Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience.
Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide.
We're hiring a Head of Professional Services to build and lead Vercel's global professional services organization, composed of our Technical Account Management (TAM) and Forward Deployed Engineering (FDE) functions. These functions embed directly with our most strategic customers to design, build, and ship production applications on Vercel using Vercel's core platform, Next.js and our agent stack, taking projects from first prototype through production rollout. You will define how this function operates: the engagement model, the delivery methodology, the commercial structure, and the team itself.
You will report directly to our Global Head of Field Engineering, and partner closely with GTM and Product Leadership to run a business function that turns our largest customers' complex implementations into production deployments, drives adoption and expansion, and feeds what you learn in the field back into our product roadmap.
This is a hybrid position based in San Francisco, Austin or New York, with 3 days per week (Monday, Tuesday, Friday) required in office.
The San Francisco, CA base pay range for this role is $300,000 - $370,000 plus annual bonus. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process.
Vercel runs a cloud platform for software teams to build, deploy and operate web applications and AI agents without managing their own servers. Its tools connect code repositories to automated builds and give teams separate environments for testing changes before publishing them. The platform combines application hosting, global content delivery and security controls, while its AI Gateway connects applications to models from several providers. Developers start on a free plan, and professional teams pay subscriptions, metered infrastructure charges or negotiated enterprise contracts. Vercel was founded in 2015 and is headquartered in San Francisco, California, United States.
Company Size
501-1,000
Company Stage
Series F
Total Funding
$863M
Headquarters
San Francisco, California
Founded
2015
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AI infrastructure heats up as models get more efficient. AI efficiency is exploding while security demands intensify, giving developers both more powerful tools and greater responsibility. From Microsoft's new models to critical vulnerabilities in popular libraries, the infrastructure Revolter AB depend on is being reshaped in real time. Today's tech news paints a picture of two counteracting forces in the developer world. On one hand, the AI industry is accelerating, with more efficient models and more accessible tools appearing constantly. On the other hand, security threats grow exponentially as more people and systems depend on these same tools. AI is getting faster and more efficient. Microsoft unveiled MAI-Base-2, a mixture-of-experts model that is 4 to 12 times more efficient than DeepSeek-V4-Pro in training. That sounds technical, but the meaning is concrete: for developers, this means you can train larger models for less money and in less time. Greater efficiency in AI infrastructure trickles down to everyone building AI features. Nvidia is pushing the same trend with Nemotron-Bolt, a 74-billion-parameter model that now fits on a standard 24GB laptop. This completely changes what's possible to build locally without relying on API calls. On-device inference stops being a futuristic vision and becomes a practical reality for developers who want low latency and better privacy. Meta also released an open-weight AI model optimized for Apple Silicon, meaning modern AI models can now run efficiently on consumer hardware. The shared trend here is unmistakable: AI is becoming less dependent on cloud infrastructure and more distributed to the edges. Platforms are democratizing development. Bluesky expanded Attie, its AI-powered tool for building websites and games without knowing code. It's a sign that artificial intelligence is starting to make it possible for non-developers to ship things that previously required specialized knowledge. For those of Revolter AB building at a professional level, this means competition from no-code solutions is only growing. Vercel introduced always-on workers, letting you run persistent backend compute without traditional serverless scaling. It's subtle but important, because it removes one of the biggest friction points in serverless architectures: everything constantly spinning up and down. This lets developers focus on real problems instead of infrastructure orchestration. RapidCanvas raised 29 million dollars for its platform that lets non-ML-experts build custom AI tools. This is a symptom that AI tooling is rapidly normalizing and abstracting away from pure machine learning toward becoming a business problem that gets solved quickly. Security holes are growing at the same pace as success. But this isn't just a success story. GitHub was targeted in a coordinated attack where hackers compromised around 1900 accounts and targeted 30000 repositories. It's a reminder that developer infrastructure is something attackers prioritize heavily, because a compromised GitHub account is a gateway into thousands of projects. Even worse was the LiteLLM CVE-2026-35029, a critical vulnerability that exposed API keys at scale. LiteLLM sits between your applications and multiple LLM providers, so a vulnerability here can potentially compromise your entire integration chain. This is classic supply-chain risk that developers need to take seriously. OpenAI also announced deprecation of the GPT-4o API with only three months notice instead of the promised six months. This says something about the dynamics in the AI market: vendors move fast and can change terms on short notice. For developers, it means you should build abstractions around your AI API calls so you don't get locked into a single vendor. Responsibility and transparency on the rise. Anthropic gave METR researchers employee-level access to evaluate Claude, signaling a willingness toward transparent security assessment. This is the opposite pole from the security holes Revolter AB saw earlier. This type of open scrutiny could become industry standard as AI systems become more critical. What ties today together is a simple truth: the more powerful AI infrastructure becomes, the more important security, transparency, and long-term architecture become. Speed shouldn't mean building houses on sand. This is part of Revolter's daily developer brief series.
