AI Integration Services
Add LLM, retrieval, and assistant capabilities to the software and workflows your team already uses.
Use cases worth assessing
- 01
An assistant inside an existing SaaS application.
- 02
Intelligent search over approved internal knowledge.
- 03
Document analysis connected to existing approval and review tools.
What the engineering scope includes
- 01
Review of APIs, authentication, data boundaries, and integration options.
- 02
A scoped model or retrieval integration with an application interface.
- 03
Error handling, rate limits, provider failure and fallback considerations.
- 04
Tests, operational documentation, and a staged release plan.
Build, evaluate, deploy, improve.
Each phase answers a different risk: business fit, data readiness, system design, AI behaviour, and operational reliability.
Discuss your AI projectDiscover
Define the users, workflow, data, constraints, and what success should mean.
Architect
Plan the application, model integrations, data access, permissions, and evaluation approach.
Prototype
Test a focused workflow with representative inputs before committing to a full build.
Build
Implement the product, integrations, interfaces, and observable AI workflow.
Evaluate
Check task quality, failure cases, access controls, latency, and running cost.
Deploy & improve
Release with monitoring, operational documentation, and a plan for iteration.
Questions about this service
AI Integration Services
Will we need to replace our existing application?
Not necessarily. We first assess APIs, architecture, permissions, and data access. The goal is a useful integration within the current product where that is technically appropriate.
Can you integrate more than one model provider?
Multiple providers can be considered when they serve a clear requirement. Routing and fallback behaviour add complexity and should be evaluated against quality, cost, and operational needs.
What happens to our customer data?
We identify what data enters the model or retrieval layer and agree access controls, storage, and provider terms. Data handling is scoped before integration; compliance is not assumed from a product label.
How do you handle provider outages or limits?
We plan timeouts, retries where safe, clear errors, and suitable fallback behaviour. The exact approach depends on the operation and the provider.
How is integration work priced?
The existing application, APIs, data preparation, UI changes, and evaluation requirements determine scope and cost. A discovery phase can resolve uncertain dependencies.
Related AI services
- 01
AI Agent Development
Tool-using AI agents for bounded tasks, approved data sources, and multi-step workflows.
- 02
AI Workflow Automation
Connect business tools and automate repetitive operational work with visible decisions and human checkpoints.
- 03
AI MVP & SaaS Development
Validate a focused AI product and build the application, data flow, and operating model around it.
Discuss your AI project.
Share the workflow, product, or operational problem you want to improve.
Or email hello@arraybros.com
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