AI-assisted mobile and web products
User-facing products that explain, generate, classify, summarize or recommend inside a clear product experience — not a chat box bolted to a homepage.
AI Development
Practical AI products and integrations built around a real use case.
Bytecraft applies AI where it genuinely improves a product or removes repetitive work — starting from the problem and the data flow, not the label.
User
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The product answers, shows where the answer came from, and records the usage against the account.
User
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Saying “I am not sure” is a designed outcome. A product that guesses here is worse than one that stops.
User
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Outages are not an edge case, they are a Tuesday. The product has to fail without costing the user anything.
AI should improve the product or the workflow, not exist as a feature without a purpose.
Connecting a model takes an afternoon. What decides whether the feature is useful in real conditions is everything around it: the experience, the instructions, the data boundaries, the failure states, the cost, the latency, the quality checks, the analytics and the human oversight.
Most AI features that get quietly removed were not removed because the model was bad. They were removed because nobody designed what happens on the days it is wrong, slow or unavailable.
A demo needs
- A model connection
- A prompt
- A happy-path example
Production also needs
- Data boundaries
- Uncertainty handling
- Retries and timeouts
- Access limits and credits
- Cost monitoring
- Usage analytics
What we build
AI capabilities connected to complete digital products.
Knowledge and support assistants
Interfaces that help people find and understand information from an approved source, with defined boundaries and sensible fallback behaviour.
Intelligent workflow automation
AI-assisted steps inside real processes — extracting information, preparing drafts, routing work — while keeping human review where it matters.
Content and data processing
Structured workflows for summarization, classification, transformation and extraction, where the input and expected output can be clearly defined.
AI integrations
Connecting suitable AI services to an existing mobile app, web platform, backend, internal tool or customer workflow.
AI product foundations
Backend orchestration, access control, usage limits, observability, failure handling, analytics and cost-aware decisions behind the visible response.
Fit
Where AI helps, where it needs care, and where it does not belong.
We would rather tell you this before the project than after it. Not every problem improves with AI, and the ones that do not are usually obvious at the assessment stage.
| Verdict | The task | The data | The risk |
|---|---|---|---|
| Good fit | The task is repetitive, language-shaped, and a person could check the output | The information needed is available and allowed to be used | A wrong answer is inconvenient, not dangerous |
| Needs care | The output influences money, eligibility or someone's record | The data is sensitive, regulated, or scattered across systems | Human review has to stay in the loop by design |
| Poor fit | The task needs a guaranteed-correct answer every time | The information does not exist in any usable form | Conventional software would be cheaper, faster and more reliable |
Approach
Design for usefulness, uncertainty, cost and control.
Clear task definition
What the AI is expected to do, what it must not do, and how success will be judged — agreed before anything is built.
Data boundaries
What information enters the workflow, where it is processed, what is retained, and what should never be submitted at all.
Quality and fallback behaviour
What the product does when a response is uncertain, incomplete, slow or unusable. This is most of the engineering.
Human review where it belongs
People stay in the loop wherever the use case genuinely needs judgement, approval or a higher bar of confidence.
Cost and latency awareness
Flows and usage controls designed with response time and ongoing per-request cost in mind, because both are real.
Measurement after launch
Tracking whether users actually complete the task, and where the AI experience creates friction or fails outright.
The journey
From a use case to an AI feature that survives real users.
The demo is the easy part. This is what happens between an idea and something you can leave running in production — what you hand over at each stage, what we do with it, and what exists afterwards.
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You describe the task, not the technology
- You bring
- The job you want done and who it is for. You do not need to know which model or technique fits.
- We do
- We work out whether AI is genuinely the right tool, and say so plainly when conventional software would be better.
- You get
- An honest assessment of fit before any budget is committed.
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We check the data and the risk
- You bring
- What information exists, where it lives, and what is sensitive or off-limits.
- We do
- We assess data availability, quality, privacy constraints, and what a wrong answer would actually cost you.
- You get
- A clear picture of what is feasible, and what would need to change to make it feasible.
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We prototype the workflow
- You bring
- Realistic examples — including the awkward ones, not just the clean ones.
- We do
- We build a narrow version and run your real inputs through it to see where it holds up and where it breaks.
- You get
- Evidence rather than a demo. Sometimes the evidence says stop, which is a useful outcome.
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We design the product around it
- You bring
- Feedback on how the experience should feel when the answer is good, uncertain, or unavailable.
- We do
- We design the surrounding product: input, waiting states, uncertainty, errors, limits and the path forward.
- You get
- A product experience, not a prompt with a text box in front of it.
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We build the controls behind it
- You bring
- Your requirements on cost ceilings, access and retention.
- We do
- Backend orchestration, access control, usage limits, retries, logging, analytics and cost monitoring.
- You get
- Something you can run in production without being surprised by the invoice.
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We launch carefully and improve
- You bring
- Access to real usage once it is live.
- We do
- We watch completion rates, failures and cost per outcome, then tighten the parts that need it.
- You get
- A feature that keeps working as usage grows, rather than one that quietly degrades.
