Trusted by companies across the USA
A dedicated LLM developer who builds models you can actually trust
A fintech team shipped a customer-facing chatbot that confidently quoted refund rules that did not exist. They did not need a research scientist. They needed someone who could ground answers in their real policy documents and prove, with numbers, that the model stopped making things up.
Tell us what you need. We will match you within 48 hours.
Trusted by companies across the USA
Your developer wires retrieval over your own documents so answers point back to the paragraph they came from. When the model says something, you can check where it got it.
Before a prompt change reaches users, it runs against a test set with pass and fail thresholds. You stop guessing whether the new version is better or quietly worse.
Your developer adds checks for hallucination, prompt injection, and off-topic replies. The model declines what it should not answer instead of bluffing through it.
Chunking, embeddings, and the database are chosen around how your content is actually structured, not a copy-pasted tutorial setup. Retrieval quality lives or dies here.
Token usage, response times, and per-feature spend get logged and surfaced. You see which prompts are expensive and why, instead of being surprised by a bill.
Fine-tuning runs, eval suites, and data prep are scripted and version-controlled. When your data changes, you rebuild in an afternoon, not a week.
The person who learns your data and your prompts is the person who keeps working on them. We do not rotate people off your project without telling you, and we do not swap in a stranger mid-sprint.
The code, the prompts, the eval sets, and the fine-tuned weights belong to you from the first commit. There is no licensing catch and nothing held hostage if you leave.
Our team is in India and shifts to overlap with US Eastern and Pacific mornings. You get live hours for standups and pairing, plus progress waiting when you log on.
We sign your NDA and a written agreement before the developer touches your repo or your data. That matters more than usual when the work involves your private documents.
If the developer is not right for your team, we match you with someone else and carry over what they already learned. You are not stuck paying out a bad pairing.
You get a working demo or a written progress note every week, not a status color on a dashboard. With a 12-hour gap, visible updates are how trust gets built.
One developer working your hours, in your standups, treating your roadmap as their only job. Best when LLM features are core to the product and need someone in the weeds daily.
Half a developer's month for teams adding LLM features alongside existing work. Enough time to keep a RAG pipeline and its evals moving without a full headcount.
You pay for the hours used, logged and visible, with no minimum monthly block. Good for a proof of concept, an eval audit, or fixing a chatbot that keeps making things up.
More than one person when the work spans data engineering, model tuning, and a frontend. We scope the mix with you and keep the same people on it.
Transparent pricing. No hidden fees.
From first contact to your developer writing code — here is how it works.
Get StartedWe start with a call about what you are actually trying to build, not a sales pitch. You tell us whether it is a support bot, document search, or something tuned to your domain, and what counts as good output. That conversation decides which skills your developer needs.
We send you a shortlist matched to your stack, with real notes on what each person has shipped with LLMs and vector databases. You interview them directly and pick, rather than having someone assigned to you. If none feel right, we keep looking before anyone starts.
The developer signs your NDA, gets access to your repo and data, and reads how your prompts and documents are structured today. By the end of the week they have a local setup running and a written read on where retrieval quality is leaking. No billed feature work until that groundwork is done.
You and the developer agree on a small first sprint with a measurable target, like cutting wrong answers on a fixed test set. We build the eval harness early so progress is a number, not an opinion. You see the plan before the sprint starts and can change it.
From there it settles into a steady weekly cycle with a demo or written update each Friday in your timezone. Your developer is in Slack through your morning hours and on Zoom for planning. You always know what shipped, what broke, and what is next.
Bring the chatbot that keeps making things up, or the document search that never quite works. We will tell you honestly whether you need RAG, fine-tuning, or just better evals before you commit.
Describe your project and requirements.