AI product strategy, LLM comparisons, AI coding tools, product analytics platforms, and experimentation frameworks — practical guides for Indian product teams building with AI.
Claude Sonnet 4.6 vs GPT-5.2 compared for PRDs, research synthesis, coding, image generation, and daily PM workflows. Which AI wins for Indian product teams?
AI CodingFeature matrix, INR pricing, agentic capabilities, and which AI IDE to pick for your Indian engineering team — from seed stage to enterprise.
AI StrategyBuild vs Buy API decisions, INR cost modelling at scale, latency on 4G networks, vernacular LLM strategies, and how Groww, CRED, and Practo implement AI.
AnalyticsFree tier comparison, INR cost modelling, session replay, self-hosting for RBI compliance, and which analytics tool to deploy by startup stage.
Automation5 high-value PM automations using Make.com's AI modules — competitive intel, feedback categorisation, meeting notes to JIRA, and metrics reporting.
ExperimentationVisual editor vs warehouse-native vs enterprise stats engine. Client-side vs server-side testing, and which experimentation tool fits your Indian team.
AI CodingHow product managers use Cursor's Composer and codebase indexing to generate PRDs, technical specs, and API documentation from existing code.
AI ToolsThe best ChatGPT plugins and custom GPTs for PM workflows — research, writing, data analysis, and automation use cases for Indian product teams.
Anthropic's AI assistant
OpenAI's flagship model
AI-powered research
Google's multimodal AI
AI-native code editor
AI code assistant
Product analytics
No-code automation
Hours saved weekly with AI-assisted workflows
Benchmark: 5–15 hrs/wkReturn on AI tool investment for product teams
Benchmark: 3–8xSprint velocity increase with AI coding tools
Benchmark: 30–50%Tests launched per month with proper tooling
Benchmark: 4–12/monthWe help product teams select, implement, and scale AI tools into real workflows. From tool selection to full automation systems.