Practice ii of v · AI-native products
The model belongs in the workflow, not beside it.
Full-stack web and mobile products with models embedded in the core workflow. Built fast, shipped to production and measured by outcome, by the team that will keep shipping them.
Web, iOS and AndroidOne team from the first prototype to release engineering.
01 The problem we solve
A chatbot in the corner is not a product.
Adding a model to an interface is easy. Designing the product so the model does the work, and proving that it does, is the part that decides whether anyone keeps using it.
Bolted on, then ignored
An assistant panel sits beside a workflow that never changed. People keep doing the work the old way, and the model becomes a cost line.
Nobody measured whether it worked
Without an evaluation set and an outcome metric agreed up front, there is no way to tell a better release from a worse one.
The prototype cannot ship
Authentication, offline behaviour, performance and release engineering are where prototypes stall. They are the bulk of a real product.
02 What we deliver
Products shaped around the people who use them every day.
Fast, durable and built once to run everywhere. Every product ships with the evaluation and measurement it needs to keep improving after launch.
AI-native web applications
Production web applications and platforms where the model sits in the core workflow, from retrieval to evaluation to release.
- Custom web apps
- API development
- Performance
Next.js, React, FastAPI, Python
Semantic search and matching
Search and ranking that understands meaning rather than keywords: parsing, skill and entity extraction, and ranked matches people can act on.
- Semantic search
- Ranking and matching
- Recommendation
Elasticsearch, vector databases, Python
Intelligent dashboards
Interfaces that explain what a model decided and why, with the history and factor breakdown a person needs to trust or overrule it.
- Real-time views
- Decision explanations
- Interactive charts
TypeScript, D3.js, Chart.js
Mobile AI interfaces
Native-grade iOS and Android apps that feel considered: built once, polished everywhere and easy to keep shipping.
- Cross-platform
- Offline-first
- Release engineering
React Native, Flutter, Swift, Kotlin
Reading Fig. 1
In an AI-native product the model does a step of the work itself, a person reviews what matters, and the outcome is measured against a number agreed before the build. That loop is what lets the product get better release by release.
03 How an engagement runs
Short cycles, working software, your team in the loop.
We map the objective, the data and what success has to look like, then ship in increments you can put in front of real users.
Engagement path
Discovery
Weeks 1 to 2Fixed-fee assessment: the objective, the users, the data and the outcome metric.
Architecture
Weeks 2 to 4Product design, model choice, evaluation set and a plan you can hold us to.
Build
Week 4 onwardDedicated senior team, minimum three months, shipping working software in short cycles.
Run
OngoingReleases, monitoring, evaluation and incident response under managed operations.
What you get at each step
A document in hand before every decision.
04 · Case study, digital health
Ninety-four percent of recommendations, right.
BetterHealth needed diet and fitness plans that fit each user rather than one plan for everyone. Algobain built the recommendation engine and the mobile product around it.
Read the BetterHealth case study
The brief
Generic diet plans that did not fit users' lives, no link between nutrition and fitness tracking, and no real-time feedback on progress.
What we built
A personalisation engine that weighs 50+ parameters, including BMI, activity level and dietary restrictions, and plans meals from a 1,000+ recipe library, adjusting as progress comes in.
What it runs on
A React Native app for iOS and Android, offline-first, synced with HealthKit and Google Fit, on Node.js APIs and MongoDB with auto-scaling AWS infrastructure.
05 · Insights
Adding AI to a product you already run.
Most AI-native features land inside an existing product. These pieces cover how to make room for them.
06 Questions
What product teams ask first.
- Do you build the whole product, or only the AI?
- The whole product: web and mobile clients, APIs, data, infrastructure and the model work inside it. One team owns it end to end, so nothing is lost between an AI vendor and an app agency.
- Which platforms and stacks?
- Web on Next.js and React; mobile on React Native, Flutter, Swift and Kotlin; services on Node.js, Python and FastAPI. We choose per product and say why in the architecture document.
- How do you know the model is working?
- We agree an evaluation set and an outcome metric during architecture, then measure every release against them in production. On BetterHealth that metric was recommendation accuracy, which reached 94%.
- Can you add AI to the product we already have?
- Yes, where the relevant part of the product has a clean interface. Where it does not, our legacy modernization practice opens a seam first, so the feature can ship without a rewrite.
- Who owns the code?
- Ownership is set in the engagement contract. [Confirm the standard IP assignment wording before publishing.]
- Who keeps shipping it after launch?
- The same team, under managed operations: releases, monitoring, evaluation and incident response. Or we hand over to your team with the reasoning and records attached.
Related practices
What the product runs on.
Have a product worth building?
Tell us who it is for and what it has to change for them. We will come back with the shape of the product and the number it should move.
- 1Within 48 hoursA senior engineer replies. If we are not the right fit, we say so.
- 2NDA, then a working sessionWe sign first, then map the users, the data and the outcome.
- 3A written proposalScope and fixed fee for the two-week assessment.