Syndication and co-investment for institutional venture capital.

Inflexor Ventures has announced the first close of its third fund, the Inflexor Technology Discovery Fund, at approximately ₹400 crore. The fund is anchored by the Self-Reliant India Fund, HDFC Asset Management Company and HDFC AMC Select AIF FoF, and has also drawn

Benne has raised Rs 35 crore in a funding round led by Claypond Capital, the investment firm of Rajan Pai. Benne operates a restaurant chain in India built around dosa. No stage was attached to the round. The valuation, the terms, the size of the stake taken and the identity of

Solinas Integrity has raised $5.5 million in a Series A1 round co-led by Mela Ventures. The company builds technology for the water and sanitation sector. The other co-lead and any further participants in the round were not disclosed.

Mintoak has raised Rs 80 crore in debt funding from BlackSoil. The company is using the facility to support its acquisition of ICC Loyalty. Mintoak operates in the digital payments and merchant technology sector. The size of the acquisition, the timing of the transaction and the

BlissClub, an Indian direct-to-consumer athleisure and functional apparel brand, has raised Rs 160 crore ($16.8 million) in a Series B round led by Singularity AMC. Founder Minu Margeret and her partner Vidit Aatrey participated, alongside existing investors Elevation Capital

MatrAIx is a new AI evaluation infrastructure designed to test AI systems and digital products with large populations of diverse, simulated users. The paper was submitted to arXiv on 4 August 2026 by a large research team including researchers from Stanford, MIT and other institutions. (arXiv) Traditional AI and product testing has two limitations: Human testing is expensive, slow and difficult to scale. Standard AI evaluations tend to use fixed benchmarks that do not adequately capture differences in human behaviour, preferences, backgrounds and decision-making. MatrAIx attempts to bridge this gap by creating simulated populations of users that can interact with products and AI systems. What MatrAIx provides The system has three main components: 1. Persona 8B A database of 8.3 billion persona records, represented across 1,290 categorical dimensions. The personas capture combinations of attributes that can influence behaviour. The researchers created a quality-filtered dataset of approximately 1 million personas, including: 599,847 human-grounded personas 400,000 synthetic personas The underlying persona generation uses a dependency graph designed to preserve correlations between attributes rather than randomly combining characteristics. (arXiv) 2. MatrAIx Playground A set of environments where simulated users can interact with products: Surveys AI chatbots Websites Mobile applications 3. Application tasks The platform currently contains 1,010 evaluation tasks across more than 25 domains, including commerce, software, finance and healthcare. (arXiv) How it works Instead of asking: "Does the AI produce the correct answer?" MatrAIx can ask questions closer to:"How would different types of people react to this AI or product?" For example, simulated personas can reveal differences in: willingness to pay after a price increase tolerance for latency willingness to continue using a product after an AI failure preferences and decision-making reactions to different product experiences The researchers conducted 18,189 evaluation trials across eight representative tasks, using persona agents powered by Claude Opus 4.8, GPT-5.5 and Claude Haiku 4.5. (arXiv) Validation The paper reports a controlled study of 400 trials testing whether agents actually followed their assigned persona characteristics. Across ten behavioural attributes and four environments, the declared behaviour was correctly expressed-or correctly suppressed-in 366 trials, or 91.5% of cases. (arXiv) The researchers also evaluated how accurately human-grounded personas were extracted using both human and LLM judges. Why it matters The bigger idea is AI-powered synthetic populations for product and AI evaluation. Today, companies may need to recruit hundreds or thousands of users to answer questions such as: Would customers buy this product? How would different demographic segments respond? What happens if we increase the price? How will users react when the AI makes a mistake? Which version of an interface works better? How does an AI assistant behave across different user types? MatrAIx suggests that some of this testing could potentially be performed before expensive real-world testing, using large populations of simulated users. The important caveat The 8.3 billion figure should not be interpreted as 8.3 billion realistic AI people. It refers to persona records-combinations of attributes that can be instantiated into simulated users. The paper itself validates persona adherence, but that does not mean the simulated population reproduces real human behaviour perfectly. (arXiv) This distinction is important: synthetic users can dramatically increase the scale and speed of experimentation, but real human behaviour remains the ultimate validation layer. Strategic significance MatrAIx points towards a potentially important evolution in AI evaluation: Traditional testing → AI benchmarks → AI agents → simulated populations → continuous synthetic-user testing If this approach becomes reliable, companies could eventually test products against thousands or millions of different user profiles before deploying them to real customers. The particularly interesting opportunity is not simply "AI agents pretending to be humans." It is the creation of a large-scale simulation layer for understanding how different populations interact with AI, software and digital products. MatrAIx is an ambitious attempt to turn human-centric product testing into a scalable computational process - using billions of structured personas and AI agents to simulate heterogeneous user behaviour. (arXiv) https://arxiv.org/abs/2608.04205