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Digital Data Platform: Components, Architecture and Roadmap
What a digital data platform is, the six components it needs, how to choose between build and buy, and a phased roadmap that delivers value early.
Sunday Labs blog
Notes on building with AI and data.
Field notes from the founders and engineers at Sunday Labs on AI strategy, data platforms, agents and shipping to production.
Predictive Maintenance AI: A Practical Guide for Manufacturers
How manufacturers can use predictive maintenance AI to reduce unplanned downtime: which assets to start with, what data you need and how to scale.
AI Development Company in USA: A Buyer's Guide for US Teams
How US companies evaluate AI development partners: delivery models, time-zone overlap, HIPAA and state privacy laws, US contracting, IP ownership and security reviews.
LLM Security: Prompt Injection, Data Leakage and Safe Agents
A practical guide to LLM security: prompt injection, data leakage, agent tool use, RAG permissions, supply chain risk, red teaming and the controls that hold up in production.
Demand Forecasting for D2C Brands: A Practical Guide
How D2C brands can build demand forecasts that planners trust: the data, the models, the promotion problem and the operating rhythm that turns forecasts into inventory decisions.
SLOs and Error Budgets: A Practical Guide for Engineering Teams
How to choose SLIs, set SLOs, write an error budget policy, alert on burn rate and run on-call and post-mortems that make systems more reliable over time.
App Development Company: How to Choose, Brief and Contract One
What a good app development company does at each stage, from discovery and design to backend, store releases, QA on real devices, security and maintenance.
AI in Healthcare India: Practical Use Cases That Work Today
Where AI is genuinely helping Indian hospitals, diagnostic chains, insurers and pharma companies, and what it takes to move from pilot to daily use.
Best Website Development Company: How to Choose the Right One
A buyer's guide to choosing a website development company: stack, speed, SEO, accessibility, CMS, security, ownership, support, a scorecard and the red flags to avoid.
SOC 2 vs ISO 27001: Which Should an Indian SaaS Company Do First?
A plain comparison of SOC 2 and ISO 27001 for SaaS companies: who asks for which, how the audits differ, where the controls overlap and how to sequence them.
CI/CD Best Practices: A Practical Guide for Engineering Teams
Practical CI/CD best practices for teams that want to ship more often with fewer incidents, covering pipeline design, testing, security, releases and the metrics that matter.
How to Hire a Product Manager: Brief, Scorecard and Interviews
A practical guide to hiring a product manager: defining the role and level, writing the brief, a scorecard, an interview loop with a product case, references and the first 90 days.
MLOps Guide: How to Take a Model from Notebook to Production
What MLOps really involves, how mature your setup needs to be, and a step-by-step path for getting machine learning models into production and keeping them healthy.
Penetration Testing: A Practical Guide to Scoping, Reports and Retests
What penetration testing covers, how VAPT differs, which test types you need, how to scope a test, what a good report contains and how often to retest.
Generative AI in Banking: Practical Use Cases for Banks and NBFCs
Where generative AI genuinely helps banks and NBFCs today, where it does not, and how to deploy it safely under Indian regulatory expectations.
Data Lakehouse vs Data Warehouse: Which Should You Choose?
A practitioner's comparison of data lakehouses and data warehouses, covering architecture, cost, governance and AI workloads, with a decision guide for mid-size companies.
DPDP Act AI Compliance: A Practical Guide for Indian Companies
What India's Digital Personal Data Protection Act, 2023 means for AI projects, and a practical checklist for building compliant models and GenAI systems.
LLM Evaluation: A Practical Guide for Production AI Systems
How to evaluate LLM applications properly: golden datasets, metrics that matter, LLM-as-judge done safely, regression testing and production monitoring.
Cloud Cost Optimization: A Practical FinOps Checklist
A 25-point FinOps checklist for cutting cloud waste without slowing engineering, covering visibility, rightsizing, commitments, storage, Kubernetes and governance.
Enterprise AI Agents: How to Build Them for Production
A practitioner's guide to taking enterprise AI agents from demo to production: scope, architecture, guardrails, evaluation, security and rollout.
AI Implementation Cost in India: Drivers, Ranges and Budgeting
A practical guide to AI implementation cost: the real cost drivers, indicative ranges by project type, the running costs people forget and how to budget sensibly.
Sunday Is Now Two Brands: Sunday Labs and Sunday
Sunday now runs as two brands. Sunday Labs does AI and data consulting. Sunday is an AI-powered hiring platform. Here is why, and what it means for you.
Sunday Is Now Sunday Labs: A New Name for Our AI Consulting
Our AI and data consulting business is now Sunday Labs. Same team, same way of working, and a name that says how we work.
RAG vs Fine Tuning: How to Choose for Enterprise GenAI
RAG gives a model the right knowledge; fine-tuning changes how it behaves. A clear comparison and decision guide for choosing between them, or using both.
AI Consulting Services in India: How to Choose the Right Partner
What AI consulting services in India should actually deliver, how to evaluate firms, the questions to ask and the red flags to watch for before you sign.
AI Readiness Assessment: A Practical Checklist for Leaders
A scored AI readiness assessment checklist covering strategy, data, technology, people and governance, with a simple rubric and guidance on what to fix first.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
Managed DevOps Services vs an In-House Team: How to Choose
A practical comparison of managed DevOps services and in-house teams, covering cost drivers, coverage, control and the hybrid model most growing companies end up with.