Production-grade ML shipped with evals, monitoring, and cost caps.
Concrete capabilities, shipped.
LLM apps & RAG
Chatbots, copilots, retrieval systems with eval harnesses.
Forecasting
Time-series models for demand, financial metrics, inventory.
Computer vision
Detection, classification, and OCR for documents and images.
NLP pipelines
Classification, extraction, sentiment, entity recognition.
Recommenders
Content, product, and hybrid recommendation systems.
MLOps
Model monitoring, drift detection, automated retraining.
4 stages
6 capabilities
Yours, exportable any time
Across India and overseas
A 4-phase playbook for ai, ml & deep learning.
Define the problem, data, and success metrics.
Scope
Define the problem, data, and success metrics.
Prototype
Small working model with honest eval numbers.
Productionise
APIs, caches, and safety rails; integrate with your stack.
Monitor
Cost, latency, and quality dashboards, with alerts.
What people usually add next.
Answers before you ask.
All of the above. We pick based on quality per ₹ of inference and data-sensitivity constraints.
Yes. We've shipped LLMs on customer VPCs and on-prem GPU clusters for regulated clients.
Every project gets an eval harness: golden datasets, LLM-as-judge, and human review for critical outputs.
Less than most people expect for anything built on a general model, and more than most expect for a model trained from scratch. Retrieval over your own documents needs the documents and nothing else. A classifier that learns your categories usually needs a few hundred labelled examples per category. We tell you which of those you are in during the first week, before you commit to the build.
One question your team answers by reading. Search across your own documents, extraction from invoices or forms, a first-line assistant that hands over to a person when it is out of its depth. Narrow scope, a measurable answer, and something in front of real users in weeks. The interesting work starts once we can see where it is wrong.
By deciding that before anything is built. Where the data cannot leave your estate we run open-weight models on your own servers or private cloud. Where a hosted model is acceptable we use the no-training endpoints and strip identifiers before anything is sent. Which of those applies is written down at the start.
It will, so the design assumes it. Outputs that carry consequences go to a person for approval, the model shows what it based an answer on, and confidence low enough to be unsafe becomes a handover rather than a guess. Every wrong answer someone reports joins the eval set, so the same mistake is caught the next time we change anything.
Your content and business data are yours, always, and exportable at any time. The software itself is licensed to you for your business. Domains and store accounts can sit in your own name if you would prefer that, just tell us.
Tell us what you are trying to build.
