Those tasks are easy to miss when care teams are stretched. For patients, the consequences can be another delayed appointment or a gap in coverage.

Grapefruit Health is building a workforce around that need. The company hires nursing, pharmacy, and social work students as remote Patient Champions, giving them paid experience supporting real patients while they are still in school.

Founder and CEO Eric Alvarez spoke with Nurse.org about how the model works. Grapefruit partners with health plans, provider groups, laboratories, and health systems to design outreach programs around specific goals, such as closing care gaps or helping members stay enrolled. Its platform matches students to patients and manages the outreach, with language, culture, and community helping inform those matches.

For students, the work is a chance to practice listening, explain health information in plain language, understand barriers to care, and document patient conversations. They also learn how the wider healthcare system works, including the connection between patient needs and health-plan priorities.

For healthcare organizations, it adds capacity for work that otherwise competes with the demands of hands-on care. That combination is what makes the model compelling: students develop skills they will carry into their careers while helping patients take the next step in their care today.

Proud to back Eric and the Grapefruit Health team.

Join Jelly, Build with Everywhere’s private market intelligence platform!

Jelly is a startup discovery and research platform that brings together millions of data points on private companies, investors, funding activity, accelerator batches, and more.

Tana’s new Bring Your Own Agent brings that work into the conversation itself.

Teams can bring Claude, ChatGPT, Lovable, Ari, and other supported agents into meetings, using their agents’ skills and tools to act on requests while people are still talking. The experience works in Tana, Google Meet, Zoom, and Teams.

The distinction is not just capturing what happened. It is giving the team a way to do something with the discussion as it unfolds. Instead of adding another follow-up to a list, someone can ask an agent to help with the task there and then.

For teams already using AI in their individual workflows, that creates a shared setting for putting those tools to work. People can discuss a request, see the output, and refine it together.

We’re proud to back Tana and excited to see what teams build around this new way of working.

See more on the launch here!

Join Jelly’s Waitlist!

At the earliest stages, information about private companies is fragmented across countless sources. A company might appear in an accelerator batch, a funding announcement, an investor’s portfolio, or a founder’s LinkedIn profile long before it shows up in a traditional database. Each signal matters, but it often arrives in a different channel, and piecing together the broader picture takes time. Existing data platforms can be expensive, yet still leave significant gaps in coverage and context. For a lean investment team, time spent searching across databases, tracking investor activity, and reconstructing context is time not spent with founders or making decisions.

Today, we’re launching Jelly, built for the earliest stage of venture, when a promising company is hardest to find and even harder to understand. Jelly brings together millions of data points around private companies, the funds that back them, precedent transactions, M&A, accelerator / hacker house batches, and signals from top investors. It turns a bare name and logo into a full profile including what the company does, who has invested, how much the founder has raised and where it fits in the broader market. This not only provides a tool for investors, but our hope is that it substantively democratizes how undiscovered founders are surfaced.

We initially built Jelly for ourselves, but the challenges it addresses are hardly unique to Everywhere. Many investors face the same fragmented information landscape, often with lean teams with limited time and resources. Making Jelly available to our peers is a natural extension of why we built it in the first place.

Jelly is also part of something broader we’ve been building inside Everywhere over the past year.

As our portfolio has grown, so have the operational demands of running a venture firm. Portfolio updates, LP communications, diligence, pipeline tracking, and fundraising support all require coordination across disconnected tools and channels. We wanted a better way to operate, so we began automating the workflows we were already running inside Everywhere Ventures and owning the full technology stack.

That effort has evolved into BUILD with Everywhere, our broader automation platform for investment firms, built by operators inside an active venture fund and informed by real workflows. The goal is to make information easier to access, connect, and put to work.

We’ve spent eight years building relationships and conviction across a global portfolio, and the past year building more of the technology behind that work. With Jelly and BUILD with Everywhere, we’re beginning to extend those efforts to our peers. Less time piecing the picture together equals more time to spend with founders.

