Category Archives: Technology

Scaler Goes AI Native, Rebuilds Programs to Bridge Industry Skill Gap

Apr 16 (BNP): Bengaluru-based Scaler has become India’s first fully AI-native technology career platform, redesigning its entire learning ecosystem around artificial intelligence.

The platform has rebuilt its curriculum, projects, and hiring preparation modules to focus on hands-on AI skills rather than tool-based or theoretical learning.

The move follows a joint study with CyberMedia Research, which found that while 89% of engineers believe they are AI-ready, only 19% are actually working on AI systems, highlighting a major capability gap in the industry.

Scaler said the transformation is aimed at addressing this gap by working closely with over 1,200 companies, which reported a shortage of engineers with real-world AI development experience.

The initiative is designed to better align learning outcomes with industry needs in an increasingly AI-driven job market.

AAEON Looks to Break New Ground in AI Robotics Development with the Release of the CEXD-INTRBL

Available for preorder on the company’s e-commerce platform, the CEXD-INTRBL provides an all-in-one system for AI-optimized robotics development.

Taipei, Taiwan – Apr 16: Leading-edge AI platform provider AAEON (Stock Code: 6579) today announced the release of the CEXD-INTRBL, an open robotics development system from its Embedded Computing Business Unit. Featuring an Intel Core Ultra X7 Processor 358H CPU, an integrated Intel Arc B390 GPU, and NPU 5.0, the CEXD-INTRBL provides up to 180 TOPS of AI performance. As such, AAEON has noted the product is designed to target emerging AI development segments such as humanoid robotics and autonomous vehicle platform building.

The CEXD-INTRBL’s I/O lends itself to the system’s target market, with a notable feature being its two FAKRA connectors, which offer support for up to eight GMSL camera inputs. Other interfaces for peripheral device installation include four USB Type-C ports for the LiDAR, infrared, and depth sensors required for robotics, as well as a 22-bit GPIO through a HAT 40. Elsewhere, the system hosts four 2.5GbE LAN ports with IEEE 1588 PTP (Precision Time Protocol) alongside an external CANBus port.

For OS support, the CEXD-INTRBL is compatible with Windows 11 (64-bit) and Ubuntu 25.04, or Ubuntu 24.04 and later.

“We pride ourselves not only on how quickly we are able to leverage new technologies, but also on our dedication to building unique, creative solutions to address the needs of our customers,” said Kevin Chiu, Vice President of AAEON’s Embedded Computing Business Unit and Design Support Division. “From the outset we were confident we could create an all-in-one development system with the potential to usher in the next generation of advanced robotics, and with the CEXD-INTRBL, we feel we have succeeded in doing so.” Chiu added.

Auctor Raises $20M Led by Sequoia Capital to Build the AI System of Action for the Enterprise Software Implementation Market

Enterprise software implementations fail because of fragmented institutional knowledge and tools. Auctor fixes that with one unified system built for the work itself. 

New York City, New York – Apr 16; Hundreds of billions are spent on software implementation each year, yet 50 percent of projects fail to meet deadlines, and one out of every six exceeds budgets by over 200 percent.

Today, Auctor, the AI-native system of action for the entire software implementation lifecycle, emerges from stealth. It enables professional services teams and system integrators to deliver faster, more consistently, and smarter with every project.

Auctor has raised a total of $20 million, including a Series A led by Sequoia Capital with participation from M12, Microsoft’s Venture Fund, HubSpot Ventures, Workday Ventures, OneStream, Y Combinator, Tercera, and Dig Ventures.

William Sun, the Co-Founder and CEO of Auctor, said, “Enterprise software has transformed how every industry operates, but it only creates value when it’s actually implemented well. That’s why we built Auctor: one system for the entire lifecycle, so humans can focus on the high-judgment work clients need, while Auctor handles the rest.”

Professional services and implementation teams still rely on a patchwork of meetings, spreadsheets, documents, and internal knowledge to manage discovery, scoping, solutioning, and delivery. As a result, requirements, decisions, and context are fragmented across systems and stakeholders, with no single source of truth. This fragmentation leads to misalignment, rework, margin erosion, and delayed time-to-value for customers.

“As HubSpot moves upmarket, faster and smarter implementations aren’t just nice to have, they’re essential. Auctor is built specifically to solve that problem, giving system integrators and services teams an AI-native platform that brings together critical project context and turns weeks of manual work into minutes. We’re excited to support a team that’s creating an entirely new category and solving a problem that matters for our partners and customers,” says Adam Coccari, Managing Director at HubSpot Ventures.