Vercel adds free Nemotron 3.5 Lightning, with a prompt-retention warning. Vercel's free Nemotron 3.5 Lightning route is served by NVIDIA, which may retain prompts for model improvement. Check what you send. BuilderWithin Editorial Team BuilderWithin Vercel has added a free route to NVIDIA's Nemotron 3.5 Lightning through AI Gateway, its service for connecting apps and coding agents to models. NVIDIA serves the requests, and Vercel warns that the provider may retain prompts for model improvement. The model can work with tools and return structured data that an app can process. Vercel lists support for coding agents including Claude Code, Codex, and Cursor. The route's model name is nvidia/nemotron-3.5-lightning-free. An agent's prompt can include the code or other context it needs for the task. As with open-model access through Netlify, using a gateway still requires checking how the serving provider handles your data. Review what your agent sends and the provider's data terms before using this route with private code or customer information. A public example or invented data lets you try it without sending that private material. End of article BuilderWithin Weekly · Free If you found this useful, subscribe free. Get BuilderWithin Weekly in your inbox. Daily briefings publish on the site, and the weekly roundup pulls together what mattered. Unsubscribe any time. BuilderWithin Weekly · Free · No spam Get BuilderWithin Weekly in your inbox. One weekly roundup in your inbox. Daily briefings publish on the site for people who ship products. Unsubscribe any time · Weekly email, always free
Forgepoint raises $9.8M to help manufacturers quote faster and win more business. Forgepoint's AI Inside Sales Rep Frank helps manufacturers manage the entire RFQ-to-PO process using each shop's pricing rules and approval processes. October 14, 2026 09:00 ET | Source: Forgepoint New York, NY, Oct. 14, 2026 (GLOBE NEWSWIRE) - American manufacturers can often make a part faster than they can get a quote back to the customer asking for it. That mismatch is the result of decades of automation reaching the shop floor, but rarely the office around it. Forgepoint is tackling that with Frank, an AI Inside Sales Rep built for manufacturing. Frank works alongside quoting teams across the emails, documents, pricing logic, and systems they already use, helping them move from incoming request to customer-ready quote far faster. Forgepoint founders: Jack Strauss and Alex Pareto. Forgepoint today announced a $9.8 million seed round led by Primary Venture Partners and a16z speedrun, with angel investors from OpenAI, Brex, Vercel, and SoFi also participating. The funding will bring Frank to more manufacturers, deepen integrations with the systems they already use, and grow its world-class team. Frank analyzes part files, prepares pricing using each manufacturer's own cost model, routes engineering work for human review, and follows up with the customer after approval. Engineers, estimators, and salespeople keep the technical and commercial decision making, while Frank handles the administrative work between them. Forgepoint reached a seven-figure ARR within six months of Frank's launch. Hundreds of engineers, estimators, and sales reps now use Frank each week, and across customers monthly quote throughput is up an average of 30% while time-to-quote has fallen by more than 55%. From Silicon Valley to the American Heartland Before Forgepoint, CEO Jack Strauss spent his career in industrials at Northrop Grumman, Bain & Company's Advanced Manufacturing practice, and aerospace systems integrator Xwing. He kept seeing the same disconnect: manufacturers invested heavily in automating the shop floor, while everything before and after production still moved through email, spreadsheets, and aging enterprise systems. In early 2025, Strauss and co-founder Alex Pareto, formerly Head of Engineering for Brex's Spend Management organization, spent months touring plants across the Midwest, shadowing employees and studying their processes to find where the bottlenecks were and why. "In those early days, we simply wanted to learn, as if we had been hired to do the job ourselves. We knew that before we could solve the problem, we needed to understand it deeply" said Alex Pareto, Co-Founder and CTO. The two came away convinced that the frontier AI developing