P.S. Wondering why we called it Jelly? Think of the initials of the incumbent tool and add your favorite childhood sandwich… 🥜🍇

Boom has raised a $15M Series A led by S3 Ventures and launched BoomCRM, an AI-powered leasing CRM for property managers.

The product answers calls, qualifies prospects and books tours, connecting those steps with tenant screening in one system. Information collected before an application carries through, so applicants avoid duplicate data entry and charges. Built for portfolios spread across locations, BoomCRM also schedules tours around an agent’s travel time.

Boom already runs screening for more than 400 operators covering over 500,000 units. Congratulations to Rob Whiting and the team on this next chapter!

Read the full Axios announcement.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

For patients with recurrent infections, treatment starts with understanding what is causing the problem. Standard laboratory methods do not always provide the answers clinicians need, leaving difficult infections unresolved.

Biotia is tackling that diagnostic gap with pathogen genomics and AI.

Founded by Niamh O’Hara and Chris Mason, the company develops tests for challenging and chronic infections. Its BIOTIA-ID Urine Test identifies the pathogens behind recurrent and complicated urinary tract infections and is available to physicians and patients across all 50 states.

Now Biotia has raised an oversubscribed $9M financing to scale its New York City laboratory, launch additional diagnostics in women’s health, and expand into orthopedics. The capital will also support its work on reimbursement and getting these tools into more clinicians’ hands.

The team operates a high-complexity, CLIA-certified diagnostic laboratory and works with collaborators including Mayo Clinic and Hospital for Special Surgery. That work connects its genomics platform to the clinical settings where challenging infections are diagnosed and treated.

Biotia is also adding experience across healthcare delivery, diagnostics and commercialization. Michele Colucci, CEO of DigitalDx Ventures, is board chair, and VillageMD co-founder Tim Barry has joined the board. Fellow VillageMD co-founder Dr. Clive Fields is a board observer.

Congratulations to Niamh, Chris and the Biotia team on this milestone. Excited to see their work reach more patients and clinicians.

Read the full PR Newswire announcement.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Electric Plant Company is building a way to close that gap. Today, the company is emerging from stealth with $11.2M in seed funding and introducing Bombadil, its foundation AI model for plant electrical signals.

The system starts with Bud, a low-cost sensor that attaches directly to a plant and records electrical activity in its tissue. Bombadil interprets that stream of data to estimate seven physiological stress markers, including measures of water stress and photosynthetic efficiency. Rather than waiting for a visible change, growers gain another way to understand what is happening inside the plant.

The team pretrained Bombadil on more than 4 million hours of recordings from over 1,400 plants across 100 species. Its dataset has since grown to 7 million hours. A key capability is applying what it learns across species, including plants it did not encounter during pretraining. That matters because a tool that needs to be rebuilt for every crop would be much harder to put to work broadly.

Co-founders Justin Mast and Lee von Kraus bring complementary experience to that challenge. Justin previously founded Bloomscape, while Lee is a neuroscientist and co-founder of Halo Neuroscience. Together, they are combining plant care experience with expertise in biological signals.

The technology is already being deployed at commercial sites, with hundreds of sensors in the field. Electric Plant licenses its technology to existing plant care operators, giving them a new source of insight rather than asking them to replace their businesses or expertise.

For us, the opportunity is to make an overlooked source of biological data useful. Better visibility into plant health could help growers make more informed decisions about water, care, and intervention. We’re proud to back Justin, Lee, and the team as they bring that capability to more plants and more operators.

Read the full announcement.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

For finance and engineering teams, understanding that spend is only the beginning. They also have to decide which commitments to buy, where resources are being wasted, why costs changed, and how to keep spending aligned with the business. A dashboard can surface the problem without doing much to reduce the work behind it.

North is building a system to connect those decisions.

