AI & ML Development Services

We help businesses use their data and modern AI models to make better decisions and automate work, starting with a proof of concept and moving to production only when it works.

Artificial Intelligence (AI) and Machine Learning (ML) can predict demand, classify documents, answer questions from your own knowledge base and make automation smarter. But many AI projects stall because they start with the technology instead of a clear business problem and the right data.

INNOVISCENT is an AI and ML development company in India that works the other way round. We begin with the decision or task you want to improve, check whether your data can support it, and build a small proof of concept to prove the value before any larger investment.

We also bring AI into the areas we know best. In test automation, AI helps keep test suites stable when the user interface changes. In process automation, language models read unstructured documents and emails that traditional RPA bots cannot handle.

We are practical about where AI helps and where a simpler rule-based solution is better. If a report, a set of rules or a well-designed bot will solve the problem more cheaply and more reliably than a model, we will say so. When AI is the right answer, we build it with monitoring and human review, so you can trust its output.

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AI and ML

At a glance

  • Process: Explore → Prototype → Productionise → Improve
  • Tools: Python, scikit-learn, TensorFlow, PyTorch, LLM APIs
  • Industries: Healthcare, Retail and e-commerce, Manufacturing

Problems We Solve

  • Data that isn't used

    You collect data but decisions are still made on gut feel. We turn it into forecasts, scores and dashboards people can act on.

  • Manual review work

    Staff read and sort documents, emails or tickets by hand. Models can classify, extract and route them, with people checking the uncertain cases.

  • Brittle automation

    Test scripts and bots break every time a screen changes. AI-assisted locators and document understanding make automation more resilient.

Our Process

  1. 1

    Explore

    We define the business goal, the success measure and the data available, and tell you honestly if AI is the right tool.

  2. 2

    Prototype

    We build a proof of concept on a sample of your data to test accuracy and value before committing to a full build.

  3. 3

    Productionise

    We deploy the model into your product or workflow, with an API, a user interface and monitoring.

  4. 4

    Improve

    We track model performance, retrain on new data and tune the solution as your business changes.

Tools & Technologies

We build most models in Python with scikit-learn, TensorFlow or PyTorch, and use hosted large language model APIs where they are the fastest route to value. Solutions are deployed on AWS or your own infrastructure, with monitoring so you can see how the model performs over time.

  • Python
  • scikit-learn
  • TensorFlow
  • PyTorch
  • LLM APIs
  • Pandas
  • AWS

Industries We Serve

  • Healthcare

    Document classification and data extraction from claims and records.

  • Retail and e-commerce

    Demand forecasting, recommendations and customer segmentation.

  • Manufacturing

    Predictive maintenance using sensor data from connected devices.

  • Software and QA

    AI-assisted test automation that adapts to UI changes.

How an engagement works

Example engagement: AI-assisted document triage

An outline of how we typically run this kind of project. Client details are withheld.

The challenge

A typical operations team receives hundreds of emails and attachments a day and sorts them by hand before anyone can act on them.

Our approach

We take a labelled sample of past messages, build a proof of concept that classifies each message and extracts the key fields, and measure its accuracy against the team's own decisions. Low-confidence items go to a person.

The result

If the proof of concept meets the agreed accuracy, it is connected to the mailbox and the team's workflow, so staff review sorted, pre-filled items instead of starting from scratch.

Frequently Asked Questions

Do we need a lot of data to start an AI project?

Not always. Some problems can be solved with pre-trained models and a small amount of your own data, while others need years of history. We start with a short assessment and a proof of concept on the data you have, and tell you honestly whether it is enough before you invest in a full build.

Can AI help with test automation?

Yes. We use AI to make automated tests more stable, for example by finding elements even when their IDs or layout change, and to help generate test data and test cases. This reduces the time your team spends fixing broken tests after every UI change, which is one of the biggest costs of UI automation.

Where does our data go when you use AI models?

We agree data handling rules before we start. Sensitive data can stay in your own cloud account or on your servers, and we can use models that run privately instead of public services. We sign an NDA, use anonymised samples during prototyping where possible, and document every data flow.

How do you measure whether an AI solution is working?

We agree a success measure at the start, such as accuracy against human decisions, time saved per item or forecast error, and test the proof of concept against it. After go-live we monitor the same measure, so you can see if performance changes over time and when the model needs retraining.

How long does an AI proof of concept take?

A focused proof of concept usually takes a few weeks, depending on how quickly we can access a representative data sample. At the end you get a working prototype, an accuracy report against the success measure we agreed and a clear recommendation: go ahead to production, change the approach, or stop before investing more.

Test your AI idea with a proof of concept

Describe the decision or task you want to improve. We will tell you whether AI fits and what a proof of concept would involve.

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