AI integration services
You don't need to rebuild your software from scratch to use AI. You need someone who can wire it into the systems you already run. We integrate AI into existing applications, workflows, and data pipelines so your team gets the benefits without a year-long platform migration.
SportHub
Flustr
Armor Up America
Rundivo
RAE HealthWhy AI integration is harder than it looks
The AI part is usually the easy piece. The hard part is getting it to work with everything else.
Your CRM has 10 years of customer data in a format nobody fully understands. Your ERP talks to three other systems through an API that was written in 2017 and hasn't been touched since. Your support tool runs on a platform with limited webhook support. And now someone wants to “add AI” to all of it.
That gap between a working AI model and a production system that actually uses it? That's what we close. We handle the API design, the data plumbing, the authentication layer, the error handling, and the monitoring that makes AI integration work in real business environments, not just in demos.
How to integrate AI in your business: guide to AI integration →
What we integrate
LLM integration services
Connecting OpenAI, Claude, Llama, Mistral, or Gemini APIs to your applications. We handle prompt management, response parsing, token cost optimization, rate limiting, fallback logic, and caching. The model is an API call; everything around it is engineering.
ChatGPT and GPT integration services
Custom GPT integrations for your internal tools, customer-facing apps, or back-office systems. Not the plugin-store kind. We’re talking about controlled, production-grade integrations where the model works within your data boundaries and your business rules.
AI-powered search and knowledge bases
Replacing keyword search with AI that actually understands what users are asking. We integrate retrieval-augmented generation (RAG) systems into your existing applications so your help docs, product catalogs, or internal wikis become genuinely searchable.
RAG development services→Machine learning integration
Plugging trained ML models (classification, prediction, recommendation, anomaly detection) into your existing pipelines. Whether the model runs on SageMaker, Vertex AI, or a custom Python service, we build the API layer and integration logic that connects it to your production systems.
AI agents in existing workflows
AI agents that live inside your current tools and take action on behalf of your team. Filing support tickets, routing orders, flagging anomalies in incoming data, drafting responses for human review. The agent uses your systems; your team supervises the output.
What is an AI agent? A business leader’s guide→AI data integration
Connecting your data sources to AI models and keeping them in sync. This includes ETL pipelines for AI training data, vector database indexing for RAG systems, real-time data streaming for live inference, and batch processing for periodic model updates. Your data is probably scattered across five different systems. We unify it into something an AI model can use.
Our AI integration process
System audit and integration strategy
We map your current architecture: what systems you run, how they communicate, where the data lives, and what APIs are available. Then we design an integration strategy that specifies exactly where AI fits, how it connects, and what needs to change (and what doesn’t). This usually takes 1 to 2 weeks and gives you a document you can act on even if you don’t hire us to build it.
API design and data pipeline setup
We build the integration layer: REST or GraphQL APIs, message queues, webhooks, data transformation logic, and authentication. If your existing APIs need updates to support AI-driven workflows, we handle that too. The goal is a clean boundary between your existing systems and the new AI components.
AI component development
We build (or configure) the AI service itself: the LLM orchestration, the ML model serving infrastructure, the RAG pipeline, or the agent framework. This component is designed as a self-contained service that communicates with your systems through the integration layer.
Integration testing
AI systems behave differently from deterministic software. The same input can produce slightly different outputs. We test for accuracy, latency, edge cases, cost per call, and failure modes. We also verify that the integration handles downtime gracefully: if the AI service is unavailable, your core systems keep running.
Deployment and monitoring
We deploy to your infrastructure and set up monitoring dashboards that track AI-specific metrics: response quality, inference latency, token usage and cost, error rates, and data freshness. When something drifts, you see it before your users do.
Handoff and documentation
Your team gets complete documentation: architecture diagrams, API specs, runbooks, and a guide to common maintenance tasks. If you have in-house engineers, they can manage the system independently. If you want ongoing support, we offer that too.
Data readiness: the hidden requirement for AI success→Common AI integration patterns
API-based integration
The simplest pattern. Your application calls an AI service through a REST API, gets a response, and uses it. Good for: content generation, classification, summarization, translation. We add caching, retry logic, and fallback handling so it works reliably at scale.
Event-driven integration
AI runs in response to events in your system: a new support ticket arrives, an order is placed, a document is uploaded. We use message queues (Kafka, RabbitMQ, SQS) to trigger AI processing asynchronously, so your main application stays fast and the AI work happens in the background.
Embedded AI
The AI model runs inside your application, not as a separate service. Good for: real-time recommendations, on-device inference, low-latency requirements. We deploy smaller models (or distilled versions of larger ones) directly into your application stack.