Auctor’s AI-native system of action is purpose-built for how implementation work actually runs in practice. It curates execution-ready artifacts like rough orders of magnitude, resource plans, process flows, user stories, and more – already aligned and ready for delivery.

As a result, users and teams always know what was decided, why it was decided, and how it impacts the rest of the engagement. Most importantly, Auctor helps companies standardize what great looks like, turning their best work into repeatable, reusable practices across every project. 

Auctor is already seeing top teams across leading software ecosystems fundamentally change how they run implementations. Customers are driving upwards of 80% efficiency gains across discovery and design, improving margins and even shifting toward fixed-fee models. 

“The improvement in collaboration and delivery quality has been immediate. As we continue to scale globally, Auctor is becoming a core enabler of how we operate,” said Dan Buffham, CIO of Valiantys, Atlassian’s largest global partner, which serves 65 Fortune 500 companies. 

The results extend across the entire implementation lifecycle. One team used Auctor to respond to an RFP (request for proposal) over a single weekend with just one person, secured the opportunity, and closed it within two days — work that previously required weeks and multiple team members. Separately, a principal consultant at a large enterprise software company produced a comprehensive manufacturing scoping guide in roughly 10 minutes, replacing a three-week manual effort.

The market dynamics driving Auctor’s growth are structural. 

Implementation firms are caught between a talent model that doesn’t scale and a competitive environment that won’t wait. Senior consultants are spread too thin. Junior staff lack institutional knowledge. Mid-project swaps mean someone is always ramping up. The firms that figure out how to run leaner without sacrificing quality will take market share from those that don’t. 

For system integrators stuck in margin-constrained models where delivery costs scale linearly with headcount, the math is straightforward: Auctor can unlock multiple points of EBITDA margin by fundamentally changing the way of operating.

Julien Bek, partner at Sequoia Capital, who recently wrote a viral thought leadership piece (Services: The New Software), says, “For every dollar spent on software, six are spent on services. Auctor is building the agentic operating system for software implementation to go after those six dollars.” 

Government Notifies SEZ for Tata Semiconductor Project in Dholera

Apr 15 (BNP): The government on Wednesday approved and notified a Special Economic Zone (SEZ) to be developed by Tata Semiconductor Manufacturing Pvt Ltd in Dholera, Gujarat. The proposed zone will focus on electronic hardware, software, and IT/ITeS services.

The project includes a planned investment of ₹91,000 crore to set up India’s first semiconductor fabrication facility, marking a major step toward strengthening the country’s semiconductor and electronics manufacturing ecosystem.

The proposal was cleared by the Board of Approval, the highest authority for SEZ-related decisions, which is headed by the Commerce Secretary.

The SEZ is expected to support large-scale manufacturing, attract investment, and boost India’s presence in the global semiconductor supply chain.

GIA 2.0 Soon to Expand Karnataka’s Tech Partnerships

Apr 15 (BNP): Karnataka’s Global Innovation Alliance (GIA) has emerged as a strong platform for international technology collaboration, helping connect startups, innovators, and institutions across global markets.

The initiative has facilitated partnerships between Karnataka-based startups and global innovation ecosystems, strengthening knowledge exchange and access to international opportunities.

Officials said the programme has helped position Karnataka as a leading hub for innovation-driven growth and global tech engagement.

The state is now preparing to launch GIA 2.0, which is expected to further expand global partnerships and enhance support for emerging technologies and startups.

Helical raises $10M for virtual AI lab that operates at pharma scale to make in-silico discovery reproducible 

The Helical virtual AI lab for pharma, an application layer that turns biological foundation models into decision-ready, reproducible in-silico discovery workflows. The $10 million funding will support expansion across more top-20 pharma programs and growth of its deployed science engineering team.

London, UK – Apr 15; Pharma has no shortage of ideas. It has a shortage of throughput. Roughly 50 new drugs are approved each year despite more than 10,000 known diseases, and every promising hypothesis still collides with the same constraint: slow, expensive physical experimentation. Biological foundation models have opened the door to a new mode of discovery, where scientists can test hypotheses computationally before committing to the wet lab. Helical was built to make that shift real inside modern pharma R&D.

