AI & Machine Learning Company in Bangalore

Three things move the number on a Bangalore AI build: how clean the historical data is, how many languages the model has to serve, and how much DPDPA paperwork rides along. Against those, engagements land as a scoped AI proof of concept at Bangalore rates over six to ten weeks, covering data audit, baseline model, evaluation harness, and a hosted demo, a custom production ML model (fraud scoring, churn prediction, document extraction, demand forecasting, conversational AI for one channel) including MLOps, drift detection, monitoring, and a DPDPA and MeitY-advisory-aligned model card, or a full production AI system with RAG over enterprise corpora, multiple specialised models, Indic-language coverage, fine-tuning on client data, and enterprise integrations into Salesforce, SAP, Oracle, or a custom data lake. Indic foundation-model fine-tuning at scale on Llama 3.1 70B or Mixtral 8x22B can extend further with GPU compute as a separate pass-through cost. Bangalore captive build economics from Walmart-Labs-class internal centres run two to three times an equivalent Codazz engagement when fully loaded. We give fixed-fee proposals and price the SOW in INR and USD.

Yes. We build production Indic-language applications across all 22 official Indian languages using a layered approach. For most enterprise clients we do not train a new foundation model from scratch. We start with the open AI4Bharat checkpoints (IndicTrans2 for translation, IndicBERT for classification and search, IndicBART for summarisation, Indic Voice for STT and TTS), layer Llama 3.1, Mistral, or Mixtral fine-tuned on the client's Indic corpus where the application needs generation, and route to Sarvam AI or Krutrim API endpoints when the client has a commercial relationship and the sovereignty story matters. Sarvam and Krutrim are foundation-model builders. Codazz is the applied engineering partner that takes their models, or open AI4Bharat weights, into a production application with evaluation, retrieval, safety, and the DPDPA and MeitY-advisory-aligned governance pack. We do not compete with foundation labs. We make their models usable inside Bangalore product roadmaps.

MeitY's advisory of March 1, 2024 initially asked intermediaries deploying under-tested or unreliable AI models to obtain explicit government permission and to label such outputs, and the March 15, 2024 revision narrowed the permission requirement and clarified the labelling, consent, and grievance-redressal expectations. The advisories are not statute, but Bangalore enterprises (especially GCCs of US and EU parents) treat them as the operative baseline for production AI in India. Our discovery phase runs a MeitY-advisory classification on the system: whether outputs need user-facing labelling for synthetic or AI-generated content, whether watermarking is appropriate for image and video, whether the intermediary safe-harbour position is changed by the AI feature, and whether the grievance-redressal officer required under the IT Rules 2021 is appropriately resourced to handle AI-output complaints. We build the labelling, watermarking, and grievance surface into the product rather than retrofitting under audit pressure.

Yes. A large share of our Bangalore engagements are with GCCs of US and EU parents who have an internal AI roadmap set by the US or EU HQ but need extension capacity in Bangalore for applied delivery, Indic-language work, or India-market product builds that the global team cannot prioritise. We work to the parent's internal Responsible AI standard (Microsoft RAI Standard, Google AI Principles, Amazon Responsible AI, the financial-services parents' Model Risk Management policies) and we map the equivalence to DPDPA, MeitY advisories, and Indian sector regulators so the US parent's Responsible AI office and the Indian DPO get a single artefact each. We have shipped patterns for retail (recommendations, search, demand forecasting), BFSI (fraud, AML, KYC, document intelligence), and conversational AI for the Indian market, working alongside the GCC's internal applied-science and platform teams rather than displacing them.

DPDPA 2023 covers personal data processed in connection with offering goods or services to Data Principals in India. The Draft Rules of January 2025 add operational expectations around notice and consent, Data Principal rights, breach response, Significant Data Fiduciary obligations including the DPO and independent audit, and children's data with verifiable parental consent. For AI projects, our discovery runs a Data Fiduciary or Significant Data Fiduciary classification, a data-flow map covering training data, fine-tuning data, retrieval corpus, and inference logs, a consent architecture that captures purpose-specific consent and supports withdrawal, a Data Principal rights surface for access, correction, erasure, and grievance, a children's-data guard that prevents under-eighteen data from entering training without verifiable parental consent, and a retention and deletion policy that includes deletion from vector stores, model checkpoints where feasible, and downstream analytics. Significant Data Fiduciary tier additionally gets a DPO surface, a DPIA cadence, and an independent data auditor engagement plan.

The IndiaAI Mission, approved by the Union Cabinet in March 2024 with a USD 1.25B (approximately INR 10,372 crore) outlay over five years, runs through MeitY and the IndiaAI Independent Business Division. Its pillars include the AI Compute infrastructure (over 18,000 GPUs under the Common Compute Facility being procured through an empanelled pool of cloud and compute providers), the IndiaAI Datasets Platform, the IndiaAI Application Development Initiative, the IndiaAI FutureSkills programme, the IndiaAI Startup Financing programme, and the Safe and Trusted AI pillar. Eligible Bangalore startups and research groups can apply to the GPU compute subsidy and to the application-development grants. We help clients position the AI roadmap to fit IndiaAI eligibility windows where it makes sense, but we do not promise grant outcomes. Most Bangalore clients we work with are commercially funded and use IndiaAI as an opportunistic compute and dataset partner rather than the primary funding vehicle.

Codazz defaults to AWS ap-south-1 (Mumbai, the primary residency choice for most Bangalore clients and the densest GPU and Bedrock region in India) and ap-south-2 (Hyderabad, increasingly used for DR and for additional GPU capacity), Azure Central India (Pune, the primary Azure OpenAI Service region in India) with Azure South India (Chennai) as DR, and GCP asia-south1 (Mumbai) with asia-south2 (Delhi NCR, opened in 2021, now offering Vertex AI). For sovereignty-sensitive workloads we self-host Llama 3.1, Mistral, or Mixtral on H100 or A100 instances inside the client's VPC in an Indian region, or on IndiaAI Common Compute when the client is empanelled. Cross-border inference to US or EU endpoints is acceptable for non-personal-data workloads or with a documented DPDPA cross-border position. Every project ships with a residency map at the storage, vector store, training, and inference layers, and a region failover playbook that does not silently spill regulated data out of an Indian region during a DR event.

A typical Bangalore production AI engagement (RAG enterprise assistant, fraud or credit model, conversational AI for one channel, or a demand-forecasting pipeline) takes fourteen to twenty-four weeks from kickoff to first production traffic, assuming clean historical data and the DPDPA, MeitY-advisory, and sectoral compliance documentation are scoped in. Week 1 to 4 is discovery, DPDPA classification, MeitY-advisory mapping, data audit, and a baseline. Week 5 to 14 is iterative two-week sprints with an evaluation harness running on every sprint review and a shadow-mode deployment by week 12. Week 15 to 20 is hardening, drift detection, post-market monitoring, the CERT-In drill, and the first external pen test where relevant. Week 21 onward is gradual rollout from one percent of traffic to one hundred percent under a documented kill-switch. Indic-language work or fine-tuning of a 30B+ open-weight model typically adds four to eight weeks for data curation, training runs, and evaluation across languages.