The company has raised a $60M Series B led by PeakSpan Capital, with participation from Litquidity Ventures, Companyon Ventures, Alpaca, Uncommon Denominator, Gaingels, and Mana Ventures. Congratulations to Matt Biringer, Yassine Açoine, and the entire North team on this next chapter.

North began by automating cloud commitment purchases. Providers offer discounts in exchange for committing to future usage, but making the right purchase requires predicting demand well in advance. Buying too little leaves savings behind. Buying too much means paying for capacity the business does not need.

As the team worked with customers, the opportunity expanded beyond purchasing. Companies were stitching together spreadsheets and multiple tools to manage their infrastructure budgets, pulling engineers away from building while finance waited for answers.

North now brings cost visibility, allocation, forecasting, and optimization into one platform. Teams can manage spending across AWS, Google Cloud, and Azure alongside AI and data services. They can map costs to teams or customers, identify unusual spending, and find resources that are over- or under-provisioned. Noros, North’s FinOps agent, helps explain changes in plain language.

The important shift is from understanding spend to acting on it. Automated commitment management can reduce the manual work of buying and adjusting cloud discounts. Rightsizing recommendations and anomaly detection help teams identify where usage needs attention. Together, those capabilities give finance and engineering a shared way to manage infrastructure economics rather than another set of reports to reconcile.

North now manages close to $2B in cloud spend and has saved customers more than $400M. Customers include AssemblyAI, Tines, Atlan, and SnapLogic. The new funding will support deeper automation, additional integrations, AI spend governance, and growth across the team.

As AI becomes a larger part of the software stack, infrastructure cost management becomes a more consequential operating decision. Companies need to understand not just what they are spending, but what that spending supports and where they can improve it without slowing the business down.

We’re excited to see North continue building toward that goal, giving teams more time to build and a clearer view of the economics behind their infrastructure.

Read the full release here.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Hospital operations depend on thousands of decisions that have to be made every day: who is working, where staff are needed, which beds are available, where patients should go, and how to keep clinical teams aligned as demand changes in real time.

Too often, that work still happens across spreadsheets, group texts, phone calls, and disconnected tools.

Vitalize is building a better system.

The company, founded by Veeraj Shah and Sanketh Andhavarapu, is developing an AI-powered operating system for hospital staffing, scheduling, flow, and capacity. Its platform helps health systems forecast demand, automate staffing workflows, fill schedule gaps, manage capacity, and orchestrate staff across clinical units in real time.

The need is large and immediate. Labor represents the largest expense for health systems, accounting for roughly 50% to 60% of hospital operating costs and approaching $1 trillion in annual spend nationwide. Without better forecasting and automation, large health systems can spend more than $100 million a year on premium overtime and agency labor.

The operational challenge is not just financial. Staffing and capacity decisions affect patient flow, length of stay, bed availability, clinician experience, and the speed at which patients can access care. When beds and staffing are managed in separate silos, hospitals can end up with unused capacity in one area and bottlenecks in another.

Vitalize approaches the problem as a complex logistics challenge. Its software integrates with electronic medical record and time-and-payroll systems, replacing the patchwork of scheduling, staffing, and capacity tools many hospitals rely on today. The platform uses machine learning, optimization models, and AI agents to forecast staffing needs, recommend patient placement decisions, and automate the manual coordination that clinical leaders still handle every day.

That focus is rooted in firsthand experience. Shah saw the problem directly while in medical school and while working inside health system staffing offices, command centers, and clinical units. The founding team originally started Vitalize as a healthcare worker wellness app before pivoting toward the operational infrastructure behind staffing and capacity management.

Now Vitalize has raised a $31M Series A led by Oak HC/FT, with participation from Norwest Ventures, .406 Ventures, Constellation Ventures, Y Combinator, and MemorialCare Innovation Fund.

Hospitals do not just need more software. They need systems that make the hardest operational decisions easier, faster, and more coordinated. Vitalize is building toward that future: an operating system for the hospital itself.