Today, the company announced a $10 million seed round led by redalpine with participation from Gradient, BoxGroup, Frst and notable angels including Aidan Gomez (CEO Cohere), Clement Delangue (CEO HuggingFace) and Mario Goetze (pro soccer player).

The timing reflects a gap that has emerged as bio foundation models have taken off. Pharma teams are excited about the model layer, but many efforts stall because the work between a model output and a scientific decision is still fragmented. New architectures are emerging constantly, while bench scientists and ML engineers operate in silos. As a result, teams often recreate one-off notebooks and analyses that are difficult to reproduce or transfer across programs. What pharma has needed is an application layer that turns powerful models into systems scientists can run, trust, and defend.

Helical is the virtual AI lab for pharma, designed to turn bio foundation models into reproducible discovery systems so every scientist can test hypotheses in-silico at the speed of inference. The platform has two product surfaces — the Virtual Lab for biologists and translational scientists, and the Model Factory for ML engineers and data scientists — built on the same data, the same models, and the same results. By putting both sides in the same system, Helical closes the gap between computational predictions and biological decision-making, so teams that traditionally worked in silos can collaborate on the same evidence.

“The models alone don’t discover drugs. The system does” said Rick Schneider, co-founder of Helical. “Pharma teams need a system that turns foundation models into workflows scientists can run, validate, and defend. We built Helical to make in-silico science reproducible at pharma scale, so teams can go from hypothesis to decision in days instead of months.”

Helical was founded in early 2024. The company was created by three school friends who took different paths into the same problem. Rick Schneider built tech at Amazon and later helped the German enterprise Celonis scale in France and Japan. Maxime Allard led data science teams at IBM before pursuing a PhD focused on reinforcement learning and robotics. Mathieu Klop became a cardiologist and genomics researcher. When bio foundation models emerged, the trio saw the chance to build the missing application layer that would let pharma teams move from model experimentation to reproducible, production discovery.

Helical is already in production with multiple top-20 global pharma companies, including a public collaboration with Pfizer on predictive blood-based safety biomarkers. Across deployments in target identification, biomarker discovery, and therapeutic design, teams have compressed discovery timelines from years to weeks and expanded organically from single indications into adjacent therapeutic areas.

The broader industry context is increasingly unforgiving. R&D spending exceeds $300 billion annually, timelines stretch beyond a decade, costs to bring a drug to market now exceed $2 billion on average, and more than 90 percent of candidates entering clinical trials fail. AI has been positioned as the answer, but many efforts stall in pilot because predictions alone are not enough. Discovery teams need outputs grounded in biological evidence, delivered through a system that makes decisions reproducible and explainable, not another black-box ranking. 

“We are at a unique point in time where biological foundation models and general language reasoning models are converging.” Said Daniel Graf, General Partner at redalpine. “We backed Helical because we strongly believe they have what it takes to build the pharma AI orchestration platform that will drive this transition from siloed AI models to integrated virtual AI labs.”

Looking ahead, Helical plans to deepen deployments across more therapeutic areas and programs with existing clients, expand to additional top-20 pharma organizations, and continue building the compounding evidence layer that improves performance across diseases. The company’s mission is to make every scientist able to test hypotheses at the speed of inference and to turn in-silico discovery into a reliable engine for R&D throughput.

PayU Rolls Out AI Voice Tool to Streamline Onboarding

GoCredit Rolls Out AI-Powered Loan Assistant to Simplify Borrowing

Bangalore, Apr 15 (BNP): Financial intelligence platform GoCredit has launched what it claims to be India’s first AI-powered Loan Agent in Bengaluru, introducing a chat-based system that helps users find and apply for personal loans through a single conversational interface.

The AI tool allows borrowers to compare loan options, check eligibility, and complete applications within one interaction, simplifying the lending process.

According to the company, the system has been in active use since March 2026 and is already processing real loan applications.

GoCredit also reported that it has facilitated over ₹100 crore in loan disbursals, generated more than 15 million loan offers, and served over 2 million borrowers so far.

The platform aims to make credit access faster, more transparent, and easier for users through artificial intelligence-driven solutions.

