AI integration in San Francisco
Plenty of established San Francisco software companies have a board asking where the AI feature is. The quick version, a chat box wired to a model, ships in a week and then produces answers nobody can explain, costs that scale with the wrong users, and a support queue full of edge cases. The work that matters comes after the demo.
We integrate AI into existing products with evaluation first: a test set built from your real, anonymised cases, run on every prompt or model change before it ships. Retrieval runs against your own data with permissions respected, costs are tracked per customer, and every response can be traced to its inputs. If the feature holds ongoing conversations with users, we add the pieces your support team needs next: searchable conversation history, clear labels on generated replies and a hand-off to a person.
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.
A prompt that behaved on twenty hand-picked inputs meets ten thousand real ones. We build the evaluation set from your actual traffic first, so a change can be judged rather than argued about.
Usually a large context sent on every call, or a big model doing a job a small one can do. We measure where the tokens go before recommending anything.
Logging that captures the prompt, the retrieved context, the model version and the output, so a support question has an answer that is not a guess.
A fallback path, a timeout that is shorter than your user’s patience, and a degraded mode that is honest about being degraded.
A weekly maintenance pulse where Claude writes only the prose, checked against a JSON schema and a whitelist of numbers, with a template that takes over when the model fails.
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.
Will you tell us if we do not need a model?+
Yes, and it happens often. Several requests we have taken turned out to be a search problem, a rules engine, or a form with better defaults. We would rather say that in week one than bill for a year of prompt tuning.
Whose API keys?+
Yours, in your accounts, with the spend visible to you. We never proxy your traffic through infrastructure we control.
What about our data going to a provider?+
We map exactly what leaves your systems and where it lands before anything is wired up. Where that is not acceptable, self-hosted open-weight models on your own hardware are a real option and we will price both.
Do you fine-tune?+
Rarely, and not as a first move. Retrieval and prompt structure fix most of what people bring to us as a fine-tuning problem, at a fraction of the cost and with none of the retraining treadmill.