How we saved 80% off LLM inference costs
How we saved 80 % off LLM inference costs by pruning “junk tokens”. And why this pattern works almost anywhere.
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5 Ways to Improve Your ML System
5 Ways to Improve Your ML System | Reduce cost and improve the quality of your Machine Learning pipelines.
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Case Study: Rebuilding a Scalable Student Financing Platform with Next.js and Prisma
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
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Case study: AI Legal Risk Detection | LLM + RAG for Content Review
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
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AI-Powered Sales Intelligence – Detecting Tech Buying Signals Through Job Postings
Identifying real-time buying intent has always been a challenge in sales. Humans can’t monitor thousands of job boards, careers pages, or corporate signals at once—but AI can. We partnered with a sales intelligence company to build a cutting-edge platform that automatically detects tech hiring signals, investment indicators, and purchase intent—giving sellers a head start on valuable opportunities.
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Case study: reducing 65% of ML pipeline time at Google [code available]
The AI Flow CEO helped Google push the boundaries of feature selection in machine learning pipelines. By building on top of their state-of-the-art research, we developed a tool that reduces feature sets by up to 64%, while maintaining model performance—cutting training time, inference time, and model size significantly, with the potential to scale across Google’s ecosystem.
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Case Study: Building a Legal Intelligence Platform with RAG & GPT-4o
We helped a US law firm ingest gigabytes of mixed-format case files and, within three weeks, launch a chatbot that answers legal queries and predicts case value in near real-time. A cost-optimised RAG pipeline, Pinecone vector store and GPT-4o power the solution—cutting storage by 75 % and response times below a second.
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Case Study: Oil & Gas Invoice Automation – End-to-End Data Engineering with Databricks & Azure
We helped a top-5 Oil & Gas firm turn tens of thousands of unstructured invoices into analytics-ready data. Using Databricks on Azure, AI-driven document intelligence and Power BI, we delivered a future-proof ETL pipeline that cuts costs by millions and speeds decision-making.
How to start learning AI in 2025
How to start learning AI in 2025 If you’ve scrolled through your feed recently, you’ve seen the AI buzz: from LLM integrations that claim to handle all your customer support needs to “game-changing” coding agents that promise to replace half your dev team. But behind those bold headlines, many founders and tech leads still ask
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How Tech Giants Leverage AI Acquisitions in 2025
How Tech Giants Leverage AI Acquisitions in 2025 Building a Strong Foundation in 2025 One trend that’s been hard to ignore lately: large companies that never started as “AI first” are on a buying spree, snapping up AI-focused startups in deals worth millions—even billions. This pattern isn’t just about big valuations; it’s a strategic push
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