Relume
Good at sitemap-first planning because it forces information architecture decisions early, which reduces rework once engineering starts.
Product engineering and AI, Los Angeles
Custom software and AI solutions from Los Angeles
Sidebench is a product engineering and AI solutions company based in Los Angeles. We design, build, and ship custom software, mobile apps, and AI-powered products with the teams who own them.
If a roadmap item keeps slipping, or a product concept needs a real build behind it, this is where the conversation starts.
What we actually do
Most teams don't arrive asking for an app. They arrive with a workflow held together by spreadsheets, a mobile product that has outgrown its first version, or an AI feature stuck in a demo nobody trusts. We work across four connected disciplines because a product that holds up in production usually touches all four.
A design system decision changes how fast engineering can move. Retrieval quality in a RAG system depends on how well the interface sets expectations for the person using it. Those are one problem here, not four handoffs.
-
01
Custom software development
Systems built around a constraint the off-the-shelf market doesn't cover — a legacy dependency, a compliance rule, or a workflow nobody sells a product for.
-
02
Mobile app development
Native iOS and Android, or cross-platform where it fits — chosen against what the product needs in year two, not what is fastest to demo in week two.
-
03
AI solutions
Retrieval, evaluation, escalation, and interface design around the model — with human-in-the-loop paths for the moments the model isn't sure.
-
04
UX/UI design
Design systems, rapid prototyping, and microcopy that reads like a person wrote it — including where prompt-to-code tools speed up exploration and where careful design takes over.
The most expensive part of an AI product isn't the model. It's what happens when the model is wrong.
We treat AI as a system: retrieval that shows its sources, automation with defined escalation points, and interfaces that make the AI's limits visible to the person on the other side of the screen.
Custom software
Custom software development
Custom software work starts with understanding the constraint, not the feature list. Sometimes that constraint is a legacy system that can't be replaced yet. Sometimes it's a compliance requirement that rules out a hosted option. Sometimes it's simply a workflow that no off-the-shelf tool fits.
We scope in short increments so you see working software early rather than a status report. Teams typically come to us when a product is past the prototype stage and needs to survive real users, real data volume, and real edge cases — the point where architecture and error handling start to matter more than the demo.
Constraint first
The scoping question is what the system is not allowed to assume — legacy dependencies, hosting limits, review requirements.
Short increments
Each increment ends with something running that your team can click, question, and redirect before the next one starts.
Built to operate
Deployment, monitoring, and the operational knowledge to keep the product stable stay part of the work.
Mobile apps
Mobile app development
Mobile decisions get made too early and cost teams later. Native iOS and Android builds give you the deepest access to platform capabilities, which matters when an app leans on camera, location, background processing, or push behavior. Cross-platform builds make sense when the feature set is closer to the middle and you want one codebase to maintain.
We help teams pick based on what the product needs in year two, not what's fastest to demo in week two. If AI features are part of the mobile product from day one, that choice also shapes how you handle latency and offline states.
See how the services fit togetherAI solutions
AI work goes wrong in predictable ways. A chatbot that answers confidently but incorrectly. A workflow automation that saves time until someone has to fix its mistakes. A retrieval system that pulls the wrong document because nobody tuned the chunking. None of those are model problems — they are system problems with a model inside them.
We build AI features with human-in-the-loop design as a default, so there's a clear path when the model isn't sure. That means retrieval systems that show their sources, automation with defined escalation points, and interfaces that make the AI's limits visible to the person using it.
Retrieval you can audit
Answers arrive with the source they came from, so a wrong result is traceable instead of mysterious.
Escalation before failure
Automation hands off to a person at a point you define, instead of quietly making the decision itself.
Limits on screen
The interface says what the model does and doesn't know, so users calibrate their trust instead of guessing.
UX/UI design
Design here covers the parts that show up on screen and the parts that don't. Design systems, rapid prototyping, and prompt-to-code workflows all speed up early exploration.
Those tools are less useful when the work needs consistency across a mature product, accessibility that holds up to review, or microcopy that reads like a human wrote it. We use them where they accelerate and step back to careful design work where they don't.
Review our design processHow the tools fit into a real process
Tools like Relume, Whimsical AI, Adobe Firefly, Jasper, and Bolt compress the boring parts of discovery. None of them replace product judgment.
Whimsical AI
Helps teams map user journeys before anyone opens a wireframing tool, so the flow argument happens before the pixel argument.