Kärcher India Champions ‘Mechanised cleaning’ with Manual vs Mechanised’ Campaign

New Delhi, Apr 15: The recent campaign launched by Kärcher India is known as “True Clean” and encouraging consumers to move beyond traditional cleaning methods and adopt smarter, more efficient mechanised solutions

This marketing campaign is founded on the principle that there is a distinction between surfaces that seem clean and are truly clean. Regular practices like sweeping and scrubbing tend to disperse dust and leave behind stubborn grime along with requiring physical exertion continuously. In this way, Kärcher emphasizes “invisible inefficiencies” that are associated with traditional cleaning processes and do not lead to true hygiene.

“Clean = Kärcher” is one of the company’s key slogans. This slogan reflects the efficacy of the technology-driven products provided by the organization from vacuum cleaners that capture and hold in dust instead of dispersing it, to steam cleaners that promise 100% chemical free cleaning process as well as killing off 99.999% of viruses and 99.99% of bacteria, along with high-pressure washers that deliver powerful cleaning far beyond conventional methods.

Ayesha Prasad Narain, AGM Marketing at Kärcher India said, “The cleaning process cannot be limited only to the surface level; we must go beyond visible cleaning results. This is the message of our “Manual vs Mechanised‘ campaign, which is designed to help consumers see the difference between perception and reality and motivate them to choose technology-based smart solutions that offer deep cleaning without putting any additional effort.” 

The campaign is also an indication of changing consumer behavior and lifestyle. By introducing innovations like robotic vacuum cleaners and easy-to-carry cleaning products, Kärcher makes it possible for consumers to opt for cleaner yet efficient ways of cleaning.

Through this campaign, Kärcher India aims to drive a clear shift in consumer mindset—from viewing cleaning as a manual chore to experiencing it as a smart, effortless, and efficient process.

Orbital sets date for first test mission to put AI data centers in low Earth orbit

Orbital is building and operating AI data centers in space, using solar power and radiative cooling to remove the energy and cooling constraints that limit terrestrial AI infrastructure. Backed by funding from a16z Speedrun, the company plans to launch its first test mission in 2027 and is opening Factory-1, its R&D facility in Los Angeles.

Los Angeles, California – Apr 15; The demand for AI compute is surging, but the bottleneck is no longer chips, it’s the power required to run them. Orbital was founded on the belief that the only way to scale compute and unlock future progress on artificial intelligence is to stop competing for power on Earth and generate it in orbit.

Today, the company announced funding from a16z Speedrun to support Orbital-1, the company’s first test mission on its aim of deploying data centers in space. “Speedrun backs founders to explore ambitious ideas — the harder the problem, the better,” said Andrew Chen, General Partner, a16z speedrun. “Orbital is taking on AI‘s biggest constraint with a bold and radical idea.”

Orbital is designing and manufacturing a constellation of satellites to operate in low Earth orbit, each housing a cluster of NVIDIA-powered servers. Each satellite is powered by solar arrays and cooled by radiating heat directly into space. In orbit, solar power is available 24/7 in sun-synchronous orbit and stronger, with no weather, no night, and no dependence on the power grid. 

“AI progress is being constrained by the grid,” said Euwyn Poon, CEO and founder of Orbital. “Data center economics are dominated by electricity and cooling, and both are getting harder. In orbit, solar power is continuous and cooling is fundamentally different. Orbital is building compute infrastructure that removes the energy ceiling and scales with AI‘s potential.”

Orbital‘s compute infrastructure is designed around a specific technical insight. Training large AI models requires thousands of GPUs tightly coupled, communicating at near-zero latency. That architecture does not translate to satellites. Inference is different. Each request is handled independently, and capacity can be distributed across many nodes. Orbital is focused on inference, where orbital compute can scale as a constellation and serve workloads in parallel.

Orbital‘s first satellite, Orbital-1, is scheduled to launch on a SpaceX Falcon 9 in April 2027. Its primary goal is to validate sustained GPU operation in orbit, test radiation hardening, and run AI inference workloads commercially in space post-validation. The company is also in the process of filing with the FCC to deploy a constellation of satellites for orbital AI compute infrastructure.

Orbital was founded by Euwyn Poon, a Cornell-educated engineer and lawyer who previously founded Spin, the micromobility company acquired by Ford. At Spin, Poon built and deployed hundreds of thousands of small electric vehicles across 100 cities and scaled the business to over $100 million in revenue. After exiting Spin, he began investing in AI infrastructure and saw the impending constraint clearly. “The energy ceiling on AI isn’t theoretical, it’s a real constraint that will impede the advancement of intelligence,” said Poon. “This is the solution.”