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AI Development

AI Product Development That Fits the Software You Already Run

We add AI where it saves real hours: answering customer questions, reading documents, and moving work between your tools. It is built into your existing web and mobile apps, with the costs worked out before you commit.

Book an AI Use-Case Call See What We Build
  • 10+Years building software
  • 20+Engineers in the team
  • 150+Projects delivered
Where AI Earns Its Keep

One slow task, done faster

Most businesses don’t need “an AI”. They need one slow task done faster.

  • Hours back

    Repetitive admin and data entry handled automatically, so your team spends its day on work that needs a person.

  • Faster answers

    Customers and staff get answers from your own documents in seconds, not after an email thread.

  • Better calls

    Forecasts built from data you already collect, so planning starts from numbers rather than guesses.

If a simple rule or a spreadsheet does the job better, we’ll say so.

Building a new product with AI at its centre? See AI MVP & SaaS Development

What We Build

AI features that do real work

Eight kinds of AI we add to web and mobile products, each built into the app your team already uses.

  • AI inside your app

    LLM features such as summaries, drafting and classification, added to the Laravel, React or Flutter app you already run.

    Laravel development

    Good forSaaS products and internal tools

  • Knowledge assistants

    Chat over your manuals, policies and product data that answers with its sources, so every reply can be checked (RAG).

    Good forSupport, sales and HR teams

  • Workflow agents

    Multi-step tasks across email, CRM, sheets and ERP, with an approval step before anything important happens.

    Good forOperations and back-office work

  • Document intelligence

    Fields pulled out of invoices, IDs, contracts and forms, and saved straight into your database.

    REST API development

    Good forFinance, onboarding and compliance

  • Smart search and recommendations

    Search by meaning instead of exact words, “similar items”, and product discovery that learns from use.

    Good forCatalogues, marketplaces and portals

  • Support assistants

    Website and WhatsApp assistants that hand over to a person with the whole conversation attached.

    Good forCustomer service desks

  • Predictions

    Demand, churn and pricing forecasts built from your own history.

    Good forRetail, hospitality and subscriptions

  • AI proof of concept

    A short test on your real data before any big build, so you know it works before you commit to more.

    Book a use-case call

    Good forA first AI project

Ideas by Industry

Ideas we’d explore with you

Starting points drawn from the industries in our portfolio. Pick yours to see three places AI could help, and a product we built in that field.

Hospitality

  1. Booking assistant

    Answers availability and pricing questions, then takes the booking in the chat.

  2. Review summaries

    Hundreds of guest reviews turned into a short weekly list of what to fix.

  3. Pricing hints

    Suggested room rates from occupancy, season and local events.

From our portfolioDigital Booking AppSee the case study
How It Ships

From one idea to a feature in use

Six steps, and you can stop after any of them. Each one ends with something you can see and judge.

  1. 1–2 days (typical, to be confirmed)

    Use-case workshop

    Choose one task and agree how success will be measured.

  2. About 1 week (typical, to be confirmed)

    Data and cost check

    What data exists, privacy limits, the model to use and the running cost per 1,000 requests.

  3. 2–3 weeks (typical, to be confirmed)

    Proof of concept

    Working on your real data and scored against a test set.

  4. 4–10 weeks (typical, to be confirmed)

    Build and integrate

    Inside your app, with login, logs and a fallback when the model fails.

  5. During the build

    Guardrails

    Accuracy checks, cost caps, rate limits and human review where it matters.

  6. Ongoing

    Launch and improve

    Usage reports, prompt updates and model upgrades as providers change.

Dashed durations are typical ranges, confirmed for your project after the workshop.

Start with step 1. A use-case call costs you half an hour and tells you whether AI is worth it.

Book a use-case call
Responsible AI

Safe by default, not as an extra

Six commitments that come with every AI feature we build, whatever its size.

  • Your data stays yours

    Business API terms or self-hosted models. Nothing you send is used to train public models.

    HowZero-retention settings wherever the provider offers them.

  • Personal data masked

    Names, numbers and IDs are hidden before a request ever reaches a model.

    HowMasking runs on your server, before the API call.

  • People approve what matters

    Payments, customer emails and record changes wait for a person to say yes.

    HowAn approval step built into the workflow itself.

  • Cost caps and alerts

    Spending limits and alerts, so a monthly bill never surprises you.