Read the full Fierce Healthcare article.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Commercial insurance is one of the largest and most operationally complex markets in financial services.

Most insurance is still sold through human agents, and for good reason: the work is nuanced, relationship-driven, and highly specific to each customer. But the systems supporting those agents have not kept up. Brokers and agencies still spend enormous amounts of time on manual paperwork, application forms, document review, and administrative tasks that slow down the process for both customers and advisors.

Outmarket is building for that gap.

The company, founded by Vishal Sankhla, uses AI to help insurance agencies and brokers automate the tedious back-office work that sits behind commercial insurance. The opportunity is especially large in commercial lines, where businesses need coverage across a wide range of risks — from general liability and workers’ compensation to D&O and other specialized policies.

That complexity creates a real workflow problem. Different businesses need different coverage. Different coverage types require different applications, documents, and review processes. Brokers are expected to make sense of all of it while still serving customers quickly and thoughtfully.

Outmarket’s platform helps automate the repetitive administrative work so brokers can spend more time on the higher-value parts of the job: understanding a client’s needs, recommending the right coverage, and responding when something urgent happens.

The company’s momentum reflects how much demand exists for better infrastructure in insurance distribution. Outmarket has already attracted more than 300 insurance agencies as customers, including 25% of the top 100, only 14 months after launching its new product.

Now, Outmarket has raised a $34.5M Series B led by SignalFire, with participation from Fika Ventures, Permanent Capital Ventures, TTV Capital, and Dash Fund. The financing follows the company’s $17M Series A just four months earlier.

The broader market is enormous. The U.S. property and casualty insurance market alone represents more than a trillion dollars in annual premiums. As AI becomes more capable of handling complex, document-heavy workflows, insurance distribution is a natural category for transformation.

Outmarket is not trying to replace the human relationship at the center of insurance. It is giving brokers better tools so they can work faster, smarter, and with more time for the judgment-driven work customers actually value.

Read the full TechCrunch article.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

The partnership will use Century Health’s CHARM AI platform to mine more than a decade of ophthalmology data and convert unstructured clinical information into structured, research-grade datasets. The work focuses on chronic retinal diseases including diabetic retinopathy and age-related macular degeneration, drawing from more than 200,000 de-identified patient records.

This is an important unlock for ophthalmology and life sciences research. Some of the most valuable variables in eye care, imaging findings, subtle structural changes, quantitative retinal measurements, and disease progression metrics, often live inside PDFs, scanned reports, and other unstructured files. That makes them difficult to use at scale for drug development, trial design, post-marketing research, payer evidence, and label expansion work.

Century Health is building the infrastructure to make that data usable. Its CHARM AI platform extracts and structures longitudinal clinical data so life sciences teams can better understand how patients progress and respond to therapies over years of routine care.

For Century Health, the Eye Health America partnership deepens the company’s footprint in ophthalmology and supports its broader strategy of building a specialty-focused data network across areas including neurology, immunology, and ophthalmology. The company now covers more than one million longitudinal records across 12 disease categories.

Congratulations to the Century Health team on this partnership.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Trebellar announced an $18M Series A led by Blossom Capital, with participation from Alt Capital, Haystack, 1Flourish, and Bynd.

The company is building the AI-native platform for corporate real estate. Enterprise real estate teams sit on a huge amount of operational data across facilities, workplace systems, sensors, occupancy tools, asset records, and planning workflows, but that data is often fragmented across disconnected systems. Trebellar brings that information together and turns it into actionable intelligence for teams managing complex real estate portfolios.

That matters because corporate real estate is becoming a more strategic function. Companies need to understand how space is used, where costs can be optimized, how employees experience the workplace, and how facilities decisions connect to broader business goals. Trebellar gives teams the data foundation and AI layer to make those decisions faster and with more confidence.

