QEval
Manual call center QA covers 1 to 5% of interactions. The other 95% goes unreviewed. QEval closes that gap with AI-powered quality assurance that scores every voice, chat, and email interaction automatically.
The platform combines speech analytics, sentiment analysis, compliance monitoring, keyword detection, automated evaluation workflows, agent coaching tools, gamification, and 110+ analytics dashboards. Compliance includes PCI, HIPAA, and GDPR at 98% accuracy with real-time violation alerts. The scoring engine is trained on 138M+ contact center interactions and delivers 94% classification accuracy.
Organizations deploy QEval in 30 days, three to four times faster than typical quality monitoring platforms. Etech Global Services developed QEval through 20+ years of operating contact centers for Fortune 500 clients in healthcare, telecom, retail, banking, and BPO. ISO 27001, SOC 2, PCI-DSS certified. Built for QA managers, CX directors, and operations leaders replacing manual QA.
Additional capabilities include call recording and playback, screen capture for desktop activity review, customizable evaluation scorecards, QA calibration sessions to ensure scoring consistency across evaluators, and dispute management workflows for agents to challenge scores. The platform supports omnichannel quality monitoring with unified scoring across phone, chat, email, and social media interactions.
Supervisors access real-time dashboards to monitor live calls and intervene when needed. Automated alerts flag compliance risks, negative sentiment spikes, and performance drops instantly. Role-based permissions, audit logging, and end-to-end encryption meet enterprise security requirements. QEval connects with CRM, ACD, workforce management, and telephony systems through API integrations. Multi-site and multilingual support enables centralized QA management across geographically distributed contact center operations.
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Assembled
With Assembled, support leaders can unify human and AI agents in one intelligent platform that drives efficiency without compromising quality. Our technology enables over 50% automation of customer interactions, precise demand forecasting, and optimized staffing across in-house teams and BPO partners. From live workload balancing to AI agents that match your workflows and brand voice, Assembled ensures every chat, call, and email is handled with speed and consistency. Companies including Stripe, Canva, and Robinhood trust Assembled to elevate the customer experience and reduce operational costs. Core solutions span workforce and vendor management, real-time performance visibility, and AI Copilot — giving agents translation, reply suggestions, and instant task automation to resolve issues faster.
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DeepFND
DeepFND is a cutting-edge software solution tailored for the intricate design of deep foundation piles, integrating both geotechnical and structural calculations within a cohesive platform. This remarkable tool enables engineers to determine the axial geotechnical pile capacity with precision, taking into account elements such as skin friction and end bearing, while also permitting the evaluation of settlement behaviors based on data from Standard Penetration Tests (SPT) or Cone Penetration Tests (CPT). In addition, users can perform detailed lateral pile analyses that concentrate on displacement, shear, and moment under specified head-loads or through pushover techniques. The software is adept at designing not only individual piles but also pile groups and pile rafts, effectively managing various custom pile-cap shapes. DeepFND accommodates a diverse range of pile types, including drilled, driven, continuous flight auger (CFA), caissons, drilled-in-displacement, micropiles, and helical piles, while proficiently modeling soil-structure interaction through soil springs or advanced 3D finite-element analysis. Furthermore, it supports structural evaluations under both service and factored load scenarios, adhering to a multitude of international design codes such as ACI, AISC, Eurocodes, AS/NZS, Chinese Standards, and AASHTO LRFD, ensuring adaptability and compliance with various engineering standards. The combination of its extensive features and a focus on user experience propels DeepFND to the forefront as a vital resource for engineers engaged in deep foundation projects, making it an indispensable part of modern civil engineering practices.
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Stable LM
Stable LM signifies a notable progression in the language model domain, building upon prior open-source experiences, especially through collaboration with EleutherAI, a nonprofit research group. This evolution has included the creation of prominent models like GPT-J, GPT-NeoX, and the Pythia suite, all trained on The Pile open-source dataset, with several recent models such as Cerebras-GPT and Dolly-2 taking cues from this foundational work. In contrast to earlier models, Stable LM utilizes a groundbreaking dataset that is three times as extensive as The Pile, comprising an impressive 1.5 trillion tokens. More details regarding this dataset will be disclosed soon. The vast scale of this dataset allows Stable LM to perform exceptionally well in conversational and programming tasks, even though it has a relatively compact parameter size of 3 to 7 billion compared to larger models like GPT-3, which features 175 billion parameters. Built for adaptability, Stable LM 3B is a streamlined model designed to operate efficiently on portable devices, including laptops and mobile gadgets, which excites us about its potential for practical usage and portability. This innovation has the potential to bridge the gap for users seeking advanced language capabilities in accessible formats, thus broadening the reach and impact of language technologies. Overall, the launch of Stable LM represents a crucial advancement toward developing more efficient and widely available language models for diverse users.
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