    HowPer-feature budgets, with a warning before the limit.

  • No lock-in

    The model can be switched: OpenAI, Claude, Gemini or open models.

    HowOne thin layer between your app and the provider.

  • Tested before launch

    Checked against real questions from your business, not demo prompts.

    HowA scored test set that runs before every release.

Tools and Models

The right model for the job, not one favourite

We choose per task, test on your data, and keep the model swappable.

  • Models

    • OpenAI
    • Anthropic Claude
    • Google Gemini
    • Llama
    • Mistral

  • Frameworks

    • LangChain
    • LlamaIndex
    • Vercel AI SDK

  • Vector stores

    • pgvector
    • Qdrant
    • Pinecone

  • Backend

    • Laravel
    • Python · FastAPI
    • Node.js

  • Apps

    • React
    • Vue
    • Flutter
    • React Native

  • Cloud

    • AWS
    • Azure OpenAI
    • Google Cloud
Ways to Work With Us

Start small, grow when it pays

Each step builds on the one before, so nothing you pay for is thrown away.

  • AI Pilot

    2–4 weeks, fixed scope (typical, to be confirmed)

    A working proof on your own data, so the decision to build rests on evidence.

    • One use case, agreed up front
    • Tested against your real questions
    • A go / no-go report with running costs
    Start with a pilot

  • Product Build

    Milestone-based

    From pilot to launch, built into your web or mobile app by one team.

    • Payments tied to delivered milestones
    • Login, logs, fallbacks and guardrails
    • Launch support and a full handover
    Plan a build

  • Dedicated AI Team

    Monthly

    Engineers who join your roadmap and keep improving what has shipped.

    • Developers in your tools and stand-ups
    • Regular prompt, model and cost reviews
    • Scale the team up or down each month
    Talk about a team
Why AmCodr

Engineers first, AI second

AI is one part of a product. We build the rest of it too.

  • We build the whole product, not just prompts

    Apps, APIs and mobile around the model, so the AI feature has somewhere real to live.

  • Shipping software since 2016

    AI joins engineering habits proven over 10 years: code reviews, tests and staged releases.

  • A direct line to the engineers

    IST hours with a morning overlap for Europe and evening calls for the US.

  • You own everything

    Code, API keys and cloud accounts are in your name from day one.

Related Services

Already have an app that needs work?

Good to Know

Questions before an AI project

Short answers to what buyers ask us most. Anything else, ask in your request.

+91 97123 07570Mon–Fri, 9:30 AM – 7:00 PM IST
Do we need a lot of data to start?

No. Many assistants start from documents you already have: manuals, policies and product sheets. Predictions need more history, and the data check tells you whether you have enough before you spend on a build.

Which model will you use: OpenAI, Claude or Gemini?

The one that fits the task, your budget and your privacy rules. We compare a few on your own test questions during the proof of concept, and build so the model can be switched later.

Can you add AI to our existing Laravel, PHP or mobile app?

Yes, that is most of our AI work. The feature is added to the app you already run, with its own logs and a fallback, rather than as a separate tool your team has to learn.

Is our business data safe with AI APIs?

We use business API terms that keep your data out of model training, mask personal data before it is sent, and keep keys on your server. Where your rules require it, we use models hosted in your own cloud.

What does an AI feature cost to run each month?

It depends on how many requests it handles and which model it uses. The data and cost check gives you an estimate per 1,000 requests, and cost caps keep the bill inside the limit you set.

How long does a proof of concept take?

It depends on the task and the state of your data, and the timeline is agreed before we start. You get a working proof on your real data, scored against a test set, so the decision to build is based on evidence.

What happens when the AI gets an answer wrong?

We plan for it. Answers show their sources, low-confidence replies go to a person, and important actions wait for approval. Wrong answers found in use join the test set, so the same mistake is caught before the next release.

Do you support the feature after launch?

Yes. We watch usage and cost, update prompts and models as providers change, and send regular reports. Many teams move to a monthly Dedicated AI Team for this.

Book an AI Use-Case Call

Tell us the task you’d like AI to take over

One slow, repetitive task is enough to start. We’ll tell you honestly whether AI is the right tool for it.

  1. A reply within one working dayFrom an engineer, not a sales script.
  2. A 30-minute use-case callWe pick the one task worth trying first.
  3. A written next stepScope, data needed and a cost range.

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