Connect AI capabilities to your existing software stack, APIs, and business systems.
Integration work is what stands between a model that performs well in a demo and a model that changes how a company operates on a Tuesday. The technical content is unglamorous: authentication against systems that predate the project, queues that survive a failed request, a schema that lets a result be written back where somebody will see it, permissions that stop the wrong person reading the wrong record, and a log that lets you reconstruct what the system did three weeks ago when a customer complains.
Most failed AI projects do not fail at the model. They fail at this layer, because the pilot ran against a clean export and production runs against a live database with four kinds of duplicate record in it. An integration agency earns its fee by taking the pilot apart and rebuilding the parts that only worked because somebody was watching. That is why the discovery phase of an integration engagement spends more time on your existing stack than on anything new.
The question to ask every candidate is what they do when the model is unavailable or wrong. A system that calls an external provider inherits that provider rate limits, outages, deprecations, and price changes. Good integration work answers for all four before launch: a cached fallback, a retry policy, a human review path for low-confidence results, and a wrapper thin enough that swapping providers is a configuration change rather than a rebuild. A firm that has no answer has not run one of these in production.
The agencies in this category work across the common enterprise platforms as well as bespoke internal systems. Read each listing for the services named and the city the firm works from, and ask for a reference engagement in a stack that resembles yours. Integration skill transfers less cleanly between platforms than the proposals usually suggest.
What buyers ask most before hiring an AI Integration partner, answered in plain terms.
An AI integration agency connects AI capabilities to your existing business systems — CRMs, ERPs, databases, or SaaS tools. They specialize in embedding AI into your current tech stack rather than building standalone systems. Common integrations include adding an LLM layer to Salesforce, connecting a chatbot to Zendesk, or building a RAG pipeline over your internal knowledge base.
AI integration projects typically cost $10,000 to $75,000 depending on the number of systems involved, data volume, and security requirements. Single-system integrations (one CRM or helpdesk) run $10,000–$25,000. Multi-system integrations with custom middleware run $25,000–$75,000.
A single-system AI integration takes 4–8 weeks. Multi-system integrations with custom API development take 8–16 weeks. Enterprise-grade AI integration programs with security reviews and compliance requirements take 3–6 months.
AI integration agencies work with API-first LLM platforms (OpenAI, Anthropic, Google), integration middleware (MuleSoft, Boomi, custom APIs), vector databases for RAG pipelines, and iPaaS platforms (Zapier, Make, n8n) for workflow orchestration. Most have pre-built connectors for common SaaS tools.
You need an AI integration agency if you want to add AI capabilities to existing systems without rebuilding them — connecting an LLM to your CRM, adding AI search to your intranet, or embedding document analysis into your ERP. Choose a custom AI development agency when you need a net-new AI product or infrastructure.