Digital product design agency for AI & B2B products

We design for adoption and revenue. “A fresher UI” is not our story. Most agencies optimize for aesthetics. We start from your AI roadmap, data model, and KPIs, then design flows that increase activation, feature usage, and expansion.

We’ve shipped 600+ products, 30+ AI/ML tools and copilots, and 140+ award-winning interfaces. That work has translated into 30–300% conversion lifts, $500K–$40M in new revenue, and over $500M raised by clients from top-tier investors. Your CFO and board care about those numbers. 

If you want the cheapest or “prettiest” option, we’re not it. If you want a team that treats design as a revenue lever and can speak to your C-suite in metrics, that’s what we do. On a call, we can walk through examples from AI-native SaaS and fintech similar to yours.

You should expect movement on adoption, retention, and revenue. Nicer screens are a bonus benefit. A few examples from our portfolio: 300% conversion increase on B2B onboarding, 42% retention lift for an EdTech platform, $500K in first-month sales for an e-commerce launch, 3x revenue growth for a consumer brand, and $40M raised for an AI company after we rebuilt their product story and UX.

Book an intro call, and we'll project your funnel metrics into similar case studies to show realistic impact ranges before you commit.

We plug in as a specialist AI/UX unit. Your PMs and in‑house designers own the roadmap; we take the hardest product UX problems off their plate and turn them into tested, implementation-ready flows and systems.

Day to day, we align on KPIs and constraints with your product/AI leads, agree on decision rights, then run in Figma with async reviews and tight dev handoff. Your team gets pattern libraries, design systems, and validated user flows they can extend, instead of starting from scratch.

So, your team focuses on strategy and shipping, while we handle the heavy UX lifting for AI features, complex workflows, and adoption-critical flows.

Use a freelancer when you need a few screens, a landing page, or simple UI polish on a tight budget. They’re great for contained tasks but weak for complex AI products with multiple stakeholders, compliance, and revenue targets attached.

Bring us in when the cost of getting it wrong is real: AI copilots, dense analytics, multi-step onboarding, or feature sets your sales team needs to demo clearly. There, you want a team that has done this hundreds of times, can pressure-test edge cases, and stand behind business outcomes.

If you’re deciding between options, we can walk through your roadmap and tell you honestly where a freelancer is enough and where a specialized AI product team like ours pays for itself.

For AI/B2B products, most meaningful projects run 8–12 weeks for design, and 3–6 months end‑to‑end including rollout. Smaller scoped UX fixes can be done in 4–6 weeks; complex platforms and ecosystems take longer.

We structure work into discovery, design, refinement, and launch/optimization. You’ll see tangible outputs in the first 2–3 weeks (flows, concepts, prototypes) and implementation-ready design systems by weeks 6–10, depending on scope. Measurable impact on activation, adoption, or conversion usually shows up within 4–12 weeks after launch, depending on your release cadence and sales cycle. 

If you truly need a 4‑week turnaround, we can discuss a narrow, visual‑only pass and be upfront about risk to outcomes.

Before we start, we align on success metrics, scope, constraints, and decision rights. Then a typical 8‑week engagement looks like this:

Weeks 1–2 are for discovery: stakeholder sessions, 5–10 user interviews, analysis of 8–12 competitors/adjacent tools, mapping your AI roadmap to user journeys and KPIs.

Weeks 2–5 are for design: flows, wireframes, visual language, AI interaction patterns, initial design system, accessibility baseline, weekly reviews.

Weeks 5–7 are for refinement: edge cases, interaction details, responsive states, implementation‑ready specs and tickets.

Weeks 7–8 are for launch support and optimization: QA with dev, resolving UX questions, early metric checks, iteration priorities.

We ask for a single empowered decision‑maker, ~1–2 hours a week for reviews, and 48‑hour feedback. When four stakeholders must approve every detail, timelines and outcomes both suffer.

