8 Best Machine Learning Companies in Bangalore

Why Bangalore is where most Indian ML work happens

If you need a team to build a machine learning system in India, Bangalore has more of them than any other city. It posts 25.4% of India's AI jobs, the highest share in the country, according to a CBRE study of 64,500 Naukri listings. It also holds about 30% of India's 2,740 global capability centres and more than 3,600 funded tech startups, 45 of them unicorns.

All that choice makes picking a partner harder. Some Bangalore firms are analytics giants with thousands of data scientists. Others are small studios that build one model at a time, and a few are product companies whose ML you buy rather than commission.

This list is for companies that want to hire an ML partner. If you are looking for ML jobs in Bangalore, job boards like Naukri will serve you better. We picked 8 firms, scored each out of 10, and noted who each one suits.

How we scored the companies

Each company got a score out of 10, built from six questions we would ask before signing an ML contract:

1. ML depth: does the team build and train its own models, or mostly wire up third-party APIs?

2. Production record: has it shipped models that run in production, with MLOps, monitoring and retraining?

3. Domain fit: does it know the industries it serves, such as healthcare, retail or finance?

4. Proof: are there named clients, case studies, regulatory clearances or published results?

5. Support after launch: will it maintain and retrain the model, or hand over and leave?

6. Client feedback: what do reviews, testimonials and repeat clients say?

We used public sources for every fact about other companies and linked them in each profile. Scores are our editorial judgement. Prognos Labs is our own company, and we have ranked it first because end-to-end custom ML is the work we do every day; judge that claim against the others on this list.

The 8 best machine learning companies in Bangalore at a glance

1. Prognos Labs: best overall ML development partner

Website: prognoslabs.ai · Score: 9.4/10

Prognos Labs is an AI consulting and development lab. We build custom models and LLM systems, agentic AI that automates multi-step workflows, and predictive systems, then manage them after launch. Most of our work so far is in healthcare, where models have to be accurate, auditable and compliant from the first release.

One example is StethoScribe, an AI medical scribe that cut clinicians' documentation time by 65%. Another is the FitzStyles we built an AI stylist in 11 weeks that lifted conversion 3.2 times.

Strengths:

• One team covers strategy, model building, deployment and LLMOps, so nothing gets lost between vendors.

• Agentic AI work with documented cost reductions of up to 50%.

• Compliance-first builds, which matter most in healthcare and finance.

2. Fractal Analytics: best for Fortune 500 enterprise AI

Website: Fractal.ai | Score: 9.1/10

Fractal was founded in Mumbai in 2000 and has a large Bengaluru office. It had about 4,600 employees in 2024 and listed on the NSE and BSE in February 2026, in an IPO of about ₹2,834 crore that Business Today called India's first pure-play enterprise AI listing. It has also spun out products, including Qure.ai for radiology and Eugenie.ai for anomaly detection.

Strengths:

• Scale and process for global enterprises; the Americas bring in 66% of revenue.

• Backed by Apax ($200M in 2019) and TPG ($360M in 2022), per.

• A track record of turning client work into standalone AI products.

3. MathCo: best for owning the IP you pay for

Website: mathco.com | Score: 8.9/10

MathCo, formerly TheMathCompany, started in 2016 and runs its global headquarters from a 200,000 sq ft, 2,000-seat centre at IWF Campus in Bengaluru (GlobeNewswire). It does enterprise analytics, MLOps, GenAI and data engineering on its NucliOS platform, under a hybrid model where clients end up owning what is built.

Strengths:

• Clients keep the IP and build in-house capability alongside MathCo's team.

• A reusable platform shortens delivery on common analytics problems.

• Long experience with Fortune 500 clients.

4. Tredence: best for retail, CPG and BFSI

Website: trendence.com | Score: 8.8/10

Tredence was founded in 2013 and does industry-specific AI and data science for retail, CPG, healthcare and financial services. It had about 3,500 employees in 2025 and raised $175M led by Advent International in December 2022 (Business Today). Business Today describes it as Bengaluru-based; Wikipedia lists San Jose as its headquarters.

Strengths:

• Ready-made accelerators for retail and CPG problems such as demand forecasting.

• Large, well-funded delivery team.

