As in every industry, Excel is the workhorse financial professionals have relied on to handle calculations of arbitrary levels of complexity.  In fund accounting, it is typical for it to be employed twice: once by third-party fund administrators — relied upon by some firms for most finance operations — and again by internal teams to verify their work. But for all its strengths, Excel was not intended to serve as a transactional database, and its very flexibility allows users to create calculation logic too complicated to parse or debug.

Thus, as the industry scales, the cracks in Excel are widening: Spreadsheets multiply, errors compound, and firms risk institutional knowledge walking out the door with each departing employee.  Fund accountants spend days untangling or rebuilding models, reconciliations drag on between admins and teams, and GPs sometimes remain one step removed from the calculations that matter most.

Maybern*’s founders started with a straightforward “Excel to software” premise. A platform that generalized correctly across the breadth of financial “primitive” concepts used by firms would enable them to define their calculation logic lucidly with proper transactional histories, permissions, and audit trails in a single and dynamic performance book-of-record. Moreover, this logic would be simple to execute, and calculations that can take days to prepare could be handled in minutes.

Achieving this simplicity has been no small feat, especially given the diversity of limited-partner agreements (LPAs), side letters, and rounding conventions that must be correctly applied in each calculation. Instantiating this context precisely enough to deliver precision down to the penny requires deep understanding of the domain, an expert delivery methodology, and a calculation engine capable of representing all the nuances in play. The stakes are higher than just efficiency: Clean audits and avoidance of restatements are essential to the LP trust and regulator approval.

From our first conversation with co-founders Ross Mechanic and Ashwin Raghu more than two years ago, it was clear they built Maybern with this reality in mind. Engineers by training, they had lived the problem themselves at Cadre, a real estate investment platform, and immersed themselves so deeply in the mechanics of fund operations that, as one customer put it, “they should have honorary degrees in fund accounting.”

That fluency shows up in the product, a canonical data model for fund accounting that is built to serve as the system of record for every calculation. Each time a fund is onboarded, Maybern ingests its LPAs, side letters, and historical calculations, then abstracts them into reusable primitives: fee rules, waterfall tiers, rounding conventions, and more. These primitives form the foundation of a library that grows to capture the quirks and edge cases of the industry.

Over time, we think this library becomes something larger: a living alphabet of fund economics, powering a scalable system that makes even the most complex fund structures simple to model, audit, and trust.

In just 18 months since launch, Maybern is already partnering with some of the largest funds across the alternatives landscape. CFOs describe how Maybern provides them leverage as operational complexity grows — ensuring accuracy, reducing latency in critical calculations, and delivering both tighter control and greater transparency. For internal fund accounting teams, Maybern is unlocking time savings and beginning to enable more-sophisticated forms of reporting and scenario modeling. The platform is also laying the foundation for AI-driven LPA ingestion and auditability, providing not just results but clear explanations of every calculation.

Battery is proud to partner with the Maybern team on this journey and to announce that we are leading the company’s $50 million Series B, alongside our peers at Primary, Human Capital, and MetaProp Ventures.

The post Maybern: The Performance Book of Record for Fund Accounting appeared first on Battery Ventures.

Over the last year, that focus has deepened as we’ve studied how AI is impacting selling. We launched our Battery CRO Dinner Series, bringing together more than a hundred CROs to exchange best practices, and hosted another 125 CROs and revenue leaders at our 2025 Battery Kick-Off sales summit, organized by our Operating Partner Bill Binch, a former CRO himself. Across these conversations, one theme came through clearly:

Today’s buyers expect immediacy, precision, and personalization at a scale humans alone can’t deliver.

That theme is core to our newest investment. We’re thrilled to announce Battery is leading the Series A financing of 1mind, the company behind a new class of AI-powered GTM “Superhumans.”

Meet the GTM Superhuman

1mind builds photorealistic, intelligent AI teammates, or Superhumans, that engage, qualify, demo, and convert customers across web, in-app, email, and even live Zoom interactions. These agents have a face, a voice, and a deep understanding of their respective companies’ products and playbooks.

Modern buyers expect fast, self-serve, personalized experiences, not forms and multi-day handoffs. 1mind meets buyers on their terms, offering instant, relevant conversations any time, including 2 a.m. on a holiday. Superhumans remember every prior interaction: They pull the right case study instantly, handle objections with accuracy and empathy, and don’t exaggerate to get a deal over the line.

