AI development & machine learning

Custom AI, trained on your data.

Machine-learning models, generative AI and the data engineering and MLOps to run them in production. Built for Fintech, Healthcare and Government, and owned by you.

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Use cases

When custom AI development is the right tool

An API or a ready-made tool is enough for generic tasks. These are the situations where the value sits in your own data, and a model built on it is what moves the number.

Fintech & Banking

Fraud, risk and anomaly scoring

You know it when

Rules catch yesterday’s fraud and generic models have never seen your transaction patterns.

What we build

A scoring model trained on your own transaction history, served in real time with drift monitoring.

Insurance & Lending

Document-heavy operations

You know it when

Claims, KYC and loan files are read, classified and keyed by hand, and the backlog keeps growing.

What we build

Extraction and classification models that read your document formats and route work automatically.

Healthcare

Clinical and diagnostic support

You know it when

Scans, reports and signals pile up faster than specialists can review them.

What we build

Vision and signal models trained on labeled clinical data, with audit trails for regulated use.

Manufacturing & Field

Visual inspection and predictive maintenance

You know it when

Defects slip through manual checks, or equipment fails before anyone sees it coming.

What we build

Computer-vision inspection and sensor-based failure prediction on your production data.

Retail & E-commerce

Forecasting and personalisation

You know it when

Stock-outs and overstock are driven by spreadsheets, and recommendations are generic.

What we build

Demand forecasting and recommendation models trained on your catalogue, seasons and customers.

Enterprise & Government

Knowledge copilots over internal documents

You know it when

Teams spend hours finding answers buried in policies, SOPs and past cases.

What we build

A retrieval-grounded assistant that answers from your documents and cites the source.

Voice & Language

Speech and Indian-language AI

You know it when

Conversations, calls and dictation in multiple languages never make it into structured data.

What we build

Speech-to-structured-notes and language models tuned for your vocabulary and languages.

Any sector

Data that an API cannot see

You know it when

Residency, privacy or compliance rules mean your data cannot leave your environment.

What we build

Custom or fine-tuned models that run where your data lives, with full model and IP ownership.

Not sure which side of the line you are on?Get the free feasibility read
01Data

Make your data AI-ready first.

Most AI projects stall on data, not the model. We build the pipelines, cleaning, labeling and feature engineering that give a model something real to learn from, the step most teams skip.

  • Connect and unify sources across systems and silos
  • Clean, de-duplicate and label with reviewed quality
  • Feature engineering with versioned, governed datasets
  • Residency, access control and privacy built in
PipelinesLabelingFeature storeGovernance
Data engineering for AI
Data pipeline · policy-documents
Training-ready
1Sources connected
6 systems · 1.2M records
2Cleaned and de-duplicated
−3.4% invalid rows
3Labeled and reviewed
42,800 examples
4Features engineered
118 features · versioned
02Model

Machine-learning models and generative AI, trained on your data.

Computer vision, NLP, forecasting and recommendation models for the problems off-the-shelf models cannot solve. LLM applications and copilots grounded on your documents, so every answer is accurate and traceable to a source.

  • Custom training and fine-tuning to your domain and accuracy targets
  • GPT, Claude, Gemini or open models, chosen for the job
  • Retrieval-augmented generation with cited sources
  • Evaluation, bias and safety testing before it ships
Computer visionNLPFine-tuningRAGVector search
Model training and fine-tuning
Training run · claims-classifier-v3
Fine-tune · 42,800 labeled examples
Running
Accuracy
94.1%
Loss
0.18
Epoch
12 / 15
What is the claim settlement window for policies issued after March?
Policies issued on or after 1 March fall under the revised schedule: claims are settled within 15 working days of document verification, and escalations are reviewed within 5 days.
Claims-Policy-2026.pdf · p.4Escalation-SOP · §2.3
Grounded on 2,140 internal documents · sources cited
03Inference

Reliable in production, not just in a demo.

Model serving, versioning, monitoring and retraining: the operations layer that keeps your AI accurate, measurable and compliant long after launch.

  • Serving with latency and uptime targets
  • Drift and quality monitoring with alerts
  • Retraining pipelines that keep the model current
  • Audit trails for regulated environments
MLOpsModel servingMonitoringRetraining
Want ready-to-deploy AI agents on top? See the workforce
Production monitor · fraud-scoring
Healthy
Uptime · 30d
99.97%
Latency · p95
84 ms
Data drift
Low
Requests · last 24h2.1M
Start here

Get a free feasibility read.

Before anyone spends on a build, tell us the problem and what data you have. We come back with an honest written read: whether AI is the right tool, which approach fits, and what it takes to ship.

Request the feasibility readNo commitment. Senior engineers, not sales.
You send
  • The business problem, in a paragraph
  • What data you have and where it lives
  • Any compliance or residency constraints
You get
  • A data-readiness verdict and the gaps to close
  • Custom model, fine-tune or API: which and why
  • A phased plan with a ballpark estimate

AI development services: from the data to the deployment

One team for the model, the data, the application and the infrastructure, so nothing falls between vendors.

