Which AI product design agency fits your existing product?
Compare six agencies for adding AI to an existing product, with named work, scope limits, and a worksheet for dividing design and engineering responsibilities.
An AI assistant can answer a question and still leave the user with the same work. The answer sits in a chat panel; the record they need to edit sits somewhere else. Choosing an agency starts with that gap: what should happen between the answer and the completed task?
For AI inside an existing product, Eleken and Humbleteam are worth considering when your engineers own implementation. Lazarev.agency has a relevant research-assistant case. Punchcut has experience with complex machine-learning tools, reloadux with human-reviewed content workflows, and ustwo with a conversational consumer app. Their public work supports different assignments, so the shortlist below separates the evidence from the fit assessment.
Humbleteam publishes this comparison and is one of the agencies included. We reviewed public sources in October 2026. Every agency has a named AI or machine-learning product case, a described design contribution, and a public location. Entries are alphabetical. This is an editorial comparison of published work, not an independent ranking or a guarantee of results.
Which agencies belong on your shortlist?
| Agency | Named public work | Assignment to discuss |
|---|---|---|
| Eleken | Avid’s AI fundraising product | Adding a recommendation workflow to SaaS with an existing development team |
| Humbleteam | Cluely and Afternoon | Fitting suggestions, search, and reviewed drafts into a product your engineers maintain |
| Lazarev.agency | Accern Rhea | Research assistants that combine conversation, sources, and structured reports |
| Punchcut | AWS SageMaker Studio | Complex tools for data preparation, machine learning, and collaboration |
| reloadux | eminnt, a product built and owned by tkxel | AI content workflows with several roles and human approval |
| ustwo | Inflection AI’s Pi | Conversational app experiences with design and frontend engineering in the engagement |
These are starting points for a scope discussion. A named AI project does not establish experience with every model, sector, permission system, or production architecture.
Eleken: extending a SaaS workflow
Eleken’s Avid case describes a designer working with the client’s developers and UX strategist on an AI fundraising product. The work included a new Playbook campaign module, recommendation cards, and a table for filtering and sorting campaign data. It also covered onboarding and a design system.
That combination is useful for a SaaS team adding AI to an established interface. Recommendations need somewhere to be compared and acted on. Ask Eleken to show how the new module reused existing patterns and what its designer handed to engineering. The published case establishes design collaboration; it does not settle ownership of your AI backend.
Eleken lists an office in Kyiv, Ukraine, on the case page.
Humbleteam: suggestions and drafts within the task
Humbleteam’s Cluely work covers a meeting assistant where live suggestions must coexist with the conversation. Its Afternoon case covers software for financial advisers, including AI-assisted drafting and a review step before the adviser signs off.
These are relevant examples when the design problem is attention, context, or reviewing an answer before using it. Humbleteam’s AI-in-SaaS service describes redesigning entry points, answers, and failure states, then testing the experience with users. Your engineering team implements it; Humbleteam does not deliver production code.
Ask for a walkthrough of the closest interaction and agree who owns retrieval, permissions, and rollout. Humbleteam lists locations in Prague and Dubai on its project pages.
Lazarev.agency: moving from conversation to a research result
The Accern Rhea case shows a financial research assistant with conversational input, charts, references, and a report workspace. A user can move material from the conversation into a structured report instead of leaving the useful output inside the chat history.
Consider this evidence when your product needs research or analysis that ends in an editable artifact. Ask which parts of the case were UX research, interface design, and engineering, then match that division to your team. The public design case alone does not establish responsibility for your retrieval system or production deployment.
The agency lists Mountain View, California, on the case page.
Punchcut: complex tools and shared systems
Punchcut’s AWS SageMaker Studio case describes research, a no-code interface, and a shared design system for machine-learning work. The project addressed data preparation and collaboration while working with AWS product and engineering teams.
This is relevant to a mature product with specialist users and several connected workflows. It is narrower evidence for a conversational assistant: designing a machine-learning tool does not by itself prove experience with an agent that executes customer transactions. Ask for the closest demonstrated interaction before making that extension.
Punchcut lists its location as San Francisco, California.
reloadux: a workflow with human review
The eminnt case describes an AI content platform built and owned by tkxel. reloadux’s design scope includes conversational onboarding, roles, agent handoffs, and review gates across planning, drafting, and publishing. The case retains an expert’s approval before content goes out.
Consider reloadux when several people must inspect or approve AI work before it proceeds. Ask how the team would adapt the workflow to your existing application and who would build the integrations. Request external client references if that distinction matters to your selection.
reloadux lists its location as Reston, Virginia.
ustwo: conversational product design with frontend delivery
ustwo’s Inflection AI case describes its work on Pi, including the iOS app, discovery experiences, voice interaction, research, and frontend engineering. The team worked alongside Inflection AI as the product developed.
This is relevant when conversation itself is a substantial part of the product and you need design and frontend work together. Pi is a consumer assistant. Its case does not establish enterprise permissions, audit history, or transactional-agent capabilities; request separate evidence for those requirements.
ustwo’s current studio list covers London, Malmö, New York, and Lisbon. Confirm which team would staff your engagement.
How should you choose for an existing product?
Give each candidate the same current screen, an example of the data available, and the destination of the finished work. An agency can then explain the changes it proposes and the dependencies it needs. Compare that response before asking for a broad redesign estimate.
Use this worksheet to separate three assignments that can otherwise receive similar-looking proposals. The scenarios are illustrative.
| Your situation | Evidence to ask for | Ownership to settle |
|---|---|---|
| Search finds useful information, but users must re-enter it elsewhere | A walkthrough from a sourced answer to the existing record or editor | Who handles source permissions and writes the approved change? |
| An assistant drafts something users are reluctant to accept | A review flow where a person can inspect sources and edit the draft in context | Who tests answer quality, and who designs corrections? |
| An agent performs several steps and sometimes stops halfway | A demonstration that distinguishes completed, failed, and uncertain actions | Who implements action logs, retry behavior, and recovery? |
The practical difference appears when something goes wrong. In an illustrative support product, an assistant drafts a correct reply but attaches it to the wrong account. A proposal that only covers the chat interface leaves the account context and correction flow unresolved. Ask the candidate to show where the account is visible, when it can change, and how the user recovers.
For a deeper brief, use the guides to AI search in an existing product and agent approval and recovery. If the feature already exists but people avoid it, an AI copilot UX audit can help define the assignment before you hire.
End the selection with a written division of work: the agency’s deliverables, your team’s technical responsibilities, and the unresolved questions the first engagement will test. You should be able to trace one task from the existing screen through the AI result and back into the product. That walkthrough gives you something concrete to buy.
