10+ years
of experience
in UI/UX design
in UI/UX design
/ 01
AI chatbot development service
Become a client
Your chatbot demos well. Users still don't get it. At Lazarev.agency, we craft the conversational AI experience around your model with clean handoffs to humans and useful output inside real workflows. Production-ready in 4–8 months for founder-stage engagements, 4–6+ months for enterprise programs.
- From demo to a product investors trust
For founders pitching investors and sales teams running enterprise demos, the chatbot gets evaluated in the first 90 seconds. We design the conversational surfaces shown in demos and the failure-mode UI visible when something goes wrong. The chatbot holds up under live walkthroughs. - AI adoption your QBRs can point to
Heads of AI and Heads of Product carry the adoption number into every QBR. Customers often don't see or trust the AI features inside the platform. We design discovery patterns, in-product onboarding for AI surfaces, and conversational flows surviving real workflows. Quarterly reviews open with usage data. - Conversational patterns inside your design system
Design leads inherit systems that weren't built for AI behavior: inputs the system has to parse, responses the model might be wrong about, states where it's thinking, citing, regenerating, or refusing. We extend your tokens and components with the AI patterns your team would otherwise reinvent on every release.
/ 01
$500M+
in funding secured
for our clients
for our clients
/ 02
120+
awards backing
our excellence
our excellence
/ 03
2015
founded, 10+ years
of experience
of experience
/ 04
San Francisco, CA
AI product design agency
/ 05
full-cycle product design
from user research to production-ready design systems
Among our clients:
Awards
& Recognition
& Recognition
Our team’s work was honored with most of the world-known trophies
120+
Awards won all time
CSS Design
Website of the year
Awwwards
Agency of the year nominee
The Webby Awards
The FWA
Awwwards
Red Dot Awards
Behance
the drum awards
Why most AI chatbots stall after launch
AI chatbot demos land, but daily use fails to keep up. The same patterns show up across late-Seed startups, enterprise platforms, and design-led product orgs: users don't trust the answers and pilots stall after the third week. The model isn't the problem. The product around it is. Below are the pain points we hear most often.
Users don't trust the answers
Most chatbots prioritize speed over accuracy. Without citations, edit, regenerate, or a confidence signal, users can't verify answers or recover from wrong ones. Hallucinations go undetected. Even a strong model loses pilots when the surface doesn't give users control over what the model says.
Demos land. Pilots stall
The sales call goes well. Three weeks in, the champion stops responding. We've seen this pattern across 30+ AI products: the conversational UX falls apart on real workflows and the real (but messy) data buyers bring. Pilots fall flat between demo and daily use.
More AI features, less adoption
Heads of AI hear it from the C-suite every quarter: add more AI features. The roadmap doubles. Adoption, though. It flatlines with no traceable reason why. Customers can't see most of the AI in the platform. The bottleneck moved to the UX layer, where each new feature must be easily discoverable to be trusted and used.
A year of UX is a year you don't have
Founders racing toward Series A or Series B need senior product design, conversational AI patterns, demo-grade marketing, and investor-ready pitch motion inside a window protecting the next round. Hiring a senior design lead takes six months. By the time the role is filled, the runway conversation gets harder.
How we build chatbots
We've designed 30+ AI products in production since 2017. Conversational AI is one of the surfaces we know best: copilots, assistants, support bots, internal agents, and the flows around them. The real challenge starts after the pilot succeeds. Our approach closes the gap between a strong model and a weak product, with a senior AI-UX pod scaled to your team setup and the system you already run.
Trust surfaces wired into every response
Confidence signals, citations, regeneration, and quick feedback loops sit inside every answer. Users can verify and correct what the model produces in the output. Procurement and pilot evaluators get the controls they need to assess the AI for real use across the pilot window.
Failure modes designed before the code starts
Hallucinations, slow tools, partial responses, empty inputs, broken connectors. We map all possible failure modes in week one and design the UI for each before engineering writes a line. Your eval harness gets meaningful inputs. Support sees fewer fires when pilots scale into customer hands.