• Covers the path from data engineering to deployed models.

5. Sigmoid: best for data engineering plus ML

Website: sigmoid.com | Score: 8.6/10

Sigmoid was founded in 2013 by IIT alumni. Its headquarters is in San Francisco, but about 80% of its people work in India, mainly from Bengaluru. It builds data platforms and ML models for Fortune 500 companies, mostly in CPG and financial services, and raised a $12M Series B led by Sequoia India in 2022 (TechCrunch).

Strengths:

• Strong on the data plumbing that most ML projects stall on.

• Deep CPG and financial services experience.

• Delivery run from Bengaluru with US-facing teams.

6. Mu Sigma: best for decision sciences at scale

Website: mu-sigma.com | Score: 8.5/10

Mu Sigma was founded in 2004 by Dhiraj Rajaram. It is headquartered in Chicago and runs its global delivery centre in Bangalore. It works on decision sciences and big-data analytics, and counts more than 140 Fortune 500 companies as clients (Wikipedia).

Strengths:

• One of the longest-running analytics firms in India.

• Large client base among the Fortune 500.

• Suited to long-running analytics programmes.

7. SigTuple: best for medical imaging and lab diagnostics

Website: sigtuple.com | Score: 8.3/10

SigTuple was founded in Bengaluru in 2015 and makes AI-powered robotic microscopes. Its AI100 received US FDA 510(k) clearance in September 2023 for AI-assisted digital haematology. It has raised more than $40M, most recently an extended Series C led by SIDBI Venture Capital in August 2024, and Horiba Medical distributes it in South Asia (Entrackr).

Strengths:

• FDA-cleared ML in a regulated medical product.

• A clear fit for labs and hospital pathology departments.

• SigTuple sells a finished product, which suits buyers who want a device they can install.

8. Sarvam AI: best for Indian-language GenAI

Website: sarvam.ai | Score: 8.2/10

Sarvam AI was founded in Bengaluru in August 2023 and builds foundation models for Indian languages, plus an enterprise GenAI platform. It has released Sarvam-1 (October 2024), Sarvam-M (May 2025) and Sarvam-30B and Sarvam-105B (February 2026), and was selected under the IndiaAI Mission in April 2025 (Wikipedia). It raised a $41M Series A led by Lightspeed (Sarvam).

Strengths:

• Models trained for Indian languages, which global LLMs handle less well.

• Backed by Lightspeed, Peak XV and Khosla Ventures.

• A good base for voice and chat products aimed at Indian users.

How to choose an ML partner in Bangalore

Start with the problem, then match the firm to it. A forecasting model for a retail chain, an FDA-grade diagnostic tool and an Indian-language chatbot need very different teams, and the firms above are good at different things.

Before you sign, ask each shortlisted firm:

1. Which models of yours are running in production today, and who maintains them?

2. Who owns the trained model, the code and the training data at the end of the contract?

3. How will you measure success, and what happens if the model misses the target?

4. How do you handle data privacy under India's DPDP Act, and HIPAA or GDPR if we sell abroad?

5. What does retraining cost once the model is live?

A firm that answers these with specific clients and numbers is usually a safer bet than one that answers with a slide deck.

What ML projects cost and how long they take

These are typical ranges from projects we have scoped at Prognos Labs. Large enterprise firms in Bangalore usually charge more.

The biggest driver of cost is usually the state of your data. Clean, labelled data shortens a project, while data scattered across spreadsheets and systems adds weeks of cleanup before any model work starts.

Which Bangalore ML company should you pick?

Pick by the job you need done:

• Custom models, LLM systems or agentic AI, built and run by one team: Prognos Labs (9.4).

• A multi-year AI programme across a Fortune 500 business: Fractal Analytics (9.1) or Mu Sigma (8.5).

• Analytics where you want to keep the IP and build your own team: MathCo (8.9).

• Retail, CPG or banking use cases with ready accelerators: Tredence (8.8) or Sigmoid (8.6).

• An FDA-cleared diagnostic product: SigTuple (8.3).

• Voice or chat products in Indian languages: Sarvam AI (8.2).

If your project is a custom build and you want the same team to own it after launch, talk to Prognos Labs. We will tell you in the first call whether ML is the right tool for the problem.