in front of them in Silicon Valley could remove many of the bottlenecks they were seeing on factory floors across the Midwest. Starting with Inside Sales Inside Sales was the natural starting point. It is directly tied to revenue: a shop's capacity to answer RFQs determines how much business it wins. Second, when it comes to winning jobs, speed matters now more than ever. A 2025 Gardner Intelligence study found that nearly 50% of manufacturers expect a vendor response within 24 hours. That is a hard target when the typical RFQ arrives as an email with several attachments and missing specifications. An estimator may need to contact the customer, find similar jobs, build a cost model, secure engineering approval, apply margins, enter pricing into the ERP, generate the quote, update the CRM, and manage follow-up. Expert judgment is critical for a few of those steps. The handoffs and data entry around them stretch the process from hours into days or weeks. "We kept meeting manufacturers with strong demand and capable teams, but their best people were spending hours on administrative work. Speed is a competitive advantage, so we built Frank to keep jobs moving and free skilled operators to focus on the decisions only they can make," said Jack Strauss, CEO and Co-Founder of Forgepoint. How Frank works Frank joins a manufacturer like a new employee. He gets an email address at the company's domain and learns the shop's operating procedures, pricing rules, and approval requirements. When an RFQ arrives, he reads the email and attachments, flags missing requirements, and contacts the buyer for clarification. He prepares the quote using the manufacturer's own cost model, surfaces similar historical jobs, and routes the draft to the right reviewer. After approval, he creates the final document in the company's template, sends it, manages follow-up, and records the correspondence. Because quoting and pricing look radically different from shop to shop, Forgepoint configures Frank around how each one already works. At a metal service center Frank matches material lists and cut lengths against the catalog, applies customer-specific pricing, and checks inventory. At a foundry he reviews part files, gathers missing drawing specifications from the buyer, and prepares a first draft for an engineer. Across injection molding, automation, fabrication, stamping, and machining, the day-to-day differs but the goal is the same: quote faster, quote more, and win more deals. Early customer results On average, Forgepoint customers increase monthly quote throughput by 30% and cut time-to-quote by more than 55%, giving skilled operators more time back in their day."Before Forgepoint, we were building quotes by hand and barely keeping up. Now, Frank drafts and I can approve it in two minutes. I'm spending less time entering data and more time serving my customers," said Chas Aday, Founder and CRO of SIP Manufacturing. As Frank works, it also preserves the emails, attachments and reasoning behind every decision, building a memory of how each shop prices work. That matters as veteran employees retire and manufacturers risk losing years of pricing logic, customer preferences and exceptions that live in their heads and inboxes. "A manufacturer's ability to grow depends on how much work its team can quote, plan, and manage," said Zach Fredericks, Principal at Primary Venture Partners. "Forgepoint is giving those teams more capacity, starting with quoting. We backed Jack and Alex because they understand the details of that work and have a clear ambition to bring the same approach across the entire manufacturing back office." Looking ahead Forgepoint's mission is to put best-in-class technology into the hands of manufacturers. That begins with quoting, where slow responses can mean lost business. Over time, the company plans to apply the same approach to other back office departments, helping manufacturers compete and grow without adding administrative headcount at the same rate. About Forgepoint Forgepoint builds AI coworkers designed to automate and streamline back-office workflows for manufacturers, starting with the quoting and sales process Forgepoint's flagship product is Frank, an AI Inside Sales Rep for manufacturers, that gathers requirements, builds accurate quotes, and responds before your competitors even open the RFQ.