Technology
What we build AI products with.
Model providers are chosen per use case around quality, cost and latency. Most of the engineering sits in the services around them.
- OpenAI
- Anthropic Claude
- Google Gemini
- Node.js
- NestJS
- Express
- Laravel
- Firebase
- +3 more
- PostgreSQL
- MySQL
- MongoDB
- Firestore
- SQLite
- +1 more
- Firebase Analytics
- Google Analytics 4
- PostHog
- Microsoft Clarity
- Sentry
Estimating
How we arrive at a number.
We do not publish a price list, because a price without a scope is a guess. What we can share is exactly how an estimate is built — so that when you receive one, you can check the reasoning rather than simply trust it.
Every project is estimated from scratch. Nothing below changes depending on who is asking.
Tasks Roles Hours Rates
Contingency Our margin
Your estimate
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We sharpen the idea into a scope
Before anything can be costed, the idea has to become specific: who it is for, what the first version must do, and what is explicitly out of scope. Most of the cost risk in a project is decided here, not during development.
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We break the scope into real tasks
The scope is split into the actual pieces of work — screens, flows, backend services, integrations, edge cases, testing, release. Vague line items are where estimates go wrong, so the breakdown goes down to work someone can genuinely start on Monday.
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We work out the smallest team that can do it well
More people does not mean faster. We identify the minimum set of roles the work genuinely needs — and how much of each role — because every extra person adds coordination cost that the client ends up paying for.
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We estimate hours per role
Each task is estimated in hours against the role that will do it. This is where experience matters most: the difference between a good and a bad estimate is usually the work nobody remembered to count.
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Hours become cost
Estimated hours are multiplied by the rate for each role. At this point there is a number, and — more usefully — a number you can interrogate line by line rather than a single figure you have to take on faith.
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We add contingency and our margin, and we say so
A realistic estimate includes a buffer for the things that always come up, plus our margin as a business. We would rather state both openly than bury them and then return with change requests. If the total does not fit your budget, we reduce scope together instead of quietly reducing quality.
Proof
A connected AI product, not an isolated prompt.
LearnSnap AI combines camera or typed questions, AI explanations, quizzes, flashcards, saved topics, daily practice, credits, backend services, analytics and an Android product experience. Bytecraft shaped those parts as one connected learning flow — including the credit model that keeps AI-dependent actions viable to run.
Read the LearnSnap AI case study- Camera and typed question input
- AI-assisted explanations
- Quizzes and flashcards
- Credit-based usage
- Backend orchestration
- Product analytics
What we will not do
- Promise that AI output will always be correct. No one can, and building as if it will is how AI features fail.
- Train foundation models. We build products and integrations around suitable AI services.
- Add AI to something because it is fashionable, when conventional software would be cheaper and more reliable.
- Send your sensitive data somewhere without agreeing first exactly what leaves your systems.
- Give legal, medical, financial or compliance guarantees about AI output.
Questions
AI development questions we are asked most.
What type of AI projects does Bytecraft take on?
AI-enabled mobile and web products, integrations into existing systems, assistants over approved sources, workflow support, and structured content or data-processing tasks — all tied to a clear user or business problem rather than a general capability.
Can Bytecraft add AI to an existing app or website?
Yes, where the existing architecture, data flow, user experience, provider capability and operating cost make the integration responsible and genuinely useful. The check happens before the work is quoted.
Does Bytecraft build its own foundation models?
No. We focus on product development and integration around suitable AI services and the backend workflows that make them usable. Claiming otherwise would be untrue, and training general-purpose models is not what most businesses actually need.
How do you handle inaccurate AI responses?
It depends on the use case, and it is where most of the engineering goes: constrained instructions, approved sources, validation, confidence and fallback states, human review, user feedback and monitoring. No AI output should be presented to a user as guaranteed correct.
Can AI usage be controlled or monetized?
Yes. Depending on the product, access limits, credits, subscriptions or usage tracking can be designed around the per-request service cost and the value to the customer. LearnSnap uses a credit model for exactly this reason.
What does AI cost to run?
Most AI features cost money per request, which makes usage patterns a product decision rather than only a technical one. We design flows, caching and limits with that in mind, and we tell you the expected cost shape before it is built.
Is our data used to train someone else's model?
That depends on the provider and the plan, and it is something to settle before any integration is built. We map what data leaves your systems, where it is processed and what is retained, so the decision is made deliberately.
How long does an AI feature take to build?
The prototype that tells you whether it works is usually fast. The production version — with access control, failure handling, cost limits, analytics and a real user experience — is the larger part of the work, and it is the part that decides whether the feature survives contact with users.
What if AI turns out to be the wrong answer?
Then we will say so, ideally at the assessment stage before you have spent much. A workflow that needs guaranteed-correct answers, or where the necessary data does not exist, is better served by conventional software.
Have an AI use case that needs product and engineering discipline?
Share the task, the users, the information flow and the outcome you want. We will help assess whether AI is the right tool — and tell you if it is not.
Rather talk it through? Book a discovery call, or browse all Bytecraft services.