AI UX patterns without rebuilding the whole product

On‑system delivery is a non‑negotiable. Firstly, we do a proper system inventory: tokens, grids, type scales, component naming, interaction patterns, even how your Storybook and Figma map. From there, every AI pattern is designed and documented to live inside that ecosystem. Where your system is strong, we inherit it. Where AI use cases expose gaps or inconsistencies, we flag them explicitly and propose fixes before we add more surface area. The result we offer is not “Lazarev.agency’s AI library” bolted on top, but an extended and hardened version of your system that happens to understand AI use cases well. Your team shouldn’t have to translate or re‑skin anything; it should feel like a natural v2 of what you already maintain.

We assume you’re not starting from zero. The first step is a proper audit of what exists: live product patterns, squad experiments, internal prototypes. Anything that’s coherent, on‑system, and working in the wild gets canonized. We pull it into the taxonomy, clean up edge cases, and document when and how to use it so it scales beyond the original squad. Patterns that conflict, create fragmentation, or don’t meet current standards get red‑flagged with a clear rationale and options: retire, refactor, or constrain to a narrow use. Most teams discover they already have 40–60% of the raw material; it’s just scattered and undocumented. The value of the library is giving all that work a stable home and language so designers, PMs, and engineers stop reinventing it one feature at a time.

A library is only valuable if it becomes the default way work gets done. We treat adoption as a first‑class deliverable. That’s why there’s a dedicated rollout phase: playbooks for PMs and designers, engineering workshops, and office‑hours‑style support while the first teams actually ship from the library. Components come with usage guidelines and decision trees: when to use this pattern, when not to, how to handle edge cases, and examples from your own product. We also help you define lightweight governance: who owns the library, how new patterns get added, and how depreciation works so it doesn’t rot. Finally, we recommend simple analytics hooks so you can see which patterns are being used, where teams fall back to “custom,” and where more training or refinement is needed.

Yes, and for most orgs it’s the politically and operationally safest path. We typically scope the first 6–12 weeks around a single high‑leverage domain: one squad, one journey, or one AI‑heavy feature set. That pilot lets you pressure‑test the taxonomy, see how engineers experience implementation, and collect concrete before / after stories you can take to your CPO or CIO. It’s also where we tune naming, file structure, and contribution rules so they reflect your reality. Once that slice is in production and squads are pulling patterns by default, it’s much easier to make the case for extending to more teams, verticals, or compliance‑sensitive areas.

We design this to return capacity. In practice, your design lead (and maybe one system‑minded IC) is involved in 60–90 minute working sessions weekly or bi‑weekly, plus async reviews. They’re co‑steering decisions and validating fit; we’re doing the heavy lifting on exploration, documentation, and library construction. Mid‑program, you typically start to see the payoff: squads stop inventing one‑off AI patterns and start pulling from the shared set, so your senior designers spend less time doing “UX firefighting” and more time on actual product thinking. The long‑term state is a team that can handle more AI surface area per head because they’re assembling from a clean, shared system instead of re‑solving the same interaction problems every sprint.

Yes, if we keep the scope honest. The 6–12 week window is sized for a first AI pattern family that’s real enough to ship against. By week 3–4 you’ll typically have the taxonomy and key flows defined; by week 6 you have validated pattern designs; by week 8–12, Figma components and documentation are in place and engineers can start wiring them into live work. That means your QBR or board update doesn’t just say “we’re working on AI UX” but shows a concrete library in use and product surfaces that look and behave materially better. It’s visible AI progress without committing to a risky, all‑at‑once redesign.

UI kits and OSS libraries give you pixels and code while we offer product decisions targeted at your growth. They rarely answer the hard questions: when to expose a confidence score, how to handle low‑confidence states, what an override flow looks like when a user disagrees, how to balance explainability with cognitive load for non‑experts, or how compliance changes the pattern in regulated verticals. Those decisions depend on your users, risk model, and domain. This engagement produces reusable AI UX patterns plus the logic behind them: when to use, when not to, what variants exist for different risk levels, and how they show up in Figma and in code. We also wire in the organizational side: how squads adopt, how new patterns get added, and how you keep drift under control over time.

They absolutely could. The question is whether you want every squad re‑learning the same painful lessons. AI introduces failure modes that standard SaaS UX doesn’t: over‑trust, under‑trust, unclear ownership between human and model, explainability that either overwhelms or under‑informs, assistants that drift off‑brand or off‑policy. Solving those well usually takes multiple launches and a lot of trial and error. We come in with those lessons pre‑baked from many AI products already in production: confidence and escalation patterns, override and feedback flows, assistant guardrails, summarization and recommendation patterns, and their tradeoffs.