AI Sales Rep startup Forgepoint raises $9.8M to help manufacturers quote faster. Pet Age Staff//October 14, 2026// American manufacturers can often make a part faster than they can get a quote back to the customer asking for it. That mismatch is the result of decades of automation reaching the shop floor, but rarely the office around it. Forgepoint is tackling that with Frank, an AI Inside Sales Rep built for manufacturing. Frank works alongside quoting teams across the emails, documents, pricing logic, and systems they already use, helping them move from incoming request to customer-ready quote far faster. Forgepoint recently announced a $9.8 million seed round led by Primary Venture Partners and a16z speedrun, with angel investors from OpenAI, Brex, Vercel, and SoFi also participating. The funding will bring Frank to more manufacturers, deepen integrations with the systems they already use, and grow its world-class team. Frank analyzes part files, prepares pricing using each manufacturer's own cost model, routes engineering work for human review, and follows up with the customer after approval. Engineers, estimators, and salespeople keep the technical and commercial decision making, while Frank handles the administrative work between them. Forgepoint reached a seven-figure ARR within six months of Frank's launch. Hundreds of engineers, estimators, and sales reps now use Frank each week, and across customers monthly quote throughput is up an average of 30% while time-to-quote has fallen by more than 55%. From Silicon Valley to the American Heartland Before Forgepoint, CEO Jack Strauss spent his career in industrials at Northrop Grumman, Bain & Company's Advanced Manufacturing practice, and aerospace systems integrator Xwing. He kept seeing the same disconnect: manufacturers invested heavily in automating the shop floor, while everything before and after production still moved through email, spreadsheets, and aging enterprise systems. In early 2025, Strauss and co-founder Alex Pareto, formerly Head of Engineering for Brex's Spend Management organization, spent months touring plants across the Midwest, shadowing employees and studying their processes to find where the bottlenecks were and why. "In those early days, we simply wanted to learn, as if we had been hired to do the job ourselves. We knew that before we could solve the problem, we needed to understand it deeply" said Alex Pareto, Co-Founder and CTO. The two came away convinced that the frontier AI developing in front of them in Silicon Valley could remove many of the bottlenecks they were seeing on factory floors across the Midwest. Starting with Inside Sales Inside Sales was the natural starting point. It is directly tied to revenue: a shop's capacity to answer RFQs determines how much business it wins. Second, when it comes to winning jobs, speed matters now more than ever. A 2025 Gardner Intelligence study found that nearly 50% of manufacturers expect a vendor response within 24 hours. That is a hard target when the typical RFQ arrives as an email with several attachments and missing specifications. An estimator may need to contact the customer, find similar jobs, build a cost model, secure engineering approval, apply margins, enter pricing into the ERP, generate the quote, update the CRM, and manage follow-up. Expert judgment is critical for a few of those steps. The handoffs and data entry around them stretch the process from hours into days or weeks. "We kept meeting manufacturers with strong demand and capable teams, but their best people were spending hours on administrative work. Speed is a competitive advantage, so we built Frank to keep jobs moving and free skilled operators to focus on the decisions only they can make," said Jack Strauss, CEO and Co-Founder of Forgepoint. How Frank works Frank joins a manufacturer like a new employee. He gets an email address at the company's domain and learns the shop's operating procedures, pricing rules, and approval requirements. When an RFQ arrives, he reads the email and attachments, flags missing requirements, and contacts the buyer for clarification. He prepares the quote using the manufacturer's own cost model, surfaces similar historical jobs, and routes the draft to the right reviewer. After approval, he creates the final document in the company's template, sends it, manages follow-up, and records the correspondence. Because quoting and pricing look radically different from shop to shop, Forgepoint configures Frank around how each one already works. At a metal service center Frank matches material lists and cut lengths against the catalog, applies customer-specific pricing, and checks inventory. At a foundry he reviews part files, gathers missing drawing specifications from the buyer, and prepares a first draft for an engineer. Across injection molding, automation, fabrication, stamping, and machining, the day-to-day differs but the goal is the same: quote faster, quote more, and win more deals. Early customer results On average, Forgepoint customers increase monthly quote throughput by 30% and cut time-to-quote by more than 55%, giving skilled operators more time back in their day."Before Forgepoint, we were building quotes by hand and barely keeping up. Now, Frank drafts and I can approve it in two minutes. I'm spending less time entering data and more time serving my customers," said Chas Aday, Founder and CRO of SIP Manufacturing. As Frank works, it also preserves the emails, attachments and reasoning behind every decision, building a memory of how each shop prices work. That matters as veteran employees retire and manufacturers risk losing years of pricing logic, customer preferences and exceptions that live in their heads and inboxes. "A manufacturer's ability to grow depends on how much work its team can quote, plan, and manage," said Zach Fredericks, Principal at Primary Venture Partners. "Forgepoint is giving those teams more capacity, starting with quoting. We backed Jack and Alex because they understand the details of that work and have a clear ambition to bring the same approach across the entire manufacturing back office."
