Native iOS Apps
Swift and SwiftUI against current SDKs, architected for the scale you are planning for rather than the traffic you have today. Built the way your iOS engineers would build it if they had the hours.
We design, build, and rescue native iOS and Android products for Bay Area startups, scaleups, and technology companies. The engineers who scope your architecture are the ones who write it, from the first call through post-launch support.
30 Minutes | No Obligation
As one of the top mobile app development firms in San Francisco, we’re recognised across the industry for the work we deliver.
Most agency pages are written for a buyer who wants to be reassured. This one is not, because in San Francisco the person on the other side of the table usually ships software for a living. That changes the first call. Instead of asking how long it takes, they ask how you handle state, why you chose that persistence layer, and what happens to the data model at version four. They are not testing whether you sound competent. They are checking whether the architecture survives contact with their own judgement.
The scepticism is earned. Ask around and most technical founders here have a story about an agency, and it is usually the same story: the demo was clean, the codebase underneath was not, and the real cost showed up eighteen months later as a rewrite nobody budgeted for. The engineering is also genuinely harder. A consumer app out of SOMA is judged on day-seven retention before anyone looks at the feature list. A B2B product selling upmarket stalls in security review, not in the sales cycle. An AI company’s mobile surface lives or dies on first-token latency, because a two-second wait makes the whole product feel broken regardless of how good the model is. So as mobile app developers in San Francisco, we start where those conversations end. We bring the system design to the first meeting, explain what we rejected and why, and tell you which parts of your roadmap we would cut. If your engineers disagree, that is the point. Better to argue the contract than after the third sprint.
Full-lifecycle mobile application development in San Francisco, from the first architecture conversation through years of production support.
Swift and SwiftUI against current SDKs, architected for the scale you are planning for rather than the traffic you have today. Built the way your iOS engineers would build it if they had the hours.
Kotlin and Jetpack Compose, tested across the device range your analytics actually show rather than the newest Pixel.
React Native and Flutter, where one codebase genuinely serves both without a performance cost. We tell you when it does not, which is more often than most agencies admit.
A tightly scoped first version in 8 to 12 weeks, instrumented from the start and built so version two extends it instead of replacing it.
Mobile surfaces for AI products, where inference latency, streaming responses, and graceful failure decide whether the experience works at all.
SSO, role-based access, audit logging, and the security posture your buyers’ procurement teams will actively test before a seat closes.
Onboarding, retention mechanics, push strategy, headless commerce, and checkout built for a launch spike rather than a Tuesday.
A vendor who vanished, a stalled build, or a codebase you have outgrown. We audit, stabilise what holds, and migrate in phases while the current product stays live.
Where most scaling failures actually begin. Data layer, real-time infrastructure, wearable and device integration, plus SLA-backed support once you are live.
AI Engineering
Every company here has an AI roadmap. The gap is between the demo that impressed the board and something that holds up against real latency, real load, and California’s disclosure rules.
Active pipeline
Models that answer from your own product data, documentation, or knowledge base, with retrieval quality you can measure rather than hope for.
First-token time is the difference between a product that feels alive and one that feels broken. We engineer around it: streaming rendering, aggressive caching, and speculative prefetch.
Multi-step agents that call tools, act on your systems, and stop when they should. Built with permission boundaries and human checkpoints rather than autonomy for its own sake.
Voice, camera, and document capture as product inputs. Transcription, classification, and extraction running wherever latency and privacy require.
Inference bills scale with success. We route between models by task, cache aggressively, and instrument spend so growth does not quietly become a margin problem.
Prompt versioning, golden test sets, and output monitoring in CI. The part nobody demos and every production AI feature needs by month two.
The standard agency model is to win the work with principals and deliver it with whoever is free. It is cheaper to run, and it is why so many Bay Area teams inherit a codebase nobody wants to open.
We do not keep a bench, which limits how many projects we take and settles who is writing your code.
The engineers in your technical discovery are the ones committing code. There is no delivery team introduced in the starting week and no version of us you have not already met.
Documented decisions, real test coverage, and conventions your in-house engineers recognise. We write assuming someone else maintains it, because eventually someone will.
Your CTO asks our engineers directly. Nobody relays an architecture question in one direction and returns with a partial answer.
Phase 01
We define success commercially and technically: users, goals, scale assumptions, and the compliance scope that applies, including CCPA and CPRA for anything touching California consumer data.
Expected load, acceptable latency, and growth assumptions written down as numbers during discovery, not discovered during a launch-week incident.
A technical blueprint, a backlog ordered by impact rather than build convenience, and a timeline we will be held to.
Phase 02
Clickable prototypes tested with real users before production code exists.
Backend infrastructure, schema, dependencies, and scaling path mapped before anything gets built on top of them.
Encryption, authentication, access control, and data handling designed in, because retrofitting them before an enterprise security review is slow and expensive.
Phase 03
Two-week cycles ending in a build you can install. Your engineers are welcome in our code reviews.
Automated and manual testing across real hardware, plus load testing for anything expecting growth.
App Store and Play submission handled end-to-end, then crash analytics, patching, and the next phase planned from real usage data.
The languages, frameworks, cloud platforms and data tools our engineers work in every day.
We publish ranges because your time is worth more than three discovery calls before anyone says a number.
15,000 USD to 25,000 USD
For founders validating a product. Core functionality, standard UI, instrumentation, and the backend integrations needed to reach real users.