Because skipping discovery is the most expensive way to “save” money. When teams bypass research, they usually optimize the wrong thing: polishing UI while the real blockers are value prop, messaging, onboarding friction, or trust in the AI itself. That’s how you burn budget and still miss your KPIs.

We treat design as a business problem with a visual output. Discovery gives us evidence on what’s breaking activation, adoption, or expansion, so we can design the one or two flows that move your metrics.

If you already have solid, recent user research and product data, we’ll use it instead of redoing the work, then plug any gaps. The goal isn’t “more process”; it’s reducing rework for your team and increasing the odds that the next release actually shows up in your dashboards and QBRs.

We start from the job. Most users don’t care which LLM you use; they care if they can quickly see what the AI did, why, and how to correct it. We’ve designed 30+ AI products since 2017, from copilots to analytics and decision-support tools, so we know where people get confused and where trust breaks.

In practice, we focus on three things: clear outputs (what changed and why), calibrated confidence (when the system is unsure, it should say so), and control (easy ways to override, correct, and give feedback). That’s how you avoid “mysterious black box” behavior that stalls adoption.

This approach has supported results like $40M raised after an AI platform redesign, $1M for an AI MVP, and hundreds of new enterprise users onboarded for AI+legal tooling. On a call, we can show concrete patterns we’ve used for products similar to yours.

You can absolutely start small, as long as “small” still means rigorous. The right way to phase is: go deep on discovery once, then narrow the first design slice to the highest-impact flow tied to your core metric.

A typical path looks like: Phase 1 (8–12 weeks, focused core experience), Phase 2 (another 8–12 weeks expanding to adjacent flows), Phase 3 (12+ weeks for full ecosystem and mature design system). The trap is cutting discovery in Phase 1 to save money; that’s when Phase 2 turns into rework and gets more expensive than if you’d done it properly from the start.

If you can fund Phase 1 + 2, that’s usually the most efficient. If you only have Phase 1 budget, we’ll help you pick the narrowest slice that can prove impact, so you can use those results to justify the next phase internally.

We judge success on business metrics. We look at activation, conversion, retention, expansion, and sales/demo effectiveness. If those don’t move, the design missed.

Real outcomes from our recent work: 42–48% retention increases, $500K–$40M in new revenue, up to 130% traffic growth, and $1M–$200M raises tied directly to redesigned products and onboarding. Most clients see early movement within 6–8 weeks of launch, with compounding gains over 3–12 months. 

The best results come when there’s at least early product–market fit, clean enough data to measure change, and a mindset that design is an ongoing growth lever. Schedule a call with our team, and we'll map our metrics to your current funnel so you know exactly what we’d track.

We specialize in AI-native and complex B2B products: AI tools and copilots, analytics platforms, SaaS, and enterprise systems where UX directly affects adoption, sales, and renewals. That’s where design decisions show up in your P&L and board decks.

We’ve driven results across AI (30+ products, $40M raised), SaaS (300% conversion lift, 42% retention increase, $1M+ funding), EdTech ($200M+ raised, millions of users), ecommerce ($500K first-month sales, 3x revenue), fintech, Web3, healthcare, non-profit, and more. The common thread is complex workflows, multiple stakeholders, and high stakes per user.

Industry experience helps us move faster because we already know the UX patterns and constraints.

Yes. Most of our clients are late-seed to Series B SaaS and AI companies, or established enterprises with high ACVs and long sales cycles. We’re used to environments where a small lift in activation or expansion has a big impact on ARR and the next round.

We’ve helped AI and SaaS teams use product design to support $1M–$200M+ raises, move from pilot to rollout in the enterprise, and make complex products “demo-ready” so sales can close faster. We understand what your C-suite, board, and investors will ask when they see the new product.

If you share your stage, ACVs, and go-to-market (sales-led, sales-assisted, PLG-assist), we can walk through specific examples from companies that looked a lot like you when we started.