Services

AI features, past the chat box

Two years ago an AI feature was the product. Now it is the baseline: people assume the tedious parts are handled, and a product that still asks them to do it by hand starts to feel dated. That rarely means bolting on a chat panel. Most of the features that earn their place never announce themselves — the field that fills itself, the list already in the right order, the summary above a thread nobody wants to read.

Where teams usually start

  • There is a model in the product already, behind a chat panel nobody opens.
  • AI is on the roadmap and nobody can say what it would do for a customer.
  • It works in the demo and falls apart on your real data.
  • Support and legal are nervous, and nobody has designed what happens when it is wrong.

What the work covers

Where a model actually helps

The unglamorous list first: what people retype, wait for, or ask support about. Those are the candidates. A chat box is rarely one of them.

Interfaces that are not chat

Suggestions in place, defaults already filled, drafts to accept or reject, a summary above the detail. The model does the work; the thing stays a product rather than a conversation.

Designing for the wrong answer

Confidence shown honestly, an obvious way to undo, and a path back to doing it by hand. Trust is lost once.

Latency as a design problem

Models are slow and uneven. What the screen does with those seconds is the difference between a product that feels fast and one that feels broken.

Limits, costs and what gets sent

What leaves the product, what each action costs, and what happens at the rate limit — decided in design rather than discovered in production.

Evaluated before it ships

Real inputs from your data, an agreed idea of what a good answer looks like, and sessions with the people who will live with the result.

How it runs

  1. We start from the job, not the model

    What is someone trying to do, and where is it slow or repetitive? Then whether a model is the best answer. Sometimes it is a better default or a saved filter, and we will say so.

  2. Prototyped on your data

    Sample data makes everything look convincing. We test on the real thing, because that is where hallucination, latency and the awkward edge cases live.

  3. Shipped small

    One feature, in front of real users, with the fallbacks in place — before it becomes a line on the roadmap for the next four.

  4. The failure path is designed too

    The wrong answer, the empty answer, the slow answer and the refusal, given the same attention as the case where it works.

What it costs

Two monthly subscriptions and a fixed-price option. Pause for up to two months or cancel with three weeks’ notice — the terms are the same whichever you pick.

Selected work

See all work
Easy Tiger

UXBOX Company LTD

We are building easytiger.vn, Vietnam's only platform dedicated to helping people navigate the country's bureaucracy by connecting them with verified local experts. Built for AI agents, every expert profile is structured for AEO and GEO so answer engines surface them directly.

GoTeach

GoTeach Collective

GoTeach.vn is Vietnam’s first platform connecting private tutors and coaches with students. In just 3 months, we helped them validate their idea, design their brand, build the platform, and launch—aided by AI, low-code technology, and a localized go-to-market strategy.

Invoicing.mu

Netch Digital

Invoicing.mu aims to become Mauritius’s leading e-invoicing SaaS in terms of user experience and seamless onboarding. With new legislation pushing SMEs to adopt compliant digital invoicing, Invoicing.mu positions itself as the affordable, user-friendly solution businesses can trust.

Tell us what your customers retype.

The task people do by hand every week is usually the feature. Send it, and we will tell you whether a model is the answer.

Other services

Where this sits in our product plan 

Does our product need AI features?

Not as something to announce. It needs them where they remove work a customer is doing by hand — and increasingly they expect that, because the products they use all day have set the baseline. If there is no such job in your product, adding a model is a cost, not a feature.

Does this mean building a chatbot?

Usually not. Chat is one interface, and it is the one that asks the customer to do the thinking: work out what to ask, phrase it, judge the answer. Most of the value is in features that do not look like AI at all — a draft ready to edit, a list already ordered, a form that arrives filled in.

What about hallucination, and trust?

Design for it. Show what the answer is based on, make the confidence honest, keep undo one click away, and leave the manual path in place. A feature that is right most of the time and unrecoverable when it is wrong will be turned off.

Our data is sensitive.

Then what leaves the product is a design decision, not an implementation detail. We work within your constraints — what is sent, what is retained, what stays on your side — and the interface says so where a customer would reasonably want to know.

Can you build it, or only design it?

We build MVPs ourselves with AI-assisted workflows, and keep a vetted network of engineers for production work. On an existing product we design for your team and stay available while they build it.

How much does this cost?

AI work is part of the engagement rather than a separate line: Fractional is $4.2K a month, Full-time is $7.7K a month, and fixed-price projects start from $30K. The pricing page states all three in your own currency.

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