Custom ML models

Computer vision, NLP, forecasting, classification and recommendation models trained on your data for the problems off-the-shelf models cannot solve.

Generative AI & LLM apps

Production LLM applications and copilots on GPT, Claude, Gemini and open models, grounded on your knowledge.

Model training & fine-tuning

Custom model training and fine-tuning of open or proprietary models to your domain, data and accuracy and cost targets.

RAG & knowledge infrastructure

Retrieval-augmented generation and vector search so models answer accurately from your real, current data.

Data engineering for AI

The pipelines, cleaning, labeling and feature engineering that make your data actually usable for training, the step most AI projects skip.

MLOps & deployment

Training pipelines, model serving, versioning, monitoring and evaluation so your AI stays reliable and improves in production.

Evaluation & guardrails

Accuracy, safety, bias and reliability testing, so you can trust the model before and after it ships.

AI product development

Building an AI product from zero to one, the model, the data, the application and the infrastructure, as one team.

From use case to production

  1. 01
    Discovery & feasibility
    We pressure-test the use case, the ROI, and whether AI is the right tool, before you spend on a build.
  2. 02
    Data engineering & readiness
    We assess and engineer your data, pipelines, cleaning, labeling and features, so the model has something to learn from.
  3. 03
    Build, train & fine-tune
    We build and train the model, or fine-tune an existing one, in reviewable iterations with real evaluation.
  4. 04
    Evaluate & harden
    Accuracy, edge cases, bias and safety, so it is trustworthy, not just impressive in a demo.
  5. 05
    Deploy & MLOps
    Production deployment with serving, monitoring and retraining so it stays reliable and keeps improving.

Is your data AI-ready?

If you can tick these, a build can start now. If not, that is where we begin, and the feasibility read tells you exactly what to fix.

  • Enough quality data to train on, or a plan to get there
  • Clean, consistent and correctly labeled for the task
  • Accessible, in pipelines, not locked in silos and spreadsheets
  • Governed, with the right permissions, residency and privacy

AI development: common questions

Who is Levitation, and what does your AI development company do?

Levitation Infotech is an AI development company based in Noida, India, working with clients in India and overseas. We provide AI development services and machine-learning development: custom ML models (computer vision, NLP, forecasting, recommendation), generative AI and LLM applications, model training and fine-tuning, retrieval-augmented generation (RAG), the data engineering that makes data AI-ready, and the MLOps to run models in production. Every system is trained on the client’s own data, built by senior engineers, compliance-aware for RBI, SEBI, HIPAA, GDPR and the DPDP Act, and handed over with full model and IP ownership.

What does an AI development company do?

An AI development company designs, trains and ships custom AI and machine-learning systems for your business, instead of selling an off-the-shelf tool. That spans data engineering, custom model training and fine-tuning, generative AI and LLM applications, and the MLOps to run them in production, all on your data with full IP handover.

How much does custom AI development cost in India?

A focused proof-of-concept can start at a few lakh; a production, trained-and-integrated AI system runs to tens of lakh, driven mostly by data readiness, whether you need custom training or fine-tuning, and integration and compliance needs. We give a fixed estimate after a short discovery call.

Do I need a custom model, or is an API enough?

An API is faster and cheaper for generic tasks. A custom or fine-tuned model is justified when you need accuracy on your specific data, a capability no general model has, lower cost at scale, or data-residency and compliance an API cannot give. We will tell you honestly which you need.

How much data do we need to train a custom model?

It depends on the task: fine-tuning an LLM can work with hundreds to thousands of good examples, while training a vision model from scratch needs far more. Often the answer is not more data but better data, which is why we start with data engineering. If you do not have enough, we help you get there.

What if our data is not AI-ready?

That is the normal starting point, and the real reason most AI projects stall. We assess your data, build the pipelines, and handle cleaning, labeling and feature engineering so there is something a model can actually learn from.

How long does an AI or ML project take?

A focused model or LLM application typically ships in a few months; broader systems with custom training and deep integration take longer and are delivered in reviewable stages so you see value early.

Who owns the model and the IP?

You do. We hand over full model, code and intellectual-property ownership, trained on your data and running in your environment, so you can run, retrain and extend it without lock-in.

What AI development services does Levitation offer?

Levitation offers end-to-end AI development services: custom machine-learning model development (computer vision, NLP, forecasting, recommendation), generative AI and LLM application development, model training and fine-tuning, retrieval-augmented generation (RAG) and knowledge infrastructure, data engineering for AI, MLOps and deployment, evaluation and guardrails, and full AI product development. We also build ready-to-deploy AI agents for support, sales, operations, knowledge, data and voice.

Where is Levitation based, and do you work with clients outside India?

Levitation Infotech is based in Noida (Delhi NCR), India, and has delivered 300+ projects over 8+ years for clients in India and overseas, including fintech, banking, healthcare and government organisations. Engagements run remotely with overlap to your working hours, and models can be deployed in your own cloud or on-premise to meet data-residency requirements.

Have an AI or ML build in mind.

Tell us the problem and your data. We will give you an honest read on feasibility, the ROI, and what it takes to ship it.