AI integration services: add AI to the software you already run, without replacing it

AI integration typically costs $10,000 to $30,000 to connect one system, $30,000 to $80,000 for an integration spanning several systems, and from $80,000 for a platform level integration with shared services and governance. The biggest drivers are API quality, the number of systems that receive writes, data clean up and compliance scope. Model usage is billed at cost on top. Vrisic gives you one fixed price in writing after a free scoping call.

Common examples include a CRM that summarises every account and drafts the next email, a service desk that classifies and routes tickets before a person reads them, an ERP that reads supplier invoices and matches them to purchase orders, a store that writes product copy from supplier sheets, and an internal app that answers questions about your own database in plain English. In each case the AI works inside a system the business already uses.

We add a small integration service beside your application rather than inside it. The service listens for events or exposes an API, calls the model, validates the output against a schema and writes back through your existing API with the same permission checks. Your core code changes only where a button, panel or webhook is added. That keeps the blast radius small and makes the AI easy to switch off.

LLM integration is the engineering work that connects a large language model, such as the GPT 5 family, Claude or Gemini, to a business system so it can read the right data, produce a structured result and hand that result back safely. It covers authentication, prompt and context assembly, retrieval, output validation, error handling, logging and cost control.

Use the vendor’s built in AI when the job lives entirely inside that one product and its standard features fit. Salesforce Agentforce, Microsoft Copilot in Dynamics 365 and HubSpot Breeze are strong inside their own walls. A custom integration makes sense when the task spans several systems, needs your own rules or models, or when per seat or per action pricing becomes expensive at your volume.

The AI reads ERP data through supported interfaces, such as OData services on SAP S/4HANA or SuiteTalk REST and SuiteScript on NetSuite, and prepares proposed entries like matched invoices, coded expenses or reorder suggestions. Writes go back through the same interfaces, usually as drafts a finance user approves. We never write directly to ERP database tables, because that bypasses validation and can void vendor support.

An MCP server exposes a system’s actions and data to AI assistants through the Model Context Protocol, an open standard that Anthropic introduced in 2024 and that is now governed by the Linux Foundation’s Agentic AI Foundation. You need one when several AI clients or agents should use the same tools under the same permissions. For a single feature in one application, a direct API integration is usually simpler.

Usually, yes. Options in order of preference are a read replica or reporting database, scheduled file exports, an existing message queue, a thin API we build in front of the old system, and as a last resort robotic process automation on the user interface. We choose the most stable route available and keep AI logic outside the legacy code so the old system does not need to change.

A single system integration takes 3 to 6 weeks, a multi system integration 6 to 12 weeks, and a platform level programme 3 to 6 months. Most delays come from waiting for API credentials, sandbox environments, security approval or a data owner’s sign off rather than from engineering. We list every access request in week one.

It should not, if the integration is designed properly. AI calls run asynchronously or in a separate service, so a slow model response never blocks a checkout, a ticket save or a posting run. We respect the platform’s API limits, add timeouts and fallbacks, and launch behind a feature flag. If anything misbehaves, the flag turns the AI off and the system carries on as before.

Only the minimum needed for each task, and only through business API tiers whose standard terms say API data is not used to train models. We can redact personal data before a call, route sensitive workloads to Azure OpenAI, AWS Bedrock or Google Vertex AI in a chosen region, or run an open weight model inside your own network when data must not leave it.

Keep three things in your hands: the code, the prompts and evaluation sets, and the choice of model. We put every model call behind a gateway so providers are swapped by configuration, store prompts in your repository, and expose tools through open standards such as the Model Context Protocol. You own the result outright and can move it to another team at any time.