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[00] AI & ML Development

AI and ML Development Services for Businesses That Run on Data

We build AI and machine learning systems that solve one defined business problem at a time: forecasting demand, reading documents, answering customer questions, spotting fraud or inspecting products. Each project starts from your data and your process, and ends with a model running inside the software your team already uses, with its accuracy measured.

Illustration: five everyday inputs (a photo of a supplier invoice, a WhatsApp reorder message, a voice note about a damaged order, a photo of a damaged delivery and an email with a purchase order PDF) are read one by one. Images get boxes around the vendor, totals or damage, and text gets its names, dates, quantities and intent highlighted. The fields fill a structured record that is sent to the ERP, CRM or helpdesk, and items the AI is unsure about go to a person for review.

key facts

typical proof of concept
3–6 weeks
typical full custom build
2–6 months
or your own servers
AWS, Azure, GCP
signed before we see your data
NDA

What we deliver

What you get with AI & ML Development. Nine parts of the service, each scoped with you.

Our work covers the whole range of applied AI: classic machine learning on tables of business data, deep learning for images, speech and video, natural language processing, and generative AI built on large language models such as GPT, Claude, Gemini and Llama. We choose the simplest approach that meets the target, not the most fashionable one.

Around the models we do the engineering that keeps them useful: data pipelines, integration with ERP, CRM and mobile apps, MLOps for versioning and monitoring, and retraining as your data changes. You can engage us for a fixed-scope project or a dedicated team of ML engineers and data scientists.

  1. t.01

    AI consulting and feasibility

    We review your processes and data, rank use cases by value and effort, and tell you plainly which ones are not ready for AI yet.

  2. t.02

    Proof of concept

    A small working model on a sample of your real data, with an agreed accuracy target, so you can decide before committing a full budget.

  3. t.03

    Predictive models

    Classification, regression and forecasting for demand, churn, credit risk, fraud and predictive maintenance, trained on your own history.

  4. t.04

    Natural language processing

    Text classification, entity extraction, summarisation, sentiment analysis, translation and search across contracts, emails, tickets and reports.

  5. t.05

    Computer vision

    Image classification, object detection, OCR and visual quality inspection, tested on photos and video from your own cameras and production lines.

  6. t.06

    Generative AI and LLM apps

    Assistants, document automation and knowledge search built on GPT, Claude, Gemini or Llama, grounded in your documents through retrieval-augmented generation (RAG).

  7. t.07

    AI agents and chatbots

    Multilingual chatbots and agents that answer questions, update records and route work across support, sales and HR, handing over to a person when needed.

  8. t.08

    Data engineering

    Pipelines, ETL and ELT jobs, warehouses and data lakes that collect and clean the data your models depend on, and keep it flowing after launch.

  9. t.09

    MLOps and integration

    Docker and Kubernetes deployment, MLflow or Kubeflow experiment tracking, monitoring and retraining, plus APIs into your ERP, CRM and apps.

Discuss your project

Why it matters

The difference it makes to your business. Results your team will notice.

AI & ML Development is planned around these outcomes, measured against a baseline we agree with you up front.

Discuss your project
  1. 01

    Less manual work

    Documents, tickets and approvals that staff now read and key in by hand can be classified, extracted and routed for a person to check.

  2. 02

    Decisions based on forecasts

    Models trained on your own history give demand, risk and churn estimates that planners can act on, with the uncertainty shown alongside.

  3. 03

    One partner from data to deployment

    The same team handles data preparation, modelling, integration and support, so nothing is lost between a consultant's report and a working system.

  4. 04

    Spend matched to evidence

    A measured proof of concept comes first, so a full build is funded only once the pilot has shown it works on your data.

How we work

How we deliver AI & ML Development

A clear five-step process, so you always know what happens next and what you will get.

Every project starts with a free, no-obligation consultation.

run.logappsinai / project

$ appsinai run

  1. Step 1: Define the problem and the data

    We agree one business problem, how success will be measured and which data exists to learn from. That data is then collected, cleaned and prepared.

  2. Step 2: Choose and train the model

    We compare candidate approaches, from simple baselines to deep learning or an LLM, then train and tune the strongest one on your data. Work runs in sprints with regular demos.

  3. Step 3: Test on unseen data

    The model is evaluated on a held-back dataset it never saw in training, and the results are reviewed with your team against the agreed target.

  4. Step 4: Integrate and deploy

    We deploy to your cloud or servers and connect the model to the systems where people work, with access controls and logging in place.

  5. Step 5: Monitor and retrain

    Accuracy, drift and usage are tracked after launch, and models are retrained as your data and business change.

↺ results feed the next improvement cycle$

Security & control

Your data stays under your control

AI projects need access to business data. Before work starts we agree in writing which data is used, where it lives and who can reach it.

Ask us about security
  • 01

    Data residency

    Models and data can run in your own cloud account, in the region you choose, or on your servers, so data does not need to leave your environment.

  • 02

    Access control

    Role-based access, encryption in storage and in transit, and secure APIs. Our engineers get only the access the project needs, removed at handover.

  • 03

    Third-party model settings

    Where we use hosted LLM APIs, we configure the options that keep your prompts and files out of the provider's training data, or use an open-source model you host.

  • 04

    NDA and IP

    We sign an NDA before you share any data. Once the project is paid for, the code and models we build are yours, as set out in the contract.

FAQ

Questions about AI & ML Development

Straight answers to what buyers ask us most. Can't find what you need?

Email sales@appsin.ai or call +91-9025555584.

Ask us directly
How much does an AI or ML project cost?

It depends on the problem, how much data preparation is needed, whether an existing model can be adapted or a new one must be trained, the number of integrations and where it is hosted. A proof of concept costs a fraction of a full build. After a requirements review you get a fixed quote for the agreed scope.

Estimate your AI project cost
How long does it take to build an AI solution?

A proof of concept typically takes 3 to 6 weeks. A full custom solution typically takes 2 to 6 months, and large enterprise platforms take longer. Most of the variation comes from data preparation and the number of systems the model has to connect to.

What data do we need to get started?

It depends on the use case. It can be database records, documents, images, video, sensor readings or public datasets. You do not need perfectly clean data on day one: preparing and cleaning it is part of our work, and the feasibility review tells you whether there is enough to learn from.

Should we build a custom model or use an off-the-shelf AI tool?

If a ready-made tool already handles your task well, use it. Custom work makes sense when your data, process or accuracy needs are specific to your business, or when the AI has to sit inside your own systems. We often combine both, for example a hosted LLM with your own retrieval and rules.

Custom AI vs off-the-shelf
Can you integrate AI with our existing systems?

Yes. We connect models to ERP, CRM, accounting, e-commerce, mobile and custom systems through their APIs, webhooks or data exports, and return results into the screens your team already uses.

Who owns the code and the trained models?

You do. On completion and final payment, the code, prompts, trained or fine-tuned models and documentation we build for you are handed over, as set out in the contract. Third-party and open-source models stay under their own licences.

What support do you provide after launch?

Monitoring, performance tuning, model retraining, security updates, bug fixes, infrastructure management and new features, under a support plan agreed at handover.

Free PDF brochure

AI & ML Development brochure

Features, outcomes, delivery process and FAQs in one PDF you can share with your team or management.

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Start with one problem worth solving

Book a free 30-minute call. Tell us the process or decision you want to improve and we will tell you honestly whether AI fits, what data it needs and what a proof of concept would involve.

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