AI development services that ship features your users would miss

AI development services include scoping the decision the AI supports, building graded test cases, the retrieval and model work, the human sign-off, and running the system after launch. Hashlogics delivers all of it with senior engineers, for enterprises and startups.

A custom AI solution is AI built around your own data, workflow and rules rather than a packaged tool configured from a menu. It reads your documents or records, works inside your systems, and is tested against cases your experts answered.

By design, before the first model call. Access is scoped per role, every model call and human decision is logged, data stays in the region your contracts require, and anything irreversible waits for a person. TrialTriage runs with a 23-action audit trail and nurse sign-off on every result.

Ask five things: who wrote the graded test cases, which engineer owns it after launch, where your data goes, whether you own the code and prompts, and what the system must never do alone. A provider who answers all five in writing has shipped before.

Yes, and most of our AI work is exactly that. Cruise Search AI is an AI search module inside the client's existing WordPress site, and ZhoopZhoop's voice agents sit inside a running collision-repair business. We add the feature, its test cases and its monitoring without rebuilding your product. The first 2 months of support and maintenance are free, with every build.

Yes. Go4Gr8 moved its AI coaching product off TypingMind onto a custom multi-tenant platform we built. For a startup we ship one feature narrow, score it on real cases, and grow it once users rely on it.

There's no honest figure before an engineer reads your data and systems. The scoping call is free. Every build starts with a fixed-fee Blueprint, credited in full against the build, and if the plan isn't one you'd act on, you don't pay for it. Builds are fixed-price per milestone.

Headquarters: East Islip, NY · Engineering: Lahore. Our client work runs in the US, the UK, Switzerland and Australia, delivered remotely.

You score it against cases your own experts answered first, and you set the passing bar before you see the results. That set becomes the thing you re-run on every prompt, model or data change. Without it, accuracy is an opinion held by whoever built the feature.

Your data goes wherever the provider's current terms say it goes, which is why we put that in writing before anything is built. We check the vendor's current terms rather than repeating what was true last year, then design around what they actually commit to. Where the data is sensitive, we keep the model away from the parts it does not need. TrialTriage de-identifies patient data before anything reaches a model.

Pick by the job, then keep the choice reversible. We have shipped on Claude, OpenAI models, Fireworks AI and Perplexity Sonar-Pro depending on what the workflow needed. The model name stays in configuration. Anthropic gives at least 60 days' notice before retiring a public model, and 60 days is short if that name is hardcoded in twelve places.

Usually not. Most business problems are retrieval problems, where the answer exists in your documents and the system needs to find and cite it. Fine-tuning teaches a model a style or a format, not a fact, and it makes every future model upgrade more expensive.

Running cost is driven by request volume, how much text each one carries, and how often you need the most expensive model. Those are the levers, and we design so the cheap model handles the easy cases. The scoping call is free, and the fixed-fee Blueprint, credited in full against the build, names the drivers before you commit.

Every build we ship has a defined path to a person, and anything irreversible sits behind that gate by design. When something does slip through, the trace shows the input, what was retrieved and what the model chose, so the fix targets the actual cause. A system you cannot replay can only be guessed at.

Both work, and the shape differs. A small team usually wants one AI feature live and evidence users rely on it, so we ship narrow and score it. An enterprise wants permissions, audit trails and a migration plan from the start, which is what Go4Gr8 needed to run enterprise pilots on AWS.