Logicwind
AI Solutions

AI, running in production.

You’re adding AI to a business that already works. Or building an AI-native product from zero.

Either way, we take it from decision to live production: models, infra, privacy posture, humans in the loop. When AI belongs in the answer, we ship. When it doesn’t, we say so.

The engagementThree-week POC. Fixed timeline, fixed price, weekly demos. If the POC works, we build. If it doesn’t, we say so.Start a conversation
The engagement, itemised

You get.

  • A decision at the end of the POC: build, or don't.

  • A working proof of concept, if the answer is build.

  • A production plan: the infra, the auth, the human handoff, the monitoring.

  • The full build, if you want us to ship it.

You skip.

  • Six-month scoping calls.

  • “AI transformation” workshops.

  • Model demos with no path to production.

  • Promises we can't back.

How the engagement runs.

  1. 01

    Discovery Call

    One honest hour. We ask more than we answer.

    You bring the problem, the constraints, and any prior AI work. We ask what you've tried, what you've ruled out, and what happens if the answer is no. Free.

  2. 02

    Conceptual Agreement

    The scope of the work, in writing.

    One page. What we'll build in the POC, what we won't, what “working” looks like, what it costs, what happens next. Signed before any billable work starts.

  3. 03

    Proof of Concept

    Three weeks to prove the thing belongs.

    A working system in your data, on your infra where possible. Weekly demos. At the end, we tell you whether it works. If it doesn't, we tell you why.

  4. 04

    Full Build

    The production system around the model.

    Auth, queues, retries, the human handoff, monitoring, the interface. Fixed scope, fixed timeline. We ship the version that survives real users, then hand it over or keep operating it, your call.

What we’re actually good at.

Three categories of work. Each one names a real capability, not a positioning claim.

The model call.

Which model. Which orchestration. Which retrieval. When a rule-based system is faster to build and cheaper to run. Twelve years of shipping software teaches you that a boring system that works beats a clever one that mostly does.

The production system around the model.

The parts nobody demos. Auth, queues, retries, timeouts, the second call when the first one flakes, the observability that tells you what actually happened. This is where most AI builds fall short.

The human-in-the-loop layer.

When the model doesn't know, a person needs to. We build the handoff, the review interface, and the feedback loop that lets your team correct the model as it runs. The system gets sharper every week it's in use.

Eugenix logoClient case study

Eugenix.

A doctor-founded hair transplant clinic with four centres in India.

Enquiries arrive at all hours and expect a reply now. Doctors need complete cases before they can assess one. Recovering patients have questions at 11 PM.

Two AI care agents on WhatsApp, built on ZINQ. Tagore screens every new enquiry, collects scalp photos and files a doctor-ready case in the CRM. Ved answers aftercare questions from Eugenix's own protocol and escalates emergencies straight to the medical team.

Every patient gets a reply at any hour. Cases reach doctors complete. Every clinical and commercial decision stays with Eugenix's team.

The Eugenix engagement, in full
Soulera logoClient case study

Soulera.

AI-driven handwriting and signature analysis for schools, organizations and individuals.

Soulera wanted to translate graphological personality analysis, a niche human expertise, into a product anyone could use directly.

We built it end to end. AI models read handwriting for margins, line heights, letter shapes and the other parameters graphologists use. A mobile app delivers instant insights. A consultant portal lets graphologists validate and refine the model as it runs.

Soulera went from idea to a live product. Every cycle of consultant review makes the model sharper.

The Soulera build, in full

The alternatives, honestly.

OptionThe honest readHow we run

Logicwind

Twelve years of shipping software, 120+ engineers, judgment on which model and which orchestration, and the production system around it.

Three-week POC, decision at the end, production build if the POC works.

Offshore agency

Bodies rented by the hour. Little product judgment. Model call gets made by whoever is on the ticket that week.

We give you a fixed team, one decision-maker, and a written scope before any billable work.

In-house build

Hires and ramp before the first POC. Runs on your team's spare cycles until it doesn't.

We ship the POC and the first production version in eight to ten weeks, then your team takes over or we keep operating it. Your call.

Do nothing

The AI question doesn't go away. Competitors ship while you decide.

One honest hour tells you whether AI is the answer for your problem. Free.

Questions.

Things people actually ask.

  • Will our data end up in an OpenAI training set?

    No. We use enterprise inference endpoints with data-privacy controls. Your data does not train the base model, and we can run inside your cloud when compliance requires.

  • What if the POC doesn't work?

    We say so, share what we learned, and don't invoice the build. The POC cost is fixed and paid regardless. You still walk away with a clear answer on whether AI belongs here.

  • What models do you use?

    Whichever fits. GPT-4 class, Claude, Gemini, open-source models, or a hybrid depending on the workload, the privacy needs, and the cost profile. We pick per engagement, not per fashion.

  • Do you fine-tune, or just prompt?

    Mostly prompting with retrieval, because most real problems don't need fine-tuning. When they do, we fine-tune. When they don't, we don't spend your money pretending.

  • How do you handle hallucinations?

    Retrieval from your ground truth, confidence thresholds on the model output, and a human handoff when the model is uncertain. The system knows when it doesn't know.

  • How do you know when to say no to AI?

    When a rule-based system is faster to build, cheaper to run, and easier to maintain. That is most of the time. Boring systems that work beat clever ones that mostly do.

  • Do you handle deployment or just the POC?

    Both. The POC is three weeks. The full build ships the production system on top: infra, auth, monitoring, the human handoff. You can take it in-house at any point after the POC.

  • Do you work with our cloud, or yours?

    Yours by default. AWS, GCP, Azure. We can run in ours for the POC if it's faster, then migrate for the production build.

  • Can we keep the code and operate it ourselves?

    Yes. Every engagement ends with the code in your repo, the infra in your account, and a written handover. We can keep operating it if you want, on a separate scope.

Tell us what you're trying to move.

One honest hour.

You name the thing: the AI feature stuck in staging, the integration that keeps hallucinating, the model demo your team can't ship past.

We ask the questions that come from twelve years of shipping software.

If AI isn't the answer, we tell you plainly.

200M+Revenue generated
100+Products shipped
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