The company already counts Meta, Uber, and Merck among its approved customer references, and the new funding will support continued growth across engineering, go-to-market, AI capabilities, and enterprise adoption.

Congratulations to Diego Ferriero Val and the full Trebellar team!

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Congratulations to Ilana Springer Borkenstein, Eric Gruskin, and the full M7 Health team on M7’s acquisition by Ascend Learning.

M7 was built around a problem that every health system understands: clinical staffing is one of the hardest operational challenges in healthcare. Nurse schedules need to balance coverage, cost, preferences, retention, burnout, and patient care needs, but too many teams are still stuck managing that complexity across manual processes and disconnected tools.

M7’s platform helps health systems forecast staffing needs, understand their workforce, optimize schedules, and plan ahead. The product gives nursing leaders and operational teams a better way to match staffing demand with the realities of the clinical workforce, while creating more flexible and predictable work environments for nurses.

The company has grown from a proof of concept into a workforce optimization platform powering hundreds of hospitals and supporting tens of thousands of clinicians every day. That progress reflects both the scale of the problem and the quality of the team Ilana has built.

Joining Ascend Learning gives M7 more resources, distribution, and reach to continue its mission of transforming how healthcare work gets done. We are proud to have been part of the M7 journey and excited for this next chapter.

Read more: https://lnkd.in/gBw7yi8p

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Congratulations to Andrew McCarthy and the Axo Longevity team on raising an oversubscribed $5M seed round.

Axo is building a preventive health membership designed to make deeper health insight more accessible. The platform combines 100+ biomarker blood tests, ongoing tracking, and personalized guidance across nutrition, sleep, movement, and supplements so members can better understand what is happening in their bodies over time.

The need is clear: most healthcare still operates reactively, and many consumers only get meaningful testing or interpretation after something has already gone wrong. Axo’s model gives members a more structured way to monitor their health, identify imbalances earlier, and make targeted changes with clearer data behind them.

The company has already expanded across multiple European markets and built a broad partner lab network, making the experience more convenient for members who want preventive testing without navigating the traditional healthcare system on their own. Axo’s annual membership includes recurring biomarker analysis and an expert-backed plan, with the company continuing to add capabilities like an AI health copilot and wearable integrations.

We invested in Axo because we believe preventive health will increasingly look like an ongoing membership, not a one-off check-up. As biomarker testing becomes more affordable and consumers become more comfortable using data to manage their health, companies that can combine access, interpretation, and behavior change in one product will be well positioned.

This new financing will help Axo expand into additional countries, grow its laboratory network, and continue building a product experience that makes preventive health practical, personalized, and more affordable. We’re excited to see Andrew and the team continue building toward a new standard for proactive health across Europe.

Read the full Business Wire announcement.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Congratulations to Jeff Wald and the BoomerangHR team on emerging from stealth and announcing a $12.7M seed round, as covered by HR Brew.

BoomerangHR is building alumni management software for the modern HR team. The platform connects with a company’s existing HRMS and ATS systems, then automates key workflows around offboarding, exit compliance, alumni engagement, referrals, and rehire matching. Instead of treating an employee’s last day as the end of the relationship, Boomerang helps companies maintain a structured, active alumni network.

That matters because former employees can be one of the strongest pools of future talent and relationship capital. They already understand the company’s culture, systems, and customers. They can come back as high-signal rehires, refer strong candidates, become customers or partners, and continue to strengthen the employer brand if the exit experience is handled well.

The HR Brew piece highlights how BoomerangHR is designed to run with minimal lift from HR teams. The platform integrates into existing systems, supports offboarding workflows like COBRA administration, 401(k) rollovers, equipment returns, and exit interviews, and keeps verified alumni profiles current so companies can match former employees to new roles.

Warby Parker is cited as an early customer, with Chief People Officer Chelsea Kaden noting that BoomerangHR gives the company a streamlined way to stay connected with former team members, whether they return, refer someone great, or simply remain part of the broader team community.

