For product and engineering teams
AI development for SaaS.
Add assistants, knowledge search, and connected actions to your SaaS product. We work alongside your engineers on implementation, testing, and technical handover.
Add capability without losing control.
Your team knows the product and its users. We contribute AI engineering where you need additional implementation capacity or a focused technical review.
Engagements are project-based and quoted against agreed requirements. We scope the integration into your application, the evaluation work, and the handover together.
Three ways we can help.
Build an AI feature
Develop an assistant or workflow around a product task. Connect approved sources and APIs, define input and output boundaries, and provide a route to human review when the feature cannot reliably complete the task.
Scope: feature implementation, integration code, representative task evaluation, and technical handover.
Improve retrieval and integrations
Retrieval-Augmented Generation (RAG) connects a language model to relevant information from approved sources. We work on how your feature retrieves that information and uses tools. Define content access, retrieval relevance, tenant boundaries, refresh behavior, and handling for unavailable or incomplete sources.
Scope: agreed retrieval or integration changes, test cases, and operating documentation.
Review an existing AI experience
Examine the feature against an agreed risk and reliability scope. Identify reproducible failure cases and prioritize changes your engineers can implement.
See AI security & safety reviewsMake the boundaries explicit.
- Evaluation and fallback
- Which tasks represent success, how do we test failure, and what should the interface do when the model or an upstream service cannot respond?
- Release and handover
- How will the change be introduced into your product, evaluated before release, and operated by your team afterward?
A focused AI integration in practice.
For The AI Innovator, ChuuniX built a reading assistant with current-article summaries, article discovery, and retrieval-grounded responses. The case report describes the delivered capability and confirmed technologies.
Read the project reportAI Care Plan / support after launch
Post-launch support is available on a retainer covering monitoring, updates, moving to new models as they become available and security updates. The AI Care Plan defines what is included rather than implying unlimited changes or an always-available support team. Knowledge updates, model and provider changes, and security re-checks are agreed against the workflow your team actually operates.
Keep a named owner for source information, connected accounts and human approvals. We agree how incidents are reported, which changes need a new scope and what is re-tested before release. AI usage and support work are distinct parts of the engagement. Scoped quote after a free discovery call.
Bring the feature you’re working on.
Tell us the product task, existing stack, and the part of the work you want help with. We’ll discuss a scope before proposing an engagement.