Former Palantir Executives Launch Perceptic with $12M to Unify AI-Driven Drug Discovery Platform
核心洞察
Former Palantir (搜索) Life Sciences executives have founded Perceptic (搜索), an AI startup that creates an end-to-end platform connecting drug discovery through clinical trial design, securing $12 million in seed funding led by Accel (搜索).
The platform addresses industry fragmentation by serving as "connective tissue" between discrete AI tools and pharmaceutical data, targeting asset scouting, indication selection, and clinical trial design with reported 50-fold increases in data extraction efficiency.
Perceptic (搜索)'s infrastructure-agnostic system allows pharmaceutical companies to integrate their own data and AI models while maintaining full traceability to prevent AI hallucinations, with multiple top-tier pharma companies already using the platform including CSL (搜索).
Three former Palantir (搜索) executives who led the company's Life Sciences practice have emerged from stealth with Perceptic (搜索), an AI startup building an end-to-end platform for pharmaceutical drug development. The London-based company announced a $12 million seed funding round led by Accel (搜索), with participation from Air Street Capital (搜索) and Elder Gull (搜索).
Tilman Flock (搜索), Perceptic (搜索)'s cofounder and CEO, spent nearly seven years at Palantir (搜索) building commercial AI platforms for life sciences companies. He founded Perceptic alongside Martin Copes (搜索) and Zaki Trache (搜索), who were key engineers on Palantir's AIP platform. The trio identified a critical gap in how pharmaceutical companies approach AI-driven drug discovery.
Addressing Industry Fragmentation
"For years, the industry has tried to improve each part of the [drug discovery] process separately, but that's a linear process where insight dies at every handoff," Flock explained. Unlike other AI startups that focus on specific aspects like protein structure prediction or patient recruitment optimization, Perceptic (搜索) positions itself as the "connective tissue" between discrete AI tools and pharmaceutical data.
The platform is "infrastructure and model agnostic," allowing customers to integrate their own data, hardware, and AI models while Perceptic (搜索) serves as the unifying layer. This approach addresses the siloed nature of pharmaceutical R&D, where different departments often work with incompatible systems and data formats.
Three Core Focus Areas
Perceptic (搜索) targets three critical areas of pharmaceutical R&D. The first involves scouting external assets from biotechnology companies that large pharmaceutical firms seek to license. The startup's system can compress scientific due diligence assessments from weeks to hours, significantly accelerating the evaluation process.
The second focus area helps pharmaceutical companies select which indications to pursue in clinical trials. According to Flock, these decisions can determine the fate of investments worth millions of dollars, making accurate and rapid assessment crucial for strategic planning.
The third area involves building a "data foundation" for clinical trial design, which has produced a 50-fold increase in clinical data extractions according to the company's internal metrics.
Data Integration and Traceability
Pharmaceutical companies typically draw from three data sources: public knowledge including patents and literature, internal proprietary data from years of research and trials, and external datasets from consultants and vendors. Perceptic (搜索)'s platform harmonizes all three data types using "AI workers" or AI agents tuned to different data formats to identify insights and optimizations.
Given the regulatory requirements in pharmaceutical development, companies need complete data provenance for any decision-making information. Perceptic (搜索)'s system addresses the critical issue of AI hallucinations by allowing customers to trace every claim back to its original source, ensuring reliability in high-stakes drug development decisions.
Market Traction and Investment Rationale
The platform is already being used by multiple top-tier pharmaceutical companies, though Perceptic (搜索) has only disclosed CSL (搜索), the Australian biotechnology company, as a named customer. Sonali De Rycker (搜索), the Accel (搜索) partner who led the investment, said she was attracted to Perceptic's ability to "follow the drug" through the entire development lifecycle rather than targeting specific departmental silos.
"From the point at which you have hypothesis and evidence all the way to when you're designing the clinical trial, and everything you do in between … it makes no sense for it to be siloed," De Rycker told Fortune.
Accel (搜索) had been tracking the founding team while they were still at Palantir (搜索) and invested roughly a year after their first meeting, by which point Perceptic (搜索) had moved beyond pilot programs into paid production deployments. The company has grown to approximately 20 employees.
Strategic Positioning and Expansion Plans
De Rycker noted that Perceptic (搜索)'s approach reflects broader trends in enterprise AI, where platforms increasingly unify workflows and data across multiple departments rather than offering standalone tools. These integrated platforms can serve as "almost a new source of truth," potentially replacing or relegating traditional databases and enterprise resource planning software.
Nathan Benaich (搜索), founder and general partner of Air Street Capital (搜索), stated that pharmaceutical R&D's next breakthrough "won't come from a thousand better point tools, or from frontier models alone" but from an operating system "that connects data, decisions, and context across a 15-year process."
The company plans to use the seed funding primarily for engineering development and customer base expansion. Most of Perceptic (搜索)'s engineering operations are based in London, drawing partly on Palantir (搜索) alumni, while many customers are located in the United States, where the company plans to expand its presence.
Flock emphasized the company's current position: "We're far beyond product-market fit. It's about scaling out." The startup enters a competitive landscape where numerous AI companies are targeting drug discovery, though no AI-discovered drugs have yet completed human clinical trials and received regulatory approval.