Superhumans take the first touch in parts of the funnel where no SDR ever reached: late-night traffic, free product tiers, and high-volume SMB segments that were previously too costly to serve. But many customers quickly find sales-adjacent use cases, like customer support and success. After all, a Superhuman’s knowledge isn’t limited to why a prospect should buy a product. They can also help customers realize maximum value post-purchase.

At HubSpot, 1mind’s Superhuman “Fiona” was deployed on its free SMB product – a part of the business with no human engagement. Fiona produced a +75% lift in free-trial signups, +25% lift in closed/won, and an 88% engagement rate. After seeing those results, HubSpot expanded Fiona’s footprint fivefold.

Across enterprise customers spanning B2B software and logistics, healthcare, and the public sector, 1mind’s Superhumans consistently outperform chatbots and traditional SDR benchmarks, cutting sales cycles from months to weeks.

You can talk to 1mind’s Superhuman on their site, and below are a few other customer Superhumans you can test out:

• Fiona, Hubspot’s Superhuman
• Bobbi, BostonDynamic’s Superhuman
• Quincy, Quickbase’s Superhuman

We’re teaming up again

This investment also marks a reunion. Founder & CEO Amanda Kahlow has spent her career reimagining how companies connect with buyers. She previously founded 6sense*, which pioneered account-based marketing and helped usher in a data-driven sales era. While building 6sense, she saw firsthand that finding buyers wasn’t the hard part – closing them efficiently was. That insight became the seed for 1mind. We first partnered with Amanda 12 years ago on the 6sense journey and we’re thrilled to be working together again.

To bring 1mind to life, Amanda partnered with Sachin Bhat, a former head of engineering at Rippling and a repeat founder himself. Together, we feel, they’ve assembled a team with rare depth – marketers who understand the GTM motion and engineers who know how to build agentic AI that operates in the real world.

Looking ahead

Today, 1mind’s Superhumans are driving millions in additional revenue across dozens of enterprise customers, with expansion into customer success and onboarding already underway. Tomorrow, we believe they’ll form the foundation of an entirely new GTM operating system – one where every company can deploy a personalized, perfectly trained digital teammate to engage buyers and accelerate growth across the entire customer lifecycle.

We’re proud to lead 1mind’s $30M Series A, partnering again with Amanda and the entire 1mind team as they build the future of intelligent, human-like GTM engagement.

The post Our Investment in 1mind: The Future of GTM Superhumans appeared first on Battery Ventures.

For decades, selling into the federal government has been a notoriously complex and costly endeavor. From combing the sam.gov website, to corralling subject-matter experts to respond to RFPs, to running costly analytics exercises, selling to the government often carries more risk than potential benefit.

Procurement Sciences has flipped the process on its head. Initially, the AI-powered platform compliantly exposed best-in-class models for contractors to streamline proposal writing. Today, the platform continuously scans all new revenue opportunities to determine potential fits for government vendors; acts as a pipeline and work-list management tool; and provides automated compliance reviews to ensure the highest probability of winning government business. The product enables existing government contractors to drive more revenue and operate more efficiently while making it easier for new entrants to spin up a profitable government contracting business more quickly. To date, the company has supported over $2 billion in won contracts. Customers have said:

“Awarded AI allows my team to ‘stay in the game’ despite staff reductions, building quality proposals with less manual effort.”

“I’ve seen a 75% productivity increase on capabilities matrices, reducing a full day’s work to two hours.”

“Awarded AI was absolutely crucial to a complex international proposal with 94 separate RFP documents. It would have been impossible without Awarded AI.”

Since our investment, CEO Christian Ferreira has grown Procurement Sciences from seven full time employees to a team of more than 50. The addition of Greg Larson as CTO –previously head of engineering at Jasper AI and Divvy – is helping the company grow and attract top AI talent. As the company will be opening offices in the Washington, D.C. and Salt Lake City areas, we highly encourage talent in both markets to explore the more than 25 open roles at the company here.

As Christian and his team aim to deliver their platform to every business looking to sell to the government, they’re excited by the additional opportunities of expanding into the SLED (state, local, education) market, building additional product for delivery of services, and establishing themselves as the AI partner for all government contractors. We look forward to continuing to support the team, and if you know someone who sells to the government, we’d be happy to connect them with Procurement Sciences.