Conversational flows engineers can build
Every flow comes with prompt structure, state diagrams, edge cases, and the design tokens your team already uses. Engineering moves to code without a 90-minute Loom. The handoff is build-ready, with the level of detail enterprise engineering teams expect on any production surface.
Adoption metrics on every flow
We focus on adoption and time-to-value starting week one. Every flow we design carries a metric. Investor updates, QBRs, and internal product reviews open with usage data your team can act on.
Case studies
AI chatbots and conversational products in production
We've designed conversational AI products across healthcare, fintech, sales tools, internal agents, and support workflows. Below are public cases where the chatbot moved from pilot to daily use, raised the next round, or unlocked an enterprise tier.
Services inside an AI chatbot engagement
What our clients say about working with us
Teams come to us when the demo lands but the product doesn't, when the next round depends on a conversational UX investors trust, when adoption needs to lift before the next quarterly review, or when an internal design team needs senior AI UX support without losing control of the system. The feedback is consistent: the chatbot starts behaving like a product, and the team in-house keeps full credit for the work.
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Through a detailed understanding of the client’s platform, Lazarev. was able to create a clean and intuitive UI/UX design that ticked all the boxes. The team was receptive to all requirements and requests and adapted well to timeline changes. They produced accurate mockups at every iterative stage.
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Lazarev. fostered a positive engagement by delivering a navigable site that allowed users to absorb information quickly. The team led a thoughtful, efficient workflow that was always prepared for meetings.
Industries we design AI chatbots for
Conversational AI behaves differently in every domain. Fintech runs inside compliance constraints. B2B sales copilots answer in the buyer's industry language. Below are industries where our conversational AI work has been in production.
Who we are and why teams like yours work with us
We exist for B2B teams under pressure to turn an AI roadmap into visible product usage, expansion, and a safer story in front of the C‑suite and investors. If design isn’t moving revenue, adoption, or retention, it’s decoration. We design to avoid that. Since 2015, we’ve shipped 600+ products and earned 120+ awards for work on complex, data-heavy tools: fintech platforms, AI copilots, decision engines, and vertical SaaS. Our work has helped clients turn “we have AI features” into “our customers actually use and pay for them.”
We started designing AI products in 2017, long before “AI-native” became a buzzword. With 30+ AI products shipped, we focus on the hard part most teams struggle with: making complex intelligence feel simple, trustworthy, and obviously valuable in a demo, a POC, or a QBR. We’re a 40+ person team of UX strategists, product designers, and analysts who treat design as a business function. Every engagement is anchored to the metrics you care about: AI feature adoption, activation and retention in key accounts, time-to-decision in core workflows, and upgrade/expansion tied to AI-powered plans.
120+
international
industry awards
industry awards
/ 02
600+
projects
successfully completed
successfully completed
/ 03
We operate on a simple principle: if you're not measuring design against business outcomes, you're wasting money.
What sets us apart from a typical agency or a single in-house hire is pattern recognition at scale. We’ve seen what works – and what quietly kills adoption – across hundreds of AI and data-heavy products. That lets us spot failure modes early, bring proven interaction patterns to your team, and reduce the risk that your next AI release is another unused toggle in a settings menu.
We start with research not because it’s “best practice,” but because designing without understanding your users, your market, and your revenue model is just guessing with nicer pixels. From there, we collaborate with your product, AI, and design leaders to define where AI should show up, how it should behave, and how to make it obvious, safe, and monetizable.
If you’re a Head of AI, Product, or an AI-native founder who needs AI capabilities to be seen, understood, and used now, not someday, we’re built to be that partner.
How we run an AI chatbot engagement
Week one is a working session with your team, your model, and your pilot data. From there, work moves in weekly increments with one Product Lead, written recaps, and a single source of truth. Founder-stage engagements typically run 4–8 months end-to-end. Enterprise programs run 4–6+ months. Below is the cadence.
You built the AI. We build the conversational experience around it.
