Generative AI that drafts the work and proves it right

A generative AI development company builds features where a model writes, reads or converts something inside your software: first drafts, document extraction, summaries, and plain-language requests turned into structured actions. Hashlogics builds each one with graded test cases and a human approval step where a mistake would cost you.

Your experts answer a set of real cases first, and every release is scored against them before it ships. Answers are grounded in your documents with the source cited, and anything the system can't support it says it doesn't know. The same cases re-run whenever the model or prompt changes.

You ground it and you gate it. Grounding means the system answers from your documents and cites the passage it used, so a reviewer can check in seconds. Gating means the actions you cannot take back wait for a person. Where your documents do not cover a question, the system says so instead of guessing. Most demos are missing that behaviour.

We pick per job and keep the model name in configuration, so switching is a config change plus a re-run of your graded cases. We ship on Claude, OpenAI and Fireworks depending on the task and the data rules. Anthropic gives at least 60 days' notice before retiring a public model. That is plenty of time if your test set exists, and nowhere near enough if it does not.

Yes, and most of ours did. Military Cruise Deals got Cruise Search AI, a search module inside the WordPress site they already ran. Scoping conversations cost nothing. Every build starts with a fixed-fee Blueprint: an engineer reads your real systems and writes the plan, with a fixed price for each milestone rather than a guess. The fee is credited in full against the build, and if the plan isn't one you'd act on, you don't pay for it.

Model spend tracks volume and how much text each call carries, and it is usually small next to the engineering around it. An internal tool answering a few hundred queries a day costs little. The lever that actually moves the bill is how much context you send on every call, which is a design decision we make with you.

Yes, and we pick the cheapest method that passes your graded cases. Prompting comes first, retrieval over your documents second, and fine-tuning last, because fine-tuning changes how a model writes but does not reliably teach it new facts. Where data can't leave your servers, we run open-weight models such as Llama on vLLM.

You do. Source code, prompts, the graded test cases, the data pipelines and the infrastructure accounts are yours, in writing, before work starts. The eval set matters most here, because it is the asset that lets any competent team take over from us later. Every build also includes the first 2 months of support and maintenance, free.

One narrow feature shipped properly is the normal way to start, and several systems we run began that way. Golancer is a small team's product with AI-generated daily priorities inside it. The scaffolding scales down: a small graded set still beats no graded set.

The honest answer names your provider and your plan, and we confirm it in writing during scoping rather than from memory. We build for data rules rather than around them. TrialTriage runs on de-identified patient data with field-level masking and an audit trail across 23 tracked actions. Your compliance team gets that design documented before anything ships.