Human-in-the-loop
AI generates suggestions, drafts, or classifications, but a human reviews and approves before anything goes live. Good for: content moderation, medical document review, financial compliance, anything where accuracy matters more than speed. We build the review interfaces and approval workflows alongside the AI integration.
Systems we integrate AI with
CRM and sales
Salesforce, HubSpot, Pipedrive, custom CRMs
ERP and operations
SAP, Oracle, NetSuite, Odoo, custom ERPs
Support and helpdesk
Zendesk, Freshdesk, Intercom, Jira Service Management
Content and knowledge
Confluence, SharePoint, Notion, Google Workspace, custom wikis
E-commerce
Shopify, Magento, WooCommerce, custom platforms
Databases
PostgreSQL, MySQL, MongoDB, SQL Server, Redis, Elasticsearch
Cloud platforms
AWS (Bedrock, SageMaker, Lambda), GCP (Vertex AI), Azure (OpenAI Service)
Communication
Slack, Microsoft Teams, email systems, custom messaging
Data infrastructure
Apache Kafka, Airflow, Spark, dbt, Snowflake, BigQuery
AI/ML services
OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Hugging Face
If your system isn't listed, that doesn't mean we can't integrate with it. If it has an API or a database, we can connect to it.
AI integration by industry
Healthcare
Integrating AI into EHR/EMR systems, clinical decision support tools, and patient communication platforms. We work within HIPAA constraints, which means careful handling of PHI, audit logging, and on-premise deployment options.
Applications of machine learning in healthcare→Financial services
Connecting AI models to trading platforms, risk management systems, compliance workflows, and customer onboarding tools. Financial systems have strict uptime and accuracy requirements, and we build integration layers that meet both.
E-commerce
AI that connects to your product catalog, order management system, and customer data platform. Personalized recommendations, dynamic pricing, intelligent search, and automated product descriptions that pull from your actual inventory data.
How generative AI in ecommerce is changing the industry→Logistics and supply chain
Integrating demand forecasting models with warehouse management systems, connecting route optimization AI to fleet tracking platforms, and automating freight document processing. These integrations need to handle high data volumes and tight timing windows.
SaaS platforms
Adding AI features to your product so your users benefit directly. Smart search inside your app, automated categorization, AI-assisted onboarding, natural language querying of your platform’s data. We build these as API services that your frontend team can call.
Why Attract Group for AI integration
We’re software engineers first.
AI integration is 20% model work and 80% systems engineering: APIs, data pipelines, authentication, error handling, monitoring. We’ve been doing that 80% for 15 years. The AI piece is new; the engineering discipline behind it isn’t.
We know your systems.
We’ve built and integrated with CRMs, ERPs, healthcare platforms, e-commerce backends, and logistics tools across dozens of projects. That experience means fewer surprises when your Salesforce instance has a non-standard schema or your legacy API returns XML in 2026.
We don’t rip and replace.
Some firms will tell you to rebuild everything on a new platform. We integrate AI into what you have. Your team keeps working in the tools they know, and the AI works behind the scenes (or right alongside them). If a system needs modernizing first, we’ll tell you, but we won’t force a rewrite when an integration will do.
You own the integration.
The code, the API specs, the deployment scripts. No proprietary middleware, no dependency on our platform. Your engineering team can maintain, extend, or replace any component we build.
We scope honestly.
If connecting AI to your system would take 2 weeks, we won’t sell you 2 months. If your data isn’t ready for AI, we’ll tell you that before you spend money on an integration that won’t produce good results.
What does AI integration cost?
Every integration is different, but here's a framework:
Single-point AI integration
$10,000 to $25,000
2-4 weeks
One AI capability connected to one system. Example: adding GPT-powered draft responses to your Zendesk instance, or connecting an LLM summarizer to your Confluence knowledge base. Straightforward data flow, limited custom logic.
Multi-system AI integration
$30,000 to $80,000
1-3 months
AI connected to multiple internal systems with shared data layers. Example: a customer support agent that pulls from your CRM, help docs, and order system to generate context-aware responses. Requires API orchestration, data unification, and more testing.
Enterprise AI integration
$80,000 to $200,000+
3-6 months
Large-scale integration across multiple business units, data sources, and user groups. Example: embedding AI across your entire operations platform with role-based access, custom dashboards, model monitoring, and compliance controls.
The biggest cost drivers: the number of systems involved, the quality of existing APIs, and whether you need real-time or batch processing.
How much does custom AI agent development cost? →What clients say
Frequently asked questions about AI integration services
Start with a conversation about your systems
Tell us what you're running and what you wish it could do. We'll map out how AI fits into your current setup, what it'll take to get there, and whether the investment makes sense. No pressure, no jargon overload.
Ready to integrate AI into your systems?
Tell us what you're running and what you need. We'll respond with an integration plan and realistic timeline.