01
Working session with your team
Week one. We sit with your founders, engineers, product leads, design lead, and pilot data. We map the conversational surfaces, the failure modes, and the metrics defining production-ready. You leave with a written recap, a roadmap draft, and a shared definition of done.
02
Conversational architecture and trust surfaces
Weeks two through six. We design the conversational flows, prompt scaffolding, trust surfaces, and failure-mode UI. Engineering gets specs in your design tokens, with state diagrams and edge cases. Your eval harness gets meaningful inputs. The chatbot starts behaving like a product.
03
Pilot integration and live iteration
Weeks five through twelve. The chatbot moves into pilots or a live product. Every weekly recap includes a metric, a failure pattern, and a design adjustment. By week eight, real users and real workflows have been through the conversational UX.
04
Marketing site, demo, and adoption surfaces
In parallel. The same pod builds the marketing site, demo flow, investor walkthrough, or product-led adoption surfaces, depending on what's in scope. Sales, product, and design show the same surfaces during demos, QBRs, and investor sessions, telling one story.
05
Handoff to your in-house team
By months four to eight, your team owns the conversational AI patterns, the design system extensions, and the eval inputs we built together. We document everything, stay on retainer when needed, and step back as the internal capability scales. No vendor lock-in.
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FAQ
/00-1
How long does an AI chatbot engagement take?
Founder-stage engagements run 4–8 months end-to-end. Enterprise programs run 4–6+ months. Week one is a working session and audit. Weeks two through twelve cover conversational architecture, trust surfaces, integration, and iteration in pilots. By month four, the chatbot is in production for real users. By month eight, supporting surfaces (site, demo motion, design system extensions, depending on scope) are live and the engagement winds down or moves to retainer.
The exact range depends on the complexity of your AI product, the surfaces in scope, the size of the pod, and your internal team's capacity. Teams racing toward a fundraise tend to pick the tighter end; teams aligning with a quarterly review pick the timeline matching it.
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What does it cost?
Engagement size depends on the complexity of your conversational AI surfaces, the scope of work, the size of the pod, and the timeline. Founder-stage chatbot engagements are scoped tighter and run on shorter cycles. Enterprise and design-led engagements run larger, with broader pods and longer timelines tied to procurement and QBR cadence.
We share a concrete estimate after the week-one working session and audit. Engagements get scoped and priced as a single program, with deliverables and milestones written into the agreement. Talk to a Startup Lead for founder-stage scoping, or an Enterprise Lead for enterprise programs.
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How is this different from hiring a senior designer or a freelancer?
Hiring a senior design lead takes around six months and locks the team into one person's pattern recognition. A single freelancer covers product UX or marketing, rarely both. Most chatbot engagements need product, site, demo motion, and pitch reinforcement at once, which means juggling four or five vendors.
Our model is a temporary design cofounder plus a full team. One pod, one Product Lead, 4–8 months. Senior pattern recognition across 30+ AI products in production, without herding freelancers or burning runway on a hire taking a quarter to ramp. Talk to a Startup Lead if you want to walk through it.
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How do you work alongside our internal design team?
We work as a senior AI-UX pod under your design lead. We start from your tokens, components, and breakpoints, and extend the system for AI patterns: inputs, responses, citations, regeneration, explainability, overrides, and uncertain or error states. Documentation lands inside your design system, so the team owns it after the engagement.
The work is drop-in presentable for PMs, engineering, and execs. Your design lead stays in control of the system. Procurement, security, SLAs, and legal review get handled inside the engagement. Talk to an Enterprise Lead for a walkthrough.
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What if we already have a chatbot in production?
Most teams we work with already have a v1 in pilots or production. The work usually starts with an audit of where users drop off, where pilots stall, and where the conversational UX is hiding the strength of the model. From there, we redesign the trust surfaces, the failure-mode UI, and the surrounding product without breaking what works.
You don't need to start over. You need the conversational layer rebuilt around behavior your users can rely on, your enterprise buyers can demo, and your design team can extend. Response within one business day.
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