AI integration in Boston
A Boston biotech's most valuable text is rarely public: protocols, standard operating procedures, study reports and years of internal presentations. Scientists want to ask it questions. Quality teams want to know that an answer citing an SOP cites the current version, and that nothing confidential has gone to a provider without an agreement in place.
We build retrieval first and the model second. Documents are indexed with their version and status, so a superseded SOP is never quoted as current, and every answer links to the passage it came from, so a scientist can check it in one click. Before anything is wired up, we map what leaves your systems and where it lands. Where that rules out a hosted provider, an open-weight model on your own infrastructure is a real option, and we price both. Then we write an evaluation set with your scientists, from questions they already ask, and the feature ships only when it beats the search they use today.
Boston's software buyers mostly work in science, medicine, education and money: biotech and pharmaceutical companies in Cambridge and the Seaport, the hospital systems, digital health start-ups, the universities and the companies spun out of them, and the asset managers and insurers downtown. In most of them, software supports something else, an experiment, a patient or a portfolio. The people who commission it are often scientists and clinicians, many of whom write some code themselves and know exactly what they need the data to do. What they want from an engineer is not a new idea. It is the idea they already have, made reliable.
The work follows the science. Biotech labs need instrument output picked up as it lands and linked to the notebook entry that explains it, and internal tools that stay fast on thousands of assay results. Digital health companies need a backend that can read from a hospital's records system, and patient apps that save offline and keep the time each entry was made. Edtech companies selling to universities need platforms that know a teaching assistant from a department administrator. Lab suppliers need commerce that runs on purchase orders and negotiated prices, and the investment firms downtown need internal tools to replace the workbooks that run their operations.
Hiring has its own shape. Built In puts the average software engineer base salary in Boston at about $137,000, and the city's engineers are pulled between big tech offices, well-funded biotech and university spin-outs. The universities keep research talent in good supply. What teams more often lack is someone who has taken a system from a notebook or a prototype to something that runs unattended, is monitored, and survives its author going back to the lab. That gap, between a result that works once and a system that keeps working, is where we are most useful.
We are in Bengaluru and move our working day for Boston. Four hours of every working day overlap with your morning in Eastern time, stand-up included, so questions, reviews and decisions happen live with the engineer who writes the code. Runbooks and architecture notes are written as we go, so the system keeps running when the people who commissioned it go back to the lab.
We are the right fit for the gap between a result that works once and a system that keeps working: a pipeline, a hospital integration, a patient app, an internal tool. Senior capacity starts within days, the first piece is a fixed-price two-week engagement, and it can carry you from a funding round or grant to your first engineering hire.
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 Boston teams.
How do you work with teams in Boston?+
We are in Bengaluru and move our working day for Boston, so four hours of every working day overlap with your morning in Eastern time, stand-up included. We join your Slack, push to your GitHub and track the work in your Linear, so progress shows up where your team already looks. The person on every call is the engineer who writes the code, whether you are a scientist, a clinician or a CTO.
Do you build for Boston's biotech and life sciences companies?+
Yes. The work is usually the software around the science: pipelines that pick up plate reader, sequencer and imaging output as it lands and link it to the notebook entry, internal tools that stay fast on thousands of assay results, and search over protocols and SOPs that always cites the current version. We work alongside your computational scientists, turning what already works in their notebooks into systems that run unattended.
How does your rate compare to hiring in Boston?+
Built In puts the average software engineer base salary in Boston at about $137,000, before bonus 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 it produces.
What do the first two weeks look like?+
You share the context: the code, the data and the problem. Within two days you have a 30-minute call with the engineer who would do the work, and within a week a written plan. Then comes a fixed-price two-week piece at $2,800, such as turning one notebook or prototype into something that runs on its own. At the end you have working code in your repository, notes on how it runs, and a clear view of what comes next.
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.