Technology
Lightfield, a customer relationship management platform built entirely around artificial intelligence, officially launched to the public this week after a year of quiet development — a bold pivot by a startup that once had 20 million users and $43 million in the bank building something completely different.
The San Francisco-based company is positioning itself as a fundamental reimagining of how businesses track and manage customer relationships, abandoning the manual data entry that has defined CRMs for decades in favor of a system that automatically captures, organizes, and acts on customer interactions. With more than 100 early customers already using the platform daily — over half spending more than an hour per day in the system — Lightfield is a direct challenge to the legacy business models of Salesforce and HubSpot, both of which generate billions in annual revenue.
“The CRM, categorically, is perhaps the most complex and lowest satisfaction piece of software on Earth,” said Keith Peiris, Lightfield’s co-founder and CEO, in an exclusive interview with VentureBeat. “CRM companies have tens of millions of users, and you’d be hard-pressed to find a single one who actually loves the product. That problem is our opportunity.”
The general availability announcement marks an unusual inflection point in enterprise software: a company betting that large language models have advanced enough to replace structured databases as the foundation of business-critical systems. It’s a wager that has attracted backing from Coatue Management, which led the company’s Series A when it was still building presentation software under the name Tome.
How Tome’s founders abandoned 20 million users to build a CRM from scratch
The story behind Lightfield’s creation reflects both conviction and pragmatism. Tome had achieved significant viral success as an AI-powered presentation platform, gaining millions of users who appreciated its visual design and ease of use. But Peiris said the team concluded that building lasting differentiation in the general-purpose presentation market would prove difficult, even with a working product and real user traction.
“Tome went viral as an AI slides product, and it was visually delightful and easy to use—the first real generative AI-based presentation platform,” Peiris explained. “But, the more people used it, the more I realized that to really help people communicate something—anything—we needed more context.”
That realization led to a fundamental rethinking. The team observed that the most effective communication requires deep understanding of relationships, company dynamics, and ongoing conversations — context that exists most richly in sales and customer-facing roles. Rather than building a horizontal tool for everyone, they decided to build vertically for go-to-market teams.
“We chose this lane, ‘sales,’ because so many people in these roles used Tome, and it seemed like the most logical place to go vertical,” Peiris said. The team reduced headcount to a core group of engineers and spent a year building in stealth.
Dan Rose, a partner at Coatue who led the original investment in Tome, said the pivot validated his conviction in the founding team. “It takes real guts to pivot, and even more so when the original product is working,” Rose said. “They shrunk the team down to a core group of engineers and got to work building Lightfield. This was not an easy product to build, it is extremely complex under the hood.”
Why Lightfield stores complete conversations instead of forcing data into fields
What distinguishes Lightfield from traditional CRMs is architectural, not cosmetic. While Salesforce, HubSpot, and their competitors require users to define rigid data schemas upfront — dropdown menus, custom fields, checkbox categories — and then manually populate those fields after every interaction, Lightfield stores the complete, unstructured record of what customers actually say and do.
“Traditional CRMs force every interaction through predefined fields — they’re compressing rich, nuanced customer conversations into structured database entries,” Peiris said. “We store customer data in its raw, lossless form. That means we’re capturing significantly more detail and context than a traditional CRM ever could.”
In practice, this means the system automatically records and transcribes sales calls, ingests emails, monitors product usage, and maintains what the company calls a “relationship timeline” — a complete chronological record of every touchpoint between a company and its customers. AI models then extract structured information from this raw data on demand, allowing companies to reorganize their data model without manual rework.
“If you realize you need different fields or want to reorganize your schema entirely, the system can remap and refill itself automatically,” Peiris explained. “You’re not locked into decisions you made on day one when you barely understood your sales process.”
The system also generates meeting preparation briefs, drafts follow-up emails based on conversation context, and can be queried in natural language — capabilities that represent a departure from the passive database model that has defined CRMs since the category’s inception in the 1980s.