We’re excited to see BoomerangHR bring more structure to a part of the employee lifecycle that most companies still manage manually, if at all. Congratulations again to Jeff and the team on this milestone.

Read the original article here.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

That is a big deal because many scientific and manufacturing workflows still depend on brittle, bespoke integrations. A lab may have multiple automated machines, but each device often speaks its own language. Connecting them into a single workflow can take specialists weeks or months. Anthropic’s goal with MHS is to reduce that integration work to hours or minutes while giving AI agents safer, standardized ways to discover devices, understand what they can do, operate within safety limits, and coordinate work across instruments.

Tetsuwan, one of our portfolio companies, is directly aligned with that future.

Tetsuwan is building an automated biology lab available to researchers and agents through an API. Its platform, ResearchOS, lets users generate, run, and manage automated workflows from natural-language protocols. Claude can help translate those protocols into scripts, which are then processed by Tetsuwan’s custom compiler into automation code for lab equipment.

In Anthropic’s research preview, Tetsuwan integrated MHS with ResearchOS to run qPCR workflows for a citizen science project studying pollution in California’s San Pedro Creek. The workflow involved coordinating across lab devices, detecting issues during liquid handling, and recovering from errors in real time.

One example captures why this matters. qPCR workflows often involve viscous reagents that can bubble or foam during pipetting, which can compromise an experiment. In the Tetsuwan pilot, a camera detected bubbles in a transfer. Claude, operating through MHS, helped identify a recovery strategy: move the tube to a centrifuge and briefly spin it to draw the liquid down. Through MHS, Claude could then issue the appropriate commands to the centrifuge.

That kind of orchestration is the real unlock. It is not just “AI plus robot.” It is a system where protocols can become more hardware-independent, devices can be discovered and coordinated dynamically, and experiments can adapt when conditions change. Anthropic’s post also notes that Tetsuwan used Claude and MHS in a closed-loop optimization experiment to improve compiler heuristics, testing 9,143 individual dispenses, 300 unique transfer types, and 1,508 measured conditions across four liquid types.

This is why we invested in Tetsuwan. Biology is dynamic. Experiments vary from run to run. The long tail of scientific work cannot be automated with rigid, one-off configurations alone. It needs an operating system that can turn scientific intent into reliable execution across real-world instruments.

ResearchOS and MHS point toward that future: labs where researchers can express what they want to test, agents can help translate that intent into workflows, and automated equipment can execute, monitor, recover, and improve over time.

Congratulations to Cristian Ponce, Théo Schäfer, and the Tetsuwan team on being part of Anthropic’s Model Hardware Standard research preview. This is an exciting step toward making experimentation more accessible, reproducible, and programmable.

Read Anthropic’s announcement.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Congratulations to OneLot on being named to Forbes Asia’s 100 to Watch list.

OneLot is building financial infrastructure for used-car dealers in the Philippines. The company provides working capital loans that help dealers expand inventory, paired with a dealership management system for operations like inventory tracking, marketing tools, and market insights.

The market need is straightforward: used-car dealers often need capital before they can grow. Inventory drives sales, but buying inventory requires financing, and many small and mid-sized dealers do not have access to flexible credit products built around how their businesses actually operate. OneLot addresses that gap with credit lines secured by vehicles, using AI and in-person inspections to assess dealer inventory before extending financing.

That combination of lending and software is what makes the model interesting. Financing helps dealers buy more cars. Software helps them manage those cars more effectively. Over time, the operating layer can also create better visibility into dealer performance, inventory quality, and market demand.

Forbes Asia’s 100 to Watch list highlights rising startups and small businesses across Asia-Pacific, with companies selected across categories including enterprise technology, fintech, robotics, and other high-growth sectors. OneLot’s inclusion is a strong recognition of the team’s work bringing better financial tools to a fragmented and important market.

