Lightfield, a customer relationship management platform built entirely around artificial intelligence, officially launched to the general public this week after a 12 months of quiet development — a daring pivot by a startup that when had 20 million users and $43 million within the bank constructing 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 many years in favor of a system that robotically captures, organizes, and acts on customer interactions. With greater than 100 early customers already using the platform each day — over half spending greater than an hour per day within the system — Lightfield is a direct challenge to the legacy business models of Salesforce and HubSpot, each of which generate billions in annual revenue.
"The CRM, categorically, is probably probably 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 corporations have tens of tens of millions of users, and also you'd be hard-pressed to search out 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: an organization betting that enormous language models have advanced enough to exchange structured databases as the inspiration of business-critical systems. It's a wager that has attracted backing from Coatue Management, which led the corporate's Series A when it was still constructing presentation software under the name Tome.
How Tome's founders abandoned 20 million users to construct a CRM from scratch
The story behind Lightfield's creation reflects each conviction and pragmatism. Tome had achieved significant viral success as an AI-powered presentation platform, gaining tens of millions of users who appreciated its visual design and ease of use. But Peiris said the team concluded that constructing lasting differentiation within 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 straightforward to make use of—the primary real generative AI-based presentation platform," Peiris explained. "But, the more people used it, the more I spotted that to essentially help people communicate something—anything—we would have liked more context."
That realization led to a fundamental rethinking. The team observed that probably 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. Somewhat than constructing a horizontal tool for everybody, they decided to construct vertically for go-to-market teams.
"We selected this lane, 'sales,' because so many individuals in these roles used Tome, and it appeared like the most obvious place to go vertical," Peiris said. The team reduced headcount to a core group of engineers and spent a 12 months constructing in stealth.
Dan Rose, a senior advisor at Coatue who led the unique investment in Tome, said the pivot validated his conviction within the founding team. "It takes real guts to pivot, and much more so when the unique product is working," Rose said. "They shrunk the team right down to a core group of engineers and set to work constructing Lightfield. This was not a straightforward product to construct, it is amazingly complex under the hood."
Why Lightfield stores complete conversations as an alternative 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 — after which manually populate those fields after every interaction, Lightfield stores the whole, unstructured record of what customers actually say and do.
"Traditional CRMs force every interaction through predefined fields — they're compressing wealthy, nuanced customer conversations into structured database entries," Peiris said. "We store customer data in its raw, lossless form. Meaning we're capturing significantly more detail and context than a standard CRM ever could."
In practice, this implies the system robotically records and transcribes sales calls, ingests emails, monitors product usage, and maintains what the corporate calls a "relationship timeline" — a whole chronological record of each touchpoint between an organization and its customers. AI models then extract structured information from this raw data on demand, allowing corporations to reorganize their data model without manual rework.
"For those who realize you would like different fields or need to reorganize your schema entirely, the system can remap and refill itself robotically," Peiris explained. "You're not locked into decisions you made on day one whenever you barely understood your sales process."
The system also generates meeting preparation briefs, drafts follow-up emails based on conversation context, and might be queried in natural language — capabilities that represent a departure from the passive database model that has defined CRMs for the reason that category's inception within the Eighties.
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 greater than 40 stalled opportunities in a single two-hour session — leads he had neglected for six months while using HubSpot.
"Inside 2 days, 10 of those were revived and have become energetic opps that moved to poc," Postle said. "The issue was, as an alternative of being a tool of motion and autotracking—HubSpot was a tool where I needed to do the work to record customer convos. Using HubSpot I used to be a knowledge hygienist. Using Lighfield, I’m a better."
Postle reported that his response times to prospects improved from weeks or months to 1 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 selected feature that addresses what he views as the first explanation for lost deals: easy 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 may draft and send the follow-up immediately. That's prevented no less than three deals from going cold this quarter."
Spineanu had evaluated competing modern CRMs including Attio and Clay before choosing Lightfield, dismissing Salesforce and HubSpot as "built for a special era." He said those platforms assume corporations have dedicated operations teams to configure workflows and maintain data quality — resources most early-stage corporations lack.
Why Y Combinator startups are rejecting Salesforce and starting with AI-native tools
Peiris claims that the present batch of Y Combinator startups — widely viewed as a bellwether for early-stage company behavior — have largely rejected each Salesforce and HubSpot. "For those who were to poll a random sampling of current YC startups and ask whether or not they're using Salesforce or HubSpot, the overwhelming answer could be 'no,'" he said. "Salesforce is just too expensive, too complex to establish, and admittedly doesn't do enough to justify the investment for an early-stage company."