Adobe Firefly
Useful when a design needs visuals rather than structure — mood, imagery options, and direction the team can react to quickly.
Jasper
Covers interface microcopy and marketing copy variants, which is most valuable when you need twenty drafts to find the one line that works.
Bolt
Shortens the path from concept to clickable prototype, so a stakeholder conversation happens with something in hand instead of a slide.
When AI design tools help and when they don't
Fewer, sharper decisions made early beat a wall of generated screens nobody reviewed. We're direct with teams about which side of that line their project sits on.
Where they help
Strongest at the beginning: exploring layouts, testing a flow, and producing something clickable to react to before the team is invested in a direction.
Where they don't
Weakest once a product has a design system, accessibility requirements, or an established component library — there's cleanup work that has to be done by hand anyway.
Choosing between native, cross-platform, and web
There's no universal answer, but there is a useful question: what will this product need to do that a web app can't? The decision should be driven by the roadmap, not the developer's preferred framework.
Native apps
Camera depth access, background location, offline-first behavior, and deep platform integration all push this way. Pick it when the product depends on the device, not just runs on it.
Chosen when hardware access is the product.
Cross-platform
Sits in between and is a reasonable default for many consumer products: one codebase to maintain, with native escape hatches where a specific feature needs them.
Chosen when the feature set sits near the middle.
Web apps
Internal tools, content products, and dashboards often work better here because deployment and iteration are simpler and users get updates the moment they ship.
Chosen when iteration speed matters most.
Common mistakes we see in AI product builds
-
1
Treating AI as a feature instead of a system
A model is one piece. Retrieval, evaluation, interface design, and error handling are the rest, and they are where most of the project risk actually lives.
-
2
Skipping the "what happens when it's wrong" talk
Teams build for the happy path, then discover users don't trust the feature when it fails silently. The conversation is cheaper before the interface exists.
-
3
Over-relying on generated code and design
AI tools speed up first drafts. They don't replace the review process that keeps a product coherent once it has more than a handful of screens.
How we work with product teams
We embed with your team rather than handing over a spec and disappearing. If you already have an in-house team, we can fill a specific gap — mobile, AI, design systems — without forcing a full handover.
Where we're based
Sidebench
149 South Barrington Ave
Los Angeles, CA 90049, USA
Monday to Friday, 9:00 AM – 6:00 PM
[email protected]
+1 415-584-8749
-
A kickoff about constraints
The first session maps what the product can't assume — dependencies, review gates, hosting limits — before anyone argues about features.
-
Short delivery increments
Each one ends with a working demo your team can use, so course corrections happen while they're still cheap.
-
Design and engineering together
Designers and engineers work on the same problem from the same week one, which is where most of the coordination overhead disappears.
-
Working across time zones
We work with teams across the United States and coordinate schedules when a project spans more than one region.
Questions teams ask us first
Short answers to the things that come up in a first scoping conversation. If your situation isn't here, the contact page is the better place to describe it.
What does a modern IT development team deliver beyond coding?
A working product that survives contact with real users. That usually includes scoping, technical architecture, design, QA, deployment, and the operational knowledge to keep the thing running. Coding is one part of a longer chain.
How do mobile app projects change when AI features are part of the product from day one?
Latency, offline behavior, and error states become design decisions rather than engineering afterthoughts. A mobile AI feature that can't handle a slow connection will frustrate users no matter how good the underlying model is. Planning for that early is cheaper than retrofitting later.
How should a company evaluate a software partner that claims both engineering and AI expertise?
Ask what happens when the AI is wrong. A partner that can describe their evaluation process, their escalation paths, and their human-in-the-loop design has thought about the problem. A partner that only talks about models hasn't.
Which AI design tools are useful for wireframing, and which are only good for demos?
Uizard, Relume, and Whimsical AI are useful in early exploration because they help you get structure and flows on screen quickly. Tools that generate polished-looking screens without a design system behind them tend to produce demos that don't survive into production.
How do teams decide between native apps, cross-platform apps, and web apps?
Start with what the product needs to do that the web can't. Deep platform integration, offline-first behavior, and hardware access push toward native. Internal tools and dashboards often work better on the web. Cross-platform is a reasonable middle for many consumer products.
What happens after you reach out
Send a short brief describing what you're trying to build, what's already in place, and where you're stuck. We'll respond to schedule a scoping conversation, and if the fit looks right, we'll propose a first increment of work. You don't need a finished specification to start — most useful conversations begin with a rough problem statement and a real constraint.