Congratulations to Harm-Julian Schumacher and the OneLot team on the recognition. We’re excited to see the company continue building for dealers across the Philippines.

Read the Manila Bulletin piece

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

We invested in Starcloud because the team is working on one of the most important infrastructure questions of the AI era: where will the next generation of compute live?

AI is often discussed as a model, software, or chip story. But the more it scales, the more it becomes a physical infrastructure story. Data centers need power. They need cooling. They need land, permitting, interconnection, supply chains, and massive capital deployment. The constraints are not theoretical; they are already shaping where and how AI systems can be built.

Starcloud is taking a very different approach: orbital data centers.

The company is developing compute infrastructure in space, where satellites can use continuous solar power and the thermal environment of orbit to support AI workloads beyond the limits of terrestrial facilities. That idea sounds futuristic until you look at the direction of AI demand. If compute keeps becoming one of the defining scarce resources of the next decade, then expanding the geography of compute is not a novelty. It is infrastructure.

The team is already proving pieces of the system. TechCrunch notes that Starcloud is the only company it knows of currently operating an Nvidia H100 terrestrial data center GPU in orbit, and the first to train a model using it. That experience is informing Nvidia’s work on a purpose-built space GPU, the Vera Rubin Space-1 chip.

The company is also thinking beyond one spacecraft. TechCrunch reports that Starcloud has requested permission from the FCC to operate 88,000 spacecraft and is working to secure future launch capacity as access to space becomes more constrained. Near term, the company is focused on launching two Starcloud-2 satellites in 2027, each designed for 8 kW compute and intended to perform orbital inference tasks for customers including U.S. government agencies. Longer term, Starcloud is building toward a much larger orbital inference layer.

This is exactly the kind of deep tech company we are excited to back: technically ambitious, infrastructure-defining, and built around a constraint that is only becoming more important. Orbital data centers sit at the intersection of space, AI, energy, chips, launch, and manufacturing. It is a hard problem, but it is also a category-defining one.

Congratulations to Philip Johnston, Ezra Feilden, Adi Oltean, and the entire Starcloud team on a major financing milestone and an even bigger vision for the future of compute.

Read the full TechCrunch article.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Fishwife’s new limited-time partnership with Sweetgreen brings the brand into one of the most visible fast-casual food routines in the country. The collaboration includes two menu items: the Fishwife Summer Niçoise, made with Spanish lemon albacore tuna, soft egg, summer vegetables, and lemon-tarragon vinaigrette; and Omega Glow, a rice bowl with miso-glazed salmon and Sweetgreen’s new miso sesame ginger dressing.

Food Network’s review gave the clearest read on why the collaboration works. The writer called the Fishwife Summer Niçoise the best and most sophisticated salad they had ever had from Sweetgreen, praising the lemon-tarragon dressing and Mediterranean feel of the bowl. Their only real ask: make it permanent.

That response matters because Fishwife has never been just about canned seafood as a commodity. The brand has helped reframe tinned fish as colorful, design-forward, premium, and culturally current. It is the kind of category work that turns a shelf-stable product into something people talk about, gift, photograph, and build meals around.

Sweetgreen is a natural next stage for that story. The collaboration takes the Fishwife brand out of the pantry and into an everyday lunch decision, showing how tinned fish can fit into the habits of customers who may not have thought of it as part of their regular rotation.

It is also a reminder that consumer-category shifts often show up first as taste, design, and culture before they show up as scale. Fishwife made tinned fish feel modern and desirable; Sweetgreen gives that movement a mainstream, highly visible format.

Congrats to Becca Millstein and the Fishwife team on a partnership that captures where the category is headed: premium, playful, and increasingly part of everyday food culture.

Read the Food Network review.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

Nearshoring is often framed as a logistics story: shorter routes, lower geopolitical exposure, faster turnaround, and closer customer relationships. But in practice, one of the biggest barriers is much more basic: buyers need to know what factories can actually make.

