member has the full picture.

Field operations to billing. A technician completes a job on site. That completion event should trigger an invoice, update inventory if parts were used, and log the work history. We build the connectors that turn task completion into billing and visibility.

Inventory to ordering to fulfillment. Your inventory system needs to talk to your ordering system (so you don't oversell) and your fulfillment system (so pickers know what to ship). We build the single source of truth that coordinates all three.

AI data pipelines. You want to build an AI feature: a recommendation engine, a predictive analytics dashboard, or a workflow that routes customers intelligently. That AI needs clean data from multiple sources. We build the pipelines that extract, transform, and load data so your models have what they need.

The CRAFT Methodology for Integration Projects

We use the same discipline for integration work that we use for product development.

Before we design architecture or write a line of code, we define what integration success looks like. Not the technical spec: the outcome. What workflows change? What data is now authoritative? How do you know it's working? This contract becomes the north star for the project.

Disciplined architecture. We design for the data you have today and the scale you'll reach. We choose technologies that are boring, proven, and maintainable. We document the data model so the next engineer understands what's happening.

Telemetry from day one. Integration projects succeed when the integration actually stays working. We instrument the data flows so we can see when records fail to sync, when data quality degrades, when a connector goes stale. This gives us and your team visibility before users are impacted.

Delivery Models

We deliver integration work through three pod structures.

A focused integration project with a defined scope. One to two systems connected, clear success metrics. You get a working integration, documentation, and a handoff to your team for ongoing operation.

Build Pod (predictable monthly retainer). Ongoing integration development. This is the right fit if you're building a connected product or expanding your integration surface area over time. We work alongside your team, building connectors and data pipelines on a predictable cadence.

Scale Pod (predictable monthly retainer). Full integration as part of a larger product engagement. This works if integration is part of a bigger engineering effort: if you're building a new product with AI capabilities, for example, and integration is a prerequisite.

All integrations use your infrastructure. Your GitHub repos, your Railway environment, your Supabase database, your Cloudflare edge. You own the code and the data.

Frequently Asked Questions

How long does a typical integration project take?

It depends on complexity. A straightforward CRM-to-accounting connector might take 4-6 weeks. A multi-system data pipeline with transformation logic might take 12 weeks. We scope this explicitly in the Intent Contract so there are no surprises.

Do we need a data warehouse?

Not always. If your integration needs are limited to connecting a few SaaS tools, a data warehouse adds cost and complexity. If you're building AI capabilities or need to analyze data across many sources, a warehouse or data lake becomes valuable. We'll recommend the right approach based on your architecture.

What if one of our systems doesn't have an API?

This is more common than it should be. We have options. For some systems, we can scrape data or work with database-level access. For others, we build custom middleware that handles the constraint. Sometimes the answer is to migrate away from the constrained system, and we can help with that decision.

What happens after you hand off the integration?

We document everything: the architecture, the data model, the connector logic, the monitoring setup. Your team takes it from there. If you want ongoing support, that's what the Build Pod is for. If you want us to check in quarterly, we can do that too. The integration is yours.

How do we know if the integration is working?

Telemetry. We set up dashboards and alerts so you can see sync failures, data quality issues, and performance metrics in real time. We also recommend a process review 30 days after launch to make sure the integration is delivering the outcomes defined in the Intent Contract.

What's the difference between integration and middleware?

Integration is the outcome: your systems working together. Middleware is part of how you get there. We might build custom middleware that acts as the integration glue between two systems. But integration is the strategy, middleware is the tactic.