Sales teams report reviving dead deals and cutting response times from months to days
Customer testimonials suggest the automation delivers measurable impact, particularly for small teams without dedicated sales operations staff. Tyler Postle, co-founder of Voker.ai, said Lightfield’s AI agent helped him revive more than 40 stalled opportunities in a single two-hour session — leads he had neglected for six months while using HubSpot.
“Within 2 days, 10 of those were revived and became active opps that moved to poc,” Postle said. “The problem was, instead of being a tool of action and autotracking—HubSpot was a tool where I had to do the work to record customer convos. Using HubSpot I was a data hygienist. Using Lighfield, I’m a closer.”
Postle reported that his response times to prospects improved from weeks or months to one or two days, a change noticeable enough that customers commented on it. “Our prospects and customers have even noticed it,” he said.
Radu Spineanu, co-founder of Humble Ops, highlighted a specific feature that addresses what he views as the primary cause of lost deals: simple neglect. “The killer feature is asking ‘who haven’t I followed up with?'” Spineanu said. “Most deals die from neglect, not rejection. Lightfield catches these dropped threads and can draft and send the follow-up immediately. That’s prevented at least three deals from going cold this quarter.”
Spineanu had evaluated competing modern CRMs including Attio and Clay before selecting Lightfield, dismissing Salesforce and HubSpot as “built for a different era.” He said those platforms assume companies have dedicated operations teams to configure workflows and maintain data quality — resources most early-stage companies lack.
Why Y Combinator startups are rejecting Salesforce and starting with AI-native tools
Peiris claims that the current batch of Y Combinator startups — widely viewed as a bellwether for early-stage company behavior — have largely rejected both Salesforce and HubSpot. “If you were to poll a random sampling of current YC startups and ask whether they’re using Salesforce or HubSpot, the overwhelming answer would be ‘no,'” he said. “Salesforce is too expensive, too complex to set up, and frankly doesn’t do enough to justify the investment for an early-stage company.”
According to Peiris, most startups begin with spreadsheets and eventually graduate to a first CRM — a transition point where Lightfield aims to intercede. “Increasingly, they’re choosing Lightfield instead and skipping that intermediate step entirely,” he said.
This represents a familiar pattern in enterprise software disruption: a new generation of companies forming habits around different tools, creating an opening for challengers to establish themselves before businesses grow large enough to face pressure toward industry-standard platforms.
Rose, the Coatue partner, sees Lightfield’s strategy as deliberately targeting this window. “Our strategy is to build quickly and grow alongside our best customers, essentially becoming the Salesforce for this new generation of companies,” Rose said, paraphrasing the company’s approach. “We’re there at the beginning when they’re forming their processes, and we scale with them as they grow.”
Can Salesforce and HubSpot retrofit their legacy systems for AI, or is the architecture too old?
Both Salesforce and HubSpot have announced AI features in recent quarters, adding capabilities like conversation intelligence and automated data entry to their existing platforms. The question facing Lightfield is whether established vendors can incorporate similar capabilities—leveraging their existing customer bases and integrations — or whether fundamental architectural differences create a genuine moat.
Peiris argues the latter. “The fundamental difference is in how we store data,” he said. “Because we have access to that complete context, the analysis we provide and the work we generate tends to be substantially higher quality than tools built on top of traditional database structures.”
Existing conversation intelligence tools like Gong and Revenue.io, which analyze sales calls and provide coaching insights, already serve similar functions but require Salesforce instances to operate. Peiris said Lightfield’s advantage comes from unifying the entire data model rather than layering analysis on top of fragmented systems.
“We have a more complete picture of each customer because we integrate company knowledge, communication sync, product analytics, and full CRM detail all in one place,” he said. “That unified context means the work being generated in Lightfield—whether it’s analysis, follow-ups, or insights—tends to be significantly higher quality.”
The privacy and accuracy concerns that come with AI-automated customer interactions
The architecture creates obvious risks. Storing complete conversation histories raises privacy concerns, and relying on large language models to extract and interpret information introduces the possibility of errors—what AI researchers call hallucinations.
