SalesTarget.ai
SalesTarget.ai — AI-Powered Sales Intelligence Operating System
Find & Enrich with 840M+ profiles. Validate contacts. Reach buyers on Email and LinkedIn. Close with a CRM built for salespeople — Power Dialer included.
SalesTarget.ai is a Sales OS built for outbound-driven B2B companies, agencies, and modern revenue teams. It centralizes every stage of the sales workflow — from data intelligence and enrichment to outreach, pipeline management, and AI assistance — eliminating the need for multiple disconnected tools.
At its core, the Intelligence Engine delivers prospecting power via 840M+ profiles, 150M+ company entities, 4,000+ data signals, and 50+ premium data providers — including real-time intent signals that surface in-market buyers before your competitors do.
Key capabilities:
Cold Email Outreach — smart sending, warm-up sequences, spintax & unified inbox
Power Dialer — auto-sequential dialing directly from the CRM
LinkedIn Automation — connection requests, InMail & multichannel drip sequences
Built-in Email Validation — reduce bounces & protect sender reputation
Integrated CRM — pipeline, deals, call logs, tasks & team collaboration
AI Co-pilot — find leads, build sequences & launch campaigns via simple chat commands
Intelligence → Enrichment → Validation → Email → Power Dialer → LinkedIn → CRM → AI Co-pilot. One platform. Infinite scale.
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Lockbox LIMS
A cloud LIMS that tracks samples, tests results, and manages inventory for life science research, industrial QC labs, and biotech/NGS. Includes regulatory support for CLIA and HIPAA, Part 11 and ISO 17025. The quality, security, traceability, and traceability for samples is crucial to a lab's success. Laboratory professionals can use the Lockbox LIMS system to manage their samples. They have full visibility of every step of the sample's journey from accession to long-term storage. LIMS analysis is more than just tracking results. Lockbox's multilayered sample storage and location management functionality lets you define your lab's storage structure using a variety location options: rooms and storage units, shelves and racks, boxes and boxes.
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Signals Research Suite
The Suite is designed to be both secure and scalable, featuring a contemporary and user-friendly interface that empowers scientists to have full control over the configuration of workflows for various techniques, modalities, and data types. The latest iteration, Signals VitroVivo 3.0, formerly known as Signal’s Screening, effectively converts raw data into practical insights, while Signals Inventa 3.0, previously called Signals Lead Discovery, serves as advanced analytics software that allows researchers to publish results effortlessly from diverse data sources. Additionally, it facilitates the capture of experimental data, oversees materials management, and streamlines collaboration workflows within an easy-to-navigate cloud-based electronic notebook. With adaptable visualizations and the ability to automate instrument data processing, the Suite enhances data quality and ensures reproducibility in research. It also provides a unified data management system for scientific outcomes, enriched with dynamic, interactive analytics. Furthermore, its extensibility allows for seamless integration with internal systems and partner processes, making it an invaluable tool for scientific discovery and innovation.
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Signals Translational
By leveraging visual analytics through TIBCO Spotfire®, PerkinElmer Signals Translational offers a comprehensive suite of tools designed to harmonize, manage, search, aggregate, and analyze extensive datasets consistently for translational research, all while ensuring scalability. This platform, driven by TIBCO Spotfire®, supports precision medicine initiatives by providing an unparalleled solution for biomarker discovery and patient stratification. The Linear Mixed Effect App (LME) within Signals Translational empowers researchers to evaluate the influence of various factors on specific phenotypes, allowing for adjustments related to random variables during analysis. Furthermore, it enables the identification of genes significantly affecting cancer stage progression, irrespective of patient origins. Notably, the LME models excel at addressing issues such as missing values and outliers, making them a robust choice for discovering potential biomarkers. Consequently, the integration of these advanced analytics tools enhances the efficacy of translational research in identifying key biomarkers that can lead to more personalized treatment approaches.
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