AI integration in Seattle
For many Seattle companies the fastest route to an AI feature is the one procurement has already approved. If your estate runs on AWS or Azure, model access through Amazon Bedrock or Azure OpenAI usually sits inside the agreement you have, keeps data inside your own cloud account, and avoids a new vendor review that could take a quarter.
We build on whatever your organisation has approved. The feature gets an evaluation set built from your real, anonymised cases, a record of every request with its cost, permission-aware retrieval over your own data, and a fallback when the model is slow or unsure. We keep provider access behind one interface, so if procurement later approves a different model, switching is a configuration change and a test run rather than a rewrite.
Seattle's software market was shaped by Amazon and Microsoft, and by the cloud platforms they run. Many of the region's startups were founded by people who left one or the other, and they cluster in cloud infrastructure, developer tools, enterprise software and, increasingly, AI. Around them sit life sciences and global health organisations, retail and outdoor brands, and a long tail of companies whose entire estate lives in AWS or Azure. Buyers here have usually run services at scale themselves, and it shows in what they ask for.
The practical effect is more rigour and less explaining. A Seattle buyer expects a written design before code, with the alternatives considered and the reasons one was chosen. They count metrics, alarms and a runbook as part of done, not a later phase, and they want trade-offs stated plainly. Most already have a cloud agreement and committed spend on AWS or Azure, so the job is usually to make that estate tidier, cheaper and better understood. The other common pattern is the founder who left a large company and misses its internal platforms, because at a startup nobody has built them yet.
Built In puts the average base salary for a software engineer in Seattle at around $149,000, and the large employers add stock on top, which is what a startup is really competing against. A senior search can run for months, and the work that waits is often the most concrete on the roadmap: a service split out of a monolith, a migration, a cloud clean-up, a model moved from a notebook into production. Each has a clear finish line, which makes it a good fit for a small team that writes the design first and the runbook as it goes.
We work from Bengaluru with four hours of live overlap every working day, placed across your morning in Seattle. Stand-ups and design reviews 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 each day starts with a written record of what changed.
We fit best on work with a clear finish line: a new service, a migration, a cloud clean-up or a model moved into production. We add senior capacity within days while a req stays open, starting 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 Seattle teams.
How do you work with teams in Seattle?+
From Bengaluru, with four hours of live overlap every working day placed across your Seattle morning. Stand-ups and design reviews happen in that window, and you talk to the engineer who writes the code. We work in your Slack, your GitHub and your Linear. Designs are reviewed before code, and runbooks and architecture notes are written as we go, so the record lives where your team already works.
Do you work with teams already running on AWS or Azure?+
Yes, and in Seattle that is most teams. We work inside the estate you have: hand-built resources imported into Terraform, serverless functions made idempotent and traced end to end, cloud costs broken down per customer and feature, and AI features built on Bedrock or Azure OpenAI through the access your agreement already covers. The aim is an account that is tidier, cheaper and easier to explain, on the cloud you already pay for.
How does your rate compare to hiring in Seattle?+
Built In puts the average base salary for a software engineer in Seattle at about $149,000, before stock, benefits and recruiting fees. 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. The usual first step is a fixed-price two-week piece at $2,800, so you judge us on shipped work before committing to more.
What do the first two weeks look like?+
You share the context, and within two days you have a 30-minute call with the engineer who would do the work. Within a week you get a written plan: the options, the one we recommend and why, and how it rolls out. Then comes a fixed-price two-week piece, built in your repository from the first commit, with the design reviewed before code and the runbook written as it goes.
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.