Custom software development in San Francisco
Not every San Francisco software buyer is a startup. The biotech companies around Mission Bay and South San Francisco run laboratories where samples, instruments and results are tracked in spreadsheets and a commercial system that covers most, but not all, of the workflow. The gap between them is where errors creep in.
We build internal tools that fill that gap: sample tracking, instrument data pulled into one place, and dashboards that show what is waiting on whom. Every record carries a history of who changed what and when, so a result can be traced back to the sample and the run that produced it. The tools sit alongside the systems you already use, and your scientists get one view of the work in place of three.
San Francisco's software market is, for now, largely an AI market. The city is home to the best-known model companies and to a much larger layer of venture-backed startups building products on top of their models, alongside the SaaS, fintech and developer-tool companies that were here before. Most buyers are young companies with funding, a deadline set by their next raise, and more product ideas than engineers.
What they need is rarely the model. It is everything around it: queues for requests that take most of a minute, streaming that survives a dropped connection, fallbacks when a provider is rate-limiting, a record of what each request cost, and evaluation sets that tell you whether last night's prompt change made things better or worse. Then, often within months, the first enterprise customer arrives, and its IT team asks for single sign-on, automatic provisioning, roles their own admin can manage and an activity log they can export. A backend built for a demo meeting that list is where many pilots stall. We build the product and the infrastructure that make a model useful to paying customers, and the enterprise layer that turns a pilot into a contract.
Built In puts the average base salary for a software engineer in San Francisco at around $181,000, and the model companies compete for the same people with equity most startups cannot match. For a seed or Series A company, every senior hire becomes a search measured in months, and the roadmap waits while it runs. The work that piles up in the meantime is usually well defined: a provider integration, an admin console, a move to usage-based billing, a mobile companion app. That is the work we pick up in days and deliver in pieces you can judge on their own.
We work from Bengaluru with four hours of live overlap every working day, placed across your morning in San Francisco. Stand-ups, design calls and code review happen in that window, with the engineer who writes the code. Everything decided outside it goes into your repository, your tracker and the architecture notes we write as we go, so you start each day knowing what moved.
We fit best when the work is defined and the roadmap will not wait for a hire: the product and infrastructure around a model, the enterprise features a first large customer asks for, or the months before your next engineer starts. Most teams begin with a fixed-price two-week piece. Agencies can bring us in white-label, under their own name.
Four ways this arrives.
Approvals, schedules or stock, tracked in a shared file with colour codes only one person understands. We turn it into an application with proper records, permissions and history, so you can see who changed what and when, and nobody works from an old copy.
Staff copy data from one system into another by hand, and the two slowly drift apart. We connect them through their APIs, clean up the data where it crosses over, and alert someone when a sync fails instead of letting it fail quietly.
The numbers exist, spread across exports and inboxes, and someone assembles a report by hand every Monday. We pull them into one place, build the dashboard the people making decisions will actually open, and check it is current rather than trusting the job that feeds it.
A legacy application still runs the business and every change to it is a risk. We map what it really does, move it piece by piece onto something maintainable, and keep both running until the last piece has landed. No weekend cut-over.
A facilities operations platform for a healthcare estate, built so supervisors can see what is overdue and what keeps coming back without exporting spreadsheets by hand.
Read the write-up →Asked by San Francisco teams.
How do you work with teams in San Francisco?+
We are in Bengaluru and overlap with you for four hours every working day, placed across your San Francisco morning. Stand-ups, design calls and code review happen live in that window, with the engineer who writes the code. We work inside your tools: Slack for conversation, GitHub for code and review, Linear for the plan. Anything decided outside the window is written down there, so nothing depends on memory.
Do you build for AI startups?+
Yes. Most of that work sits around the model rather than inside it: retrieval over your own data, evaluation sets that run on every prompt change, streaming interfaces, cost tracking per customer, fallbacks between providers, and logging that explains an answer after the fact. Then comes the enterprise layer your first large customer asks for: single sign-on, provisioning, admin roles and activity logs, built so the pilot can become a contract.
How does your rate compare to hiring in San Francisco?+
Built In puts the average base salary for a software engineer in San Francisco at about $181,000, before equity and benefits. Our published rate is $35 an hour, with a $5,000 minimum. There is no recruiting search and no employment overhead, and we can start within days. Most teams begin with a fixed-price two-week piece at $2,800, so you judge us on working code before committing to more.
Can you make our prototype ready for real customers?+
Yes, and it is a common place to start here. A prototype built fast for a demo usually needs the same things: model calls moved into queued, retried jobs, costs recorded per request, tests around the parts that change most, and deploys from CI. We read the code, send a written plan within a week, then take the most urgent piece as a fixed-price two-week job. The repository is yours throughout.
Why not buy an off-the-shelf tool?+
Often you should, and we will tell you when. If a product already does most of what you need, configuring it is cheaper than building. Custom software earns its cost when the process is how you compete, when you pay for several tools to do one job badly, or when the workarounds cost more staff time than the software would.
Can you work with the systems we already have?+
That is usually the job. Most internal tools are valuable because of what they connect to: an ERP, a work-order system, a CRM, a finance package. If it has an API, we integrate with it. If all it offers is a nightly export, we can work with that too, and we will tell you how fresh the data can honestly be.
How do you find out what we actually need?+
By talking to the people who do the work, not only the people who asked for the tool. We watch the spreadsheet being used, collect the edge cases everyone handles from memory, and write the process down before we build anything. The first release covers the core of it; the exceptions follow once people are using it.
What happens to our old data?+
It comes with you. We migrate it, check the counts and totals against the source, and keep the old system or spreadsheet readable until your team is confident nothing was lost. Where the old data is inconsistent, we show you the records that do not fit rather than quietly guessing what they meant.
Who looks after it once it is live?+
You choose. We can stay on at our hourly rate for changes and fixes, or hand it to your own team with runbooks, architecture notes and a recorded walkthrough. Internal tools change as the business changes, so we build them to be changed by someone other than us.