Timeline: 8 to 12 Weeks
25,000 USD to 120,000 USD
Custom UI/UX, cross-platform reach, advanced integrations, and a secure admin layer, for companies that need something feature-complete.
Timeline: 4 to 6 Months
120,000+ USD
Complex integrations, offline sync, full compliance adherence, and architecture built for sustained scale.
Timeline: 8+ Months
Someone who understands your architecture well enough to answer a question about it, sits in the code reviews, and raises risk before you think to ask. Assigned at kickoff and on the account until post-launch support ends.
A defined build with a clear endpoint and a number agreed before anyone writes code.
A squad assigned to your product and nothing else, sized to the work rather than to our bench.
Our developers inside your standups, your repository, and your review process. The most common model for SF clients who already have an engineering team.
Most Bay Area engagements start embedded or fixed-scope and shift as the product matures. Changing models is a conversation, not a contract negotiation.
CCPA and CPRA govern how consumer data is collected, sold, and deleted, and the California Privacy Protection Agency has moved from advising to enforcing. The state also requires disclosure when a user is talking to a bot rather than a person, which catches a lot of AI chat features built without it in mind. We handle those alongside SOC 2 for enterprise sales, HIPAA where health data is involved, PCI DSS for payments, and GDPR and the EU AI Act where your users sit outside the US. Audit documentation is produced during the build, not the week before a security review.
Every architectural decision comes with what we considered and why we chose otherwise. Your engineers can disagree with us.
Architecture sized against your growth assumptions rather than your current traffic, so a good quarter does not become an incident.
Analytics, crash reporting, and performance monitoring built in rather than bolted on when someone asks why retention dropped.
Documentation, knowledge transfer, and a codebase your in-house team can take over cleanly whenever you are ready.
Encryption, access control, and audit logging designed in from sprint one, because your enterprise buyers will test all three.
If a feature is not worth the engineering, or hiring beats outsourcing for what you need, we say so. That conversation costs us revenue and saves you more.
Sectors We Build For
As one of the leading app development companies in San Francisco, our technical experience spans the sectors the Bay Area runs on.
Discuss Your ProjectProven Results
Reference builds showing our approach to recurring problems across Bay Area sectors. Client engagements under NDA are described by problem rather than by name.
Case studies
Case Study 01 AI Products
Mobile Surface for an LLM Product
AI Products
Case Study 02 B2B Payments
B2B Payments Platform
B2B Payments
Case Study 03 Health
Remote Patient Monitoring
Health
Security and regulatory compliance are built into every phase of the build, so your software stays aligned with the standards your industry demands.
Our engineers maintain up-to-date cloud and engineering certifications, keeping your projects on modern, well-supported platforms.
Recognised across the industry for dependable delivery, measurable client outcomes, and long-term engineering partnerships.
Built for the Bay Area
A product that has outgrown its architecture, a build that stalled, a security review you keep failing, or an idea that needs a team to ship it properly.
A senior engineer comes back within one business day with an honest read on scope, approach, and fit.
Common Questions
Most projects land between $15,000 for a lean MVP and $120,000 or more for enterprise-scale platforms, with compliance, AI requirements, and expected scale driving most of the variance.
Bay Area products usually carry heavier expectations around scale, security review readiness, and AI infrastructure. Those add engineering time regardless of where the team sits.
Ranges are published before the first call, and scope is fixed after discovery. Nothing gets added mid-project without a conversation first.
A scoped MVP focused on your core feature set typically launches in 8 to 12 weeks, enough to generate real retention data before the next raise.
It depends heavily on whether inference runs on-device or in the cloud, and how much evaluation and guardrail work the feature needs. We scope it explicitly rather than folding it into a general estimate.
Yes. Most engagements convert to a retainer once the first version is live, covering monitoring, patching, and continued feature work.
Usually for capacity, or for depth we have and you do not. Most of our Bay Area work is embedded engineers extending an existing team rather than replacing one.
Yes, and we would prefer it. Technical review before signing surfaces disagreements while they are still cheap to resolve.
Yes. Architecture sessions, sprint planning, and launch work can all happen in person across the Bay Area.
Ask who specifically writes the code, what handover looks like, how they instrument for analytics, and whether they will tell you when a feature is not worth building.
Both, though what we recommend differs sharply by stage. At pre-seed, the right answer is often a smaller build than the one you arrived asking for.
We plan for it from the start. Documentation, knowledge transfer sessions, and a codebase written to be inherited rather than to create dependency.
Native in Swift and Kotlin for performance-sensitive or hardware-integrated products. React Native when speed to market matters more, which for a seed-stage consumer product it often does.
Yes, provided the data layer supports it. Retrieval-augmented search, on-device inference, agentic workflows, and multimodal input can all be added. We audit first and say honestly if the foundation will not carry it.
Yes. We start with a technical audit establishing what is usable, flag security and stability issues, then build a plan to finish it.
You do, entirely. Code sits in your repository from the first commit, and all architecture and design assets are yours.
Consumer data rights, deletion workflows, and disclosure requirements are designed into the data architecture rather than added later. California also requires disclosure when a user is interacting with a bot in certain contexts. Requirements here move quickly, so we scope current obligations at kickoff.
Yes. SSO, audit logging, access control, and the documentation pack procurement teams ask for are all things we build rather than retrofit. We sign an NDA before any project specifics are discussed.