AI integration in Austin
Every Austin enterprise SaaS roadmap now has an AI line on it, usually put there by the board or the largest customer. The demo is easy: a model, a prompt and five documents. The product is harder. Real tenants have thousands of documents in formats nobody tested, the answers need to point at their sources, the cost per request has to fit the pricing, and one customer's data must never appear in another customer's answer.
We build the feature around those constraints. Before anything is wired up, we map what data the feature needs, which provider receives it and what that provider keeps. Retrieval is scoped to the tenant it belongs to, so one customer's search can never surface another's documents. Every call is logged with its cost, an evaluation set catches regressions when a prompt or model changes, and a fallback keeps the product working when the provider is down. Your customers get answers they can check, and you get a feature whose cost you can predict.
The city's centre of gravity is enterprise SaaS and consumer fintech, and both bring the same backend problem early: multi-tenancy that was bolted on after the first enterprise deal, and a permissions model that started as a role column. Then the large customer asks for custom roles and SSO, the board asks for usage-based pricing, and the schema was built for neither. Unpicking that while the product keeps selling is most of what an Austin engagement looks like in the first month. On the fintech side the same pressure lands on the mobile app and the ledger: transfers that must never send twice, and balances that have to match what finance sees.
Austin has also been a hardware and semiconductor town for decades. The fabs and equipment suppliers produce tool telemetry, inspection images and test results in volumes most software companies never see, and the hardware start-ups around them reach the point where a board works on the bench and a pilot customer wants to see its data. Their engineers are process, yield and firmware specialists. The software around the hardware, from the device API and the dashboard to the pipeline that turns readings into something a customer can open, is a defined piece of work that suits an outside team.
Then there is timing. Austin has an unusual density of Series A and B product companies, and they tend to raise, hire aggressively for two quarters, then freeze. A mid-level engineer costs around $145,000 base before equity and benefits, and the platform or infrastructure hire is usually the one that waits longest. We are frequently the answer to "we have the roadmap and the budget, but the req is frozen until next quarter", because an engagement is an invoice rather than a headcount. It starts with a fixed-price two-week piece, and it can grow into an embedded engineer for as long as the roadmap needs one.
We move our working day for Texas. Four hours of every working day overlap with your morning in Central time, so stand-up, code review and the design conversation happen live with the engineer who writes the code. The code lives in your repositories from the first commit, and runbooks and architecture notes are written as we go, so whoever you hire next inherits work they can read.
We are the right fit when the roadmap is ready and the req is frozen. A defined piece of work, such as a permissions model, a device backend or a cloud setup brought under control, starts with a fixed-price two-week piece. Senior capacity arrives within days, and agencies can put the work 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 Austin teams.
How do you work with teams in Austin?+
We are in Bengaluru and move our working day for Texas, so four hours of every working day overlap with your morning in Central time. Stand-ups, code reviews and design calls happen live in that window. We work in your tools, whether that is Slack, GitHub or Linear, and the person on every call is the engineer who writes the code.
Why do Austin product companies come to you?+
Austin has an unusual density of Series A and B product companies with a strong front-end team and no infrastructure hire yet. That is precisely the seat we fill: the multi-tenant backend, the permissions model, the cloud setup and the deploys that nobody senior owns. It is why more of our US enquiries come from Texas than from the coasts. We build it in your codebase, alongside the team you already have.
How does your rate compare to hiring in Austin?+
A mid-level software engineer in Austin earns around $145,000 base before equity and benefits. Our published rate is $35/hour, or $5,400 a month for an embedded engineer, with a $5,000 minimum. There is no recruiting time and no employment overhead, and work starts within days of the call. The first piece is a fixed-price two-week engagement at $2,800, so you judge us on what ships.
Our req is frozen until next quarter. Can you cover the gap?+
Yes. It is a common Austin situation, and a good fit for an engagement, because an invoice is not a headcount. The roadmap keeps moving while the req waits. Start with a fixed-price two-week piece, or embed an engineer at $5,400 a month. Runbooks and architecture notes are written as we go, so when your hire does start, they inherit work they can read and extend.
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.