When your codebase grows faster than your team's ability to understand it, institutional knowledge becomes a bottleneck. Learn how CoreRAG uses real-time GitLab webhooks and Graph-RAG technology to turn dense repository data into plain-language answers, helping developers, sales teams, and tech leaders make smarter, faster decisions.

How an AI–Physics Hybrid Generalized to Unseen Antibodies—Without Benchmark-Specific Training Cross-validation can show how well a developability model performs on familiar data. But in real drug discovery, the molecules being evaluated are usually new. The real challenge is whether a model can remain reliable when it encounters molecules it has never seen before. We tested this by evaluating Apotek Landscape zero-shot on GDPa3, the blinded test panel from the 2025 Ginkgo AbD

You've probably heard of RAG (Retrieval-Augmented Generation), the technique that lets AI systems look things up before answering, rather than relying purely on memory. But here's something most people don't know: the way AI searches for information matters enormously. There isn't just one way to retrieve knowledge. In fact, there are several query modes, each with its own strengths: Dense, Sparse, Hybrid, and Graph RAG. Our product supports all of these, so let's break down

Synthetic Data Generation (SDG) is overturning the oldest assumption in AI training: that real-world data is always more credible. Field results say otherwise — better generalization, at half the cost.

The biggest bottleneck in AI was never the model. It's that the world can't give it enough to learn from.

A modular, evidence-grounded framework for rescuing, re-engineering, and accelerating biopharma assets Key Takeaways Blind Spots of Brute Force: Asset redevelopment significantly compresses clinical timelines but capturing this economic premium requires integrating heterogeneous biological, chemical, and clinical evidence into decision-ready evidence packages that conventional empirical screening cannot efficiently produce at scale. TechBio Differentiation: While generic AI

AI-driven marketing mix modeling reveals hidden inefficiencies in budget allocation, enabling marketers to optimize spend and drive measurable, incremental ROI.

Synthesizing Fragmented Biological Signals and Biomedical Knowledge for Truth-Seeking Target Prioritization Key Takeaway Data Synthesis Bottleneck: The challenge in modern drug discovery is not a lack of data, but a lack of decision-ready synthesis. Teams need a repeatable framework to convert raw omics signals and literature hits into a prioritized, actionable discovery strategy. The Prioritization Hurdle: Teams may already have omics data, public-database hits and litera

Combining deep learning models with physics-based validation to systematically screen therapeutic protein variants and accelerate developability optimization Key Takeaways Break the “Design-Test-Fail” Loop : Traditional protein optimization is a months-long bottleneck defined by high costs and limited success rates. Apotek Landscape shifts the heavy lifting from the wet lab to our computational platform, identifying low-risk candidates before a single pipette is touched. Iden

The "Right to Repair" movement has moved from a niche advocacy effort to a global regulatory reality. For Original Equipment Manufacturers (OEMs), this isn't just about compliance; it's a profound shift that demands a re-evaluation of product design, supply chains, and, critically, after-service strategies. The good news? Advanced technologies like Physical AI, offer a proactive solution to not only meet these new mandates but to transform challenges into strategic advantages

Graph RAG represents a shift from text-centric retrieval to structure-aware understanding to enable: 1. More consistent answers 2. Better global context 3. Reduced hallucination 4. Stronger reasoning over relationships Rather than asking LLMs to guess structure from text at generation time, Graph RAG makes structure explicit upfront.

Apotek Genesis "Antibody Redesign" is a platform-based antibody optimization solution that enables rapid exploration of CDR sequence space, structural prediction, and binding affinity ranking—accelerating the path from lead antibody to optimized therapeutic candidate.

"Machine-learning Method on Vectorized Three-Dimensional Model and Learning System Thereof" describes a novel method for utilizing vectorized 3D models to generate synthetic data of real-world objects for AI training. It can be applied for various applications, including object identification and anomaly detection, where training data are difficult or economically infeasible to acquire, and the robotic agents need these capabilities to function and swiftly interact with the p

Retrospective Probability of Success (PoS) answers “what usually happens across programs.” Apotek Signal answers “what is likely to happen for this asset.” Key Takeaways Apotek Signal quantifies clinical risk at the asset level : It estimates the probability that a specific trio of drug–target–indication will succeed at a given clinical phase. Phase-aware by design : Separate models for clinical trial phase I, II, and III reflect how decision criteria change across developme

Marketing measurement has gone through three distinct phases: 1. Legacy MMM Era (pre-2010s) 2. Attribution Boom (2010s) 3. MMM Rebirth (2020s) This isn’t a story about preference. It’s a story about adaptation to thrive in the everchanging business world.

How optimizing leads early can foresee risks, ensure safety and developability, and protect intellectual properties

Data Detail is Crucial: To perform causal analytics like Marketing Mix Modeling, you must preserve raw, event-level data (joint distributions). Summary reports (marginals) discard the necessary context to attribute success to specific combinations of customer traits. Avoid the "Average" Trap: Aggregated data can hide the true story. Two completely different customer behaviors can look identical in summary reports, making accurate attribution impossible without the raw source

Marketers have more performance data than ever, yet trust in that data isn’t really improving. The fix lies in performance measurement using Marketing Mix Modeling.

Translating AI-powered research into real-world cancer impact Apotek Joins NSF I-Corps Spark Special Cohort on AI/ML and Cancer Care Taipei | [November 12, 2025] — Vizuro is proud to announce that our Apotek platform has been selected for the National Science Foundation (NSF) I-Corps Spark Cohort , beginning November 13, 2025 . This specialized program focuses on AI/ML-powered innovations in cancer research and care , empowering teams to translate cutting-edge science into

In the highly regulated and patient-centric world of pharmaceutical launches, gaining a rapid, nuanced understanding of public perception is no longer optional—it is a critical necessity. Social listening has evolved from a simple monitoring tool into a sophisticated intelligence engine, providing real-time data that directly informs safety monitoring, brand strategy, and commercial success.