The post Making Government Procurement Cutting-Edge: Our Additional Investment in Procurement Sciences appeared first on Battery Ventures.

The speed of execution has never been higher, but the craft of building well is being rewritten in real time. Earlier this month at Battery’s Future of Product dinner, we brought together a dozen product leaders to discuss how the evolution of the PM role is unfolding across startups and enterprises alike. Below are seven shifts that surfaced:

1. From writing-first to building-first product culture

Many large incumbents are trying to unlearn their traditional “writing-heavy” cultures built on PRDs and structured rituals. To move at startup speed, they’re adopting a build-first, show-don’t-tell culture. PMs are now expected to prototype, validate and iterate ideas using tools like V0, Lovable, Cursor—not just write documents.

Gamma’s CEO Grant Lee recently shared an inside look into how his product team operates on Lenny’s Podcast—what once took weeks of planning can now happen before lunch. That pace changes everything about how PMs operate.

2. Engineering is no longer the bottleneck

For years, engineering bandwidth was the limiting factor in product velocity. With AI-assisted coding tools, that bottleneck has shifted to surface a new one: clarity. Engineers can now build and ship features faster than PMs can plan, and in some cases, faster than users can absorb.

As one product leader noted, customers are now asking them to slow down feature launches, frustrated that the UI seems to change every week. The greatest risk for product teams today isn’t moving too slowly; it’s moving fast in the wrong direction.

3. Everyone’s a product builder

Figma’s CEO Dylan Field recently noted, “We’re all product builders, and some of us are specialized in our particular area.” As roles overlap, the traditional Engineering–Product–Design (EPD) boundaries are dissolving. PMs are designing and prototyping independently; designers are thinking more deeply about what and why to build; and engineers are engaging directly in user discovery and validation. Design, in many ways, has become the connective tissue—translating speed into coherence.

4. Product strategy is expanding beyond the build

AI startups are finding it harder than ever to rise above a sea of sameness. As differentiation becomes tougher, PMs are being pulled deeper into pricing, packaging and positioning—areas historically owned by go-to-market teams.

Given their proximity to customer value, workflow design and user outcomes, PMs often have the best context for deciding how a product should be priced, how value should be communicated and what users will actually pay for. In many AI-native companies, like Unify*, product marketing now reports directly to product—a reflection of how tightly product strategy and narrative are intertwined.

5. FDEs and agent PMs are turning AI into ROI

One of the more interesting developments that surfaced is how startups are actually helping enterprises build with AI.

In this AI wave, everyone from startups to large enterprises is an early adopter. Many large enterprises have built AI “centers of excellence” to map and prioritize use cases, but they often lack the technical depth to actually build and deploy agentic applications.

This gap has led to the rise of the FDE (Forward Deployed Engineer) and Agent PM models, in which startups like Ramp embed dedicated pods inside enterprise customers to take a use case from definition to prototype to production and ROI. It’s a services-heavy, but ROI-driven, approach that delivers tangible outcomes fast. Over time, these partnerships are expected to evolve into more productized, self-serve models as enterprise maturity increases.

6. Redefining product success metrics for agentic apps

Traditional metrics like DAU, MAU and session time were built for UI-based products, but agentic apps often have no UI and aim to complete work autonomously. Success is shifting toward outcome-based measures such as workflow completion rates or automation accuracy. In short, “time spent” is giving way to “work done,” marking a fundamental shift in how product success and user value are defined.

One product leader noted that following the launch of AI agents, overall weekly active users declined, but engagement among power users has surged. These power users are often Admins who frequently log in to fine-tune configurations, audit agent logs and build new agentic workflows.

7. AI Evals are becoming part of PM discipline

Because AI systems are inherently non-deterministic, evaluations—once an engineering concern—is now a core part of the PM’s role. PMs are designing evaluation frameworks that measure how well models or agents perform against business goals, setting thresholds for accuracy, reliability and quality.

This “AI evaluation” capability is emerging as a new pillar of the PM’s craft—one that blends experimentation, analytics and product judgment to ensure model behavior aligns with intended outcomes.

The next frontier of product management

As AI accelerates every part of the software development cycle, roles are converging in service of one shared goal: building products that truly work for users. The PMs who thrive in this new era won’t measure their impact in features shipped or issues resolved, but in the clarity, coherence and taste they bring to what’s built—and what’s left out.

The post The Role of a Product Manager in the Age of AI appeared first on Battery Ventures.