Peec AI named fastest-growing AEO vendor by Ramp; expands North American footprint with New York office. Data from Ramp Rate reveals Peec AI quadrupled market adoption to 19% while accelerating ARR to $15M, with the second-highest adoption among enterprise companies (21%) and strong traction with growing marketing teams and agencies. October 13, 2026 12:33 ET | Source: Peec AI Berlin, BERLIN, Oct. 13, 2026 (GLOBE NEWSWIRE) - Peec AI, a leading all-in-one AI search visibility platform for Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), today announced it has been recognized as the fastest-growing vendor in the AEO category according to market spend data published by Ramp Rate. The company's market adoption rate grew from 4% in March 2025 to 19% in September 2026, driven by widespread adoption across enterprise organizations, mid-market businesses, growth marketing teams, and SEO agencies. During the same period, Peec AI accelerated its Annual Recurring Revenue (ARR) from $10M to $15M in just three months. "It's an honor to be recognized by Ramp Rate for our growth over this year. Ramp's data shows Peec AI is a top AEO vendor in enterprise adoption, with a 21% adoption rate among enterprise companies," stated Malte Landwehr, CPO and CMO of Peec AI. Peec AI ranks first in the AEO category in monthly adoption growth, at +0.84% per month. Peec AI also shows strong traction across mid-market (17%) and small-to-medium businesses (20%). Ramp Rate's dataset tracks corporate spend across more than 70,000 businesses. Enterprise-grade AI visibility, for teams of all sizes Throughout 2026, Peec AI expanded its platform with features built to enterprise standards, from brand perception analysis to log file integrations. Most features are available to teams of every size, whether they're on a self-serve plan or part of an enterprise deployment: * Comprehensive LLM ecosystem tracking: Monitors brand mentions at scale across major AI engines, with broad multi-model coverage of ChatGPT, Google AI Overviews, Google AI Mode, Microsoft Copilot, Gemini, Claude, Grok, Qwen, Deepseek, Mistral, Perplexity, and Meta Spark. * Brand perception and fact-checking: Analyzes AI answers to show how engines like ChatGPT, Gemini, AI Mode, and Claude describe a brand. Market, objections, and fact-checking diagnostics surface the attributes AI answers associate with a brand, identify the buying objections AI platforms repeat, and cross-reference AI-generated claims against company-supplied facts. * Actionable visibility KPIs: Peec AI provides granular reporting across core AI performance metrics, measured on every tracked AI answer, including visibility, sentiment, multi-model citation tracking, share of voice (SoV), and AI referrals. * Developer and agency integration: Provides programmatic API access to AI visibility data and a Model Context Protocol (MCP) integration for AI agent workflows like Claude Code, paired with customizable visualization tools like Power BI and Looker Studio. * Analytics and log file integrations: Peec AI integrates with Google Analytics and CDN log providers like Vercel, Cloudflare, AWS, and WordPress, connecting AI visibility to AI referral traffic and AI crawler activity. * Predictable scaling: Enterprise tiers include unlimited seats, SSO integration, multi-brand management, and multi-language tracking for international operations. On top of that, enterprise customers of Peec AI get a dedicated account manager and a named Customer Success Manager (CSM) alongside custom onboarding and prioritized support channels. Custom plans (starting around $499+/month) include direct phone support and named personnel to guide multi-market tracking architectures. Expanding footprint in North America Peec AI has opened a flagship U.S. office in New York City to support its expanding enterprise user base across North America. The hub will serve North American clients, agency partners, and strategic enterprise accounts. About Peec AI Peec AI helps marketing teams improve brand performance in AI search. More than 3,000 agencies and brands, including global enterprises, rely on Peec AI to understand where they stand, what shapes how they appear and get recommended, and where to act. The company combines original research with rigorous measurement of the markets, models, sources, competitors, and websites behind AI answers, helping teams separate meaningful signals from noise and make better-informed decisions about what to do next. Peec AI is one of Europe's fastest-growing AI software companies, surpassing $15M in ARR since launching in January 2025. Founded by Marius Meiners, Daniel Drabo, and Tobias Siwonia, Peec AI has raised $40M from investors including Singular, Antler, Combination VC, identity.vc, and S20. Learn more at peec.ai. About Ramp Rate Ramp Rate is a vendor directory powered by aggregated, anonymized data from Ramp's corporate card and bill pay platform, benchmarking corporate spend across billions of dollars and tens of thousands of businesses. Press Inquiries