In a Mexico Business News interview, Yumari’s Andrés Díaz lays out the core challenge. China’s manufacturing ecosystem has spent decades digitizing factory offerings through platforms like Alibaba, Global Sources, and Made-in-China. In Latin America, no equivalent data layer has existed at scale. The region has technically sophisticated factories, but structured information on capacity, materials, quality levels, pricing ranges, timelines, compliance, and specialization is fragmented or missing.

That missing infrastructure makes it difficult for global buyers to move programs from Asia into the Americas, even when the underlying manufacturing capability is already there.

Yumari is addressing that gap with AI agents that gather and structure operational data from both sides of the market. On the factory side, those agents help capture the real context behind production claims: available capacity, material constraints, quality standards, shift schedules, delays, logistics steps, and price targets. On the buyer side, agents identify exact procurement needs, from product specifications and volumes to delivery timelines.

The result is a more precise model of what regional factories can execute, when, and under what conditions. That matters because nearshoring only works if buyers can trust the visibility behind the match. Proximity alone is not enough; the market needs a reliable way to map demand to real manufacturing capacity.

The interview also highlights why this shift is timely. As brands face greater pressure around supply chain resilience, sustainability, labor standards, and tariff exposure, Latin America has a compelling opportunity to become a more competitive manufacturing hub. Yumari’s platform helps make that opportunity legible by digitizing the specialized capabilities that already exist across the region.

Congrats to Andrés Díaz and the Yumari team on the Mexico Business News feature and on building critical infrastructure for the next chapter of regional manufacturing.

Read the full Mexico Business News interview.

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.

It is an exciting moment for Eudia, which recently announced a collaboration with Microsoft to bring enterprise intelligence and specialized AI agents directly into the tools where knowledge work already happens.

Eudia is integrating across the Microsoft 365 ecosystem, including Word, Outlook, SharePoint, and OneDrive, while building on Microsoft Azure infrastructure. The collaboration also includes a joint go-to-market motion with Microsoft’s enterprise sales organization and plans for Eudia to become available through the Microsoft Marketplace.

For us, the announcement is particularly interesting because it reflects an important evolution in how enterprises are beginning to adopt agentic AI.

The opportunity is not simply to automate repetitive tasks. The much larger opportunity is to scale the judgment, expertise, and institutional knowledge of an organization’s best operators and make that intelligence available to the broader organization.

That is the idea behind Eudia’s Expert Digital Twins and specialized AI agents. Rather than functioning as generic assistants, these systems are designed to capture how experts within an organization think and operate, then make that knowledge accessible across teams and workflows.

Eudia initially built deeply within the legal function, where institutional knowledge, judgment, and context are particularly important. But the same intelligence can increasingly extend into the business teams legal supports, including sales, procurement, marketing, and finance.

Embedding that intelligence directly into Microsoft 365 is an important part of the equation. Enterprise AI becomes significantly more useful when employees do not need to leave their existing workflows to access it. Instead, the intelligence can appear inside the documents, emails, files, and collaboration environments where work is already taking place.

Eudia is already working with major enterprises including ServiceNow, Cargill, Toshiba, Ecolab, and Bayer, with its technology being used within Microsoft 365 environments.

The Microsoft collaboration provides another meaningful distribution channel for Eudia as enterprises increasingly move from experimenting with general-purpose AI toward deploying specialized agents that understand their organizations, workflows, and institutional knowledge.

We continue to believe that some of the most valuable enterprise AI companies will not simply replace individual tasks. They will create a new intelligence layer across organizations, enabling expertise that was previously concentrated in a small number of people to become available much more broadly.

Big congratulations to Omar Haroun, Ashish Agrawal, and the entire Eudia team on this milestone.

Read more: Eudia Announces Collaboration with Microsoft

Interested in investing alongside Everywhere Ventures? Learn more about joining our investor community here.