--- title: "AI Features for B2B SaaS: RAG, Copilots, Agents | Donux" description: "Ship AI features for your B2B SaaS: feature audit, architecture spec, evals, cost dashboards, and senior review. Start with a free product assessment." url: "https://donux.com/use-cases/ai-features" language: "en" --- # AI Features for B2B SaaS Add AI features and make your product better Let's add AI features to your B2B SaaS. We'll help you pick the right ones and ship them. [Build with us](https://donux.com/contact-us) [![Kontai SaaS spend management platform](https://images.app.donux.com/unsafe/case-studies/kontai/ie7l4RtlJUwqQ9NqsZmZCTBBJo.png)](https://donux.com/case-studies/kontai) Selected product design work[Kontai](https://donux.com/case-studies/kontai) [**4.9/5** on Clutch 9 client reviews](https://clutch.co/profile/donux#reviews) Selected product design clients - 4DEM - Fluida - La Marzocco - Blue.ai ## Why most AI features never reach production ProblemImplication ### Hype-driven feature lists with no clear user job. Months of build, low adoption, wasted budget. ### ML hires take 6+ months and lock in fixed cost. The market moves on before your team is ready. ### Demos work, production breaks on real data. First impression is broken output. Users churn. ### Cost runs away with token usage and bad architecture. Each new user costs more than they pay. ## AI features we ship From single-feature additions to agentic workflows. ### Smart automation Categorization, summarization, extraction, and triage. ### RAG and semantic search Find answers across your documents, tickets, and product data, even when the keywords don't match. ### AI assistants and copilots In-product chat and recommendations grounded in your data. ### Agentic workflows Multi-step actions where the AI plans, calls tools, and reports back. With proper guardrails. > They never miss a touch base and timeline expectation. **Abed el Madjid Nehme** Head of Product @Blue.ai [Our work with Blue.ai](https://donux.com/case-studies/blue-ai) ## How we ship AI features in 5 steps From feature audit to production, with cost and quality guardrails baked in. 1. ### Pick the right feature Senior humans audit your product and propose AI features tied to a real user job, a measurable outcome, and the data to support them. 2. ### Spec the architecture Model choice, retrieval strategy, evals, cost ceilings, fallback behaviour, and safety guardrails. Specced before any code is written. 3. ### Build to production quality Implementation integrated with your data, auth, and stack. Prompts, retrieval, and evals reviewed by senior engineers before anything reaches your users. 4. ### Test on real data, then merge Live preview wired to a sample of your real data. You test it, we tune. Approve, we merge. 5. ### Monitor and iterate Cost, latency, and quality dashboards from day one. We iterate on regressions surfaced by evals or users. [Book a Discovery Call](https://donux.com/contact-us) ## What you get Production-ready AI features, with cost and quality safeguards from day one. - AI feature audit with prioritized recommendations. - Architecture and evals spec. - Working AI feature integrated into your product. - Cost, latency, and quality dashboards. - Fallback behaviour for model outages. - Senior review on every change. ## Before you start. ### Who is this for? B2B SaaS teams that want to add AI features but don't have an ML team and don't want to hire one. Founders and product leads who want strategy and execution under one roof. ### Which models and providers do you use? We pick the right model for the job. OpenAI, Anthropic, open-source, or self-hosted. Every choice balances quality, cost, and latency. ### How long does it take to ship an AI feature? A first conversation defines the relevant scope, dependencies and investment. For an existing product transformation, we confirm what fits the 90-day delivery window before kickoff. A focused supporting engagement is scoped separately. ### How do you keep AI cost from running away? Token budgets, caching, model routing, and cost dashboards. Senior review flags expensive patterns before they merge. ### What about evals and quality? Every AI feature ships with evals. We run them before every merge and monitor for regressions in production. ### Can the AI feature work on our existing data? Yes. We integrate with your existing data and auth. RAG over your docs, tickets, or product data is one of the most common patterns we ship. ### Can you also handle product strategy? Yes. AI features pair naturally with our Product Management service. We help you pick the right bets and ship them. ### How does Magic Team fit in? Magic Team is our managed AI delivery option. If you have a clear scope, you can connect your repo and we ship features as reviewed PRs. For strategy or discovery first, work with our senior team directly. ## See the work. Selected product design projects. Explore the challenge, the decisions and the results behind each one. [![Kontai SaaS spend management platform](https://images.app.donux.com/unsafe/case-studies/kontai/ie7l4RtlJUwqQ9NqsZmZCTBBJo.png)](https://donux.com/case-studies/kontai) AI & Machine Learning ### How Kontai went from idea to MVP in 3 weeks [Explore the project](https://donux.com/case-studies/kontai) [![Blue.ai platform interface](https://images.app.donux.com/unsafe/case-studies/blue-ai/uzXYj3jBxT9m9q2gUnbKQpsu9M.png)](https://donux.com/case-studies/blue-ai) Marketing & Sales ### How Blue.ai transformed their omnichannel CX platform in 12 weeks [Explore the project](https://donux.com/case-studies/blue-ai) [![How La Marzocco reduced support backlogs through redesigned communication and data systems](https://images.app.donux.com/unsafe/case-studies/la-marzocco/F7FfZwIPwVziFBhJDnEY8M5c4.png)](https://donux.com/case-studies/la-marzocco) Operations & Workflow ### How La Marzocco reduced support backlogs through redesigned communication and data systems [Explore the project](https://donux.com/case-studies/la-marzocco) ## See it in practice. [Our work](https://donux.com/case-studies) [Talk to Donux](https://donux.com/contact-us)