Based on Peiris, most startups begin with spreadsheets and eventually graduate to a primary CRM — a transition point where Lightfield goals to intercede. "Increasingly, they're selecting Lightfield as an alternative and skipping that intermediate step entirely," he said.
This represents a well-known pattern in enterprise software disruption: a brand new generation of corporations forming habits around different tools, creating a gap for challengers to determine themselves before businesses grow large enough to face pressure toward industry-standard platforms. The corporate's strategy appears to deliberately goal this window, aiming to grow alongside early customers and turn into embedded of their processes as they scale.
Can Salesforce and HubSpot retrofit their legacy systems for AI, or is the architecture too old?
Each Salesforce and HubSpot have announced AI features in recent quarters, adding capabilities like conversation intelligence and automatic data entry to their existing platforms. The query facing Lightfield is whether or not established vendors can incorporate similar capabilities—leveraging their existing customer bases and integrations — or whether fundamental architectural differences create a real moat.
Peiris argues the latter. "The basic difference is in how we store data," he said. "Because we’ve got access to that complete context, the evaluation we offer 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 supply coaching insights, already serve similar functions but require Salesforce instances to operate. Peiris said Lightfield's advantage comes from unifying all the data model slightly than layering evaluation on top of fragmented systems.
"Now we have a more complete picture of every customer because we integrate company knowledge, communication sync, product analytics, and full CRM detail multi functional place," he said. "That unified context means the work being generated in Lightfield—whether it's evaluation, follow-ups, or insights—tends to be significantly higher quality."
The privacy and accuracy concerns that include AI-automated customer interactions
The architecture creates obvious risks. Storing complete conversation histories raises privacy concerns, and counting on large language models to extract and interpret information introduces the opportunity of errors—what AI researchers call hallucinations.
Peiris acknowledged each issues directly. On privacy, the corporate maintains that decision recording follows standard practices, with visible notifications that recording is in progress, and that storing sales correspondence mirrors what CRM vendors have done for many years. The corporate has achieved SOC 2 Type I certification and is pursuing each SOC 2 Type II and HIPAA compliance. "We don't train models on customer data, period," Peiris said.
On accuracy, he was similarly forthright. "In fact it happens," Peiris said when asked about misinterpretations. "It's unimaginable to completely eliminate hallucinations when working with large language models."
The corporate's approach is to require human approval before sending customer communications or updating critical fields — positioning the system as augmentation slightly than full automation. "We're constructing a tool that amplifies human judgment, not one which pretends to exchange it entirely," Peiris said.
It is a more cautious stance than some AI-native software corporations have taken, reflecting each 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. Somewhat than charging per-seat fees for a degree solution, the corporate is positioning itself as a consolidated platform that may replace multiple specialized tools — sales engagement platforms, conversation intelligence systems, meeting assistants, and the CRM itself.
"The actual problem is that running a contemporary go-to-market function requires cobbling together 10 different independent point solutions," Peiris said. "Once you pay for 10 separate seat licenses, you're essentially paying 10 different corporations to resolve the identical foundational problems over and once again."
The corporate operates primarily through self-service signup slightly than enterprise sales teams, which Peiris argues allows for lower pricing while maintaining margins. It is a common playbook amongst modern SaaS corporations 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 stays unproven. The corporate's current customer base skews heavily toward early-stage startups—greater than 100 Y Combinator corporations, in response to the corporate — a segment with limited budgets and high failure rates.
But Lightfield is betting it may turn into the system of record for a cohort of fast-growing corporations, eventually creating an installed base comparable to how Salesforce established itself many years ago. The corporate's trajectory will likely depend upon whether AI capabilities alone provide sufficient differentiation—or whether incumbents can adapt quickly enough to defend their positions.
The actual test: whether sales teams will trust AI enough to let it run their business
The corporate has outlined several areas for expansion, including an open platform for workflows and webhooks that may 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 incontrovertible 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, regardless of how much they fight to pretend to be."
Rose highlighted an unusual use case that emerged during Lightfield's own development: the corporate's product team used the CRM itself to research customer conversations and discover feature requests. "On this sense, Lightfield greater than only a sales database, it's a customer intelligence layer," Rose said.
This implies 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 corporate is concentrated on proving the core value proposition with early-stage corporations. However the broader query Lightfield raises extends beyond CRM software specifically: whether AI capabilities have advanced sufficiently to exchange structured databases as the inspiration of enterprise systems, or whether the present generation of huge language models stays too unreliable for business-critical functions.
The reply 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 slightly than calculate.
Lightfield is betting that the trade-off has already shifted in favor of approximation, no less than for the tens of millions of salespeople who currently view their CRM as an obstacle slightly than an asset. Whether that bet proves correct will help define the following generation of enterprise software.