Peiris acknowledged both issues directly. On privacy, the company maintains that call recording follows standard practices, with visible notifications that recording is in progress, and that storing sales correspondence mirrors what CRM vendors have done for decades. The company has achieved SOC 2 Type I certification and is pursuing both SOC 2 Type II and HIPAA compliance. “We don’t train models on customer data, period,” Peiris said.
On accuracy, he was similarly forthright. “Of course it happens,” Peiris said when asked about misinterpretations. “It’s impossible to completely eliminate hallucinations when working with large language models.”
The company’s approach is to require human approval before sending customer communications or updating critical fields — positioning the system as augmentation rather than full automation. “We’re building a tool that amplifies human judgment, not one that pretends to replace it entirely,” Peiris said.
This is a more cautious stance than some AI-native software companies have taken, reflecting both technical realism about current model capabilities and potential liability concerns around customer-facing mistakes.
How Lightfield plans to consolidate ten different sales tools into one platform
Lightfield’s pricing strategy reflects a broader thesis about enterprise software economics. Rather than charging per-seat fees for a point solution, the company is positioning itself as a consolidated platform that can replace multiple specialized tools — sales engagement platforms, conversation intelligence systems, meeting assistants, and the CRM itself.
“The real problem is that running a modern go-to-market function requires cobbling together 10 different independent point solutions,” Peiris said. “When you pay for 10 separate seat licenses, you’re essentially paying 10 different companies to solve the same foundational problems over and over again.”
The company operates primarily through self-service signup rather than enterprise sales teams, which Peiris argues allows for lower pricing while maintaining margins. This is a common playbook among modern SaaS companies but represents a fundamental difference from Salesforce’s model, which relies heavily on direct sales and customer success teams.
Whether this approach can support a sustainable business at scale remains unproven. The company’s current customer base skews heavily toward early-stage startups—more than 100 Y Combinator companies, according to the company — a segment with limited budgets and high failure rates.
Rose views this as a deliberate strategy rather than a limitation. “Many startups that survive do so because they have strong fundamentals,” he said, explaining the company’s thesis. “The reality is that many startups scale extraordinarily fast — they go from 10 people to enterprise-sized companies in just a few years.”
The bet is that Lightfield becomes the system of record for a cohort of fast-growing companies, eventually creating an installed base comparable to how Salesforce established itself decades ago. Whether AI capabilities alone provide sufficient differentiation to execute that strategy—or whether incumbents can adapt quickly enough to defend their positions—will likely determine the company’s trajectory.
The real test: whether sales teams will trust AI enough to let it run their business
The company has outlined several areas for expansion, including an open platform for workflows and webhooks that would allow third-party integrations. Early customers have specifically requested connections with tools like Apollo for prospecting and Slack for team communication — gaps that Postle, the Voker.ai founder, acknowledged but dismissed as temporary.
“The fact that HS and Salesforce have these integrations already isn’t a moat,” Postle said. “HS and Salesforce are going to lose to lightfield because they aren’t AI native, no matter how much they try to pretend to be.”
Rose highlighted an unusual use case that emerged during Lightfield’s own development: the company’s product team used the CRM itself to analyze customer conversations and identify feature requests. “In this sense, Lightfield more than just a sales database, it’s a customer intelligence layer,” Rose said.
This suggests potential applications beyond traditional sales workflows, positioning the system as infrastructure for any function that requires understanding customer needs—product development, customer success, even marketing strategy.
For now, the company is focused on proving the core value proposition with early-stage companies. But the broader question Lightfield raises extends beyond CRM software specifically: whether AI capabilities have advanced sufficiently to replace structured databases as the foundation of enterprise systems, or whether the current generation of large language models remains too unreliable for business-critical functions.
The answer will likely emerge not from technical benchmarks but from customer behavior—whether sales teams actually trust AI-generated insights enough to base decisions on them, and whether the efficiency gains justify the inherent unpredictability of working with systems that approximate rather than calculate.
Lightfield is betting that the trade-off has already shifted in favor of approximation, at least for the millions of salespeople who currently view their CRM as an obstacle rather than an asset. Whether that bet proves correct will help define the next generation of enterprise software.