Acunor

Acunor

IT-Dienstleistungen und IT-Beratung

Princeton, New Jersey 74,232 followers

Empowering You to Create Winning Teams with Top Talent

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Über uns

At Acunor, we transform digital challenges into sustainable business outcomes through a relationship-first approach. Our Talent and Technology Services are designed to meet the evolving needs of our customers, with offerings ranging from Talent Solutions to Cloud Services, Managed Services, and Digital Transformation. We believe that strong, trusted partnerships are key to long-term success, and we build deep relationships with our clients to ensure we fully understand and support their goals. Our core strength in tech hiring lies in delivering top-tier candidates with expertise in Generative AI, Artificial Intelligence, Machine Learning, Data Science, Analytics, and Data Engineering. Over the past four years, we have honed our proficiency by working with clients focused on these specialized requirements. Talent Solutions We are a trusted and strategic partner in fulfilling your talent needs. Our team not only delivers high-quality talent but also thoroughly vets each consultant, ensuring they meet your specific needs. Together, we help you overcome digital challenges and drive profitable, sustainable growth. Managed Services Our highly skilled professionals leverage the latest technology and best practices to deliver more effective and efficient outcomes, ensuring your business operates smoothly and efficiently. Cloud Services We help companies modernize their technology, reimagine their processes, and transform customer experiences, keeping them ahead in today’s fast-changing world. Digital Transformation We turn technological disruptions into business advantages, enabling your organization to create sustainable growth and thrive in a digitally driven marketplace. Discover how Acunor can be your trusted guide to digital success, with relationships that matter and talent that drives results.

Website
http://www.acunor.com
Industrie
IT-Dienstleistungen und IT-Beratung
Größe des Unternehmens
51-200 Mitarbeiter
Hauptsitz
Princeton, New Jersey
Typ
In Privatbesitz
Gegründet
2016
Spezialitäten
IT Consulting, IT Staffing, IT Services, Staffing, Talent Services, Analytics, Cloud Services, Managed Services, Digital Transformation, Digital Optimization, Contract Staffing, Direct Placement, Contract-to-hire, Generative AI, Business Analytics, Cloud Transformation, Application Support, Application Management, Digital Engineering, and Operations Transformation

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Aktualisierungen

  • View organization page for Acunor, graphic
    Acunor

    74,232 followers

    View profile for Dr. Govind Nidigattu Ph.D, graphic
    Dr. Govind Nidigattu Ph.D

    Head of Data Science and Generative AI

    # LLMs in Healthcare and medical Generative AI is the next generation of state-of-the art models and has significant potential to revolutionize the proactive patient management, evidence-based treatment plans, enhancing diagnosis and treatment strategies. Generative AI provides data-driven health for all, of which health is the primary concern for humans. Gen AI models can generate precise summaries, clinical decisions, patient personalization treatments, and precision medication. Generative AI to enhance patient care, disease progression, develop treatment plans and strategies, and decrease administrative load. Telemedicine services can also benefit from Gen AI which can assist in patient follow-up care. # Summarization of patient personalization and clinical evidence-based treatment by doctors Summarizes patient visits, clinical notes, doctors' specialization, treatments and discharge summaries from data inputs. Summarize and transcribe between patients and doctors during consultations, allowing for more accurate and comprehensive diagnostics. Generate personalized treatment plans based on individual patient data based on clinical diagnostics, medication, medical history, and lifestyle factors. Generate patient-friendly summaries of their medical records, helping them understand their health conditions and treatment plans. Accelerates the power of virtual assistants that provide patients with information, questions and answers, and follow up of medications and appointments with doctors. Gen AI data scientists and organizations are building applications harnessing their power to improve the performance of the LLM application, and eventually get to the point of where we should use Retrieval-Augmented Generation (RAG) or model fine tuning to improve the results. # Retrieval augmented generation (RAG) Retrieval Augmented Generation (RAG) is one of the most advanced techniques in LLMs. This RAG approach integrates the power of retrieval (searching) into LLM text generation. The Retriever of an RAG system can have access to internal and external knowledge sources of information. It combines a retriever system, which fetches relevant document information, and an LLM, which produces answers using the information from those snippets. # Chunking techniques Splitting the documents into appropriate chunks which depends on nature of the document content, selection of the embedding model, chunk length, and use case application. Here are the chunking strategies. Chunking is very important for LLM performance. 1.     Character splitter 2.     Recursive character splitter 3.     Sentence splitter 4.     Semantic splitter 5. Propositions

    • 1. Store the various format data into database. 2. Pull the required data related to medical and healthcare. 3. Split text corpus into chunks. A chunk will represent a single piece of context to be queried 4. Embedding model to transform each of the chunks into a vector representation embedding. 5. Store vector embeddings in a Vector Database. 6. Retrieval augmented generation (RAG) – Retrieve the relevant information with respect to user prompt or query. There are many RAG based techniques such as rerank-english-v3.0, reciprocal rank fusion (RRF), and RAPTOR etc. 7. Query to ask using the same embedding model that was used to embed and the resulting vector embedding to run a query against the index in the Vector DB. 8. Vector DB performs for vector search for vector embedding from the index and returns relevant context vectors. The procedure returns vectors that are most similar in a given Embedding space. Mapping the returned Vector Embeddings to the text chunks that represent them.
  • View organization page for Acunor, graphic
    Acunor

    74,232 followers

    # LLMs in Healthcare and medical Generative AI is the next generation of state-of-the art models and has significant potential to revolutionize the proactive patient management, evidence-based treatment plans, enhancing diagnosis and treatment strategies. Generative AI provides data-driven health for all, of which health is the primary concern for humans. Gen AI models can generate precise summaries, clinical decisions, patient personalization treatments, and precision medication. Generative AI to enhance patient care, disease progression, develop treatment plans and strategies, and decrease administrative load. Telemedicine services can also benefit from Gen AI which can assist in patient follow-up care. # Summarization of patient personalization and clinical evidence-based treatment by doctors Summarizes patient visits, clinical notes, doctors' specialization, treatments and discharge summaries from data inputs. Summarize and transcribe between patients and doctors during consultations, allowing for more accurate and comprehensive diagnostics. Generate personalized treatment plans based on individual patient data based on clinical diagnostics, medication, medical history, and lifestyle factors. Generate patient-friendly summaries of their medical records, helping them understand their health conditions and treatment plans. Accelerates the power of virtual assistants that provide patients with information, questions and answers, and follow up of medications and appointments with doctors. Gen AI data scientists and organizations are building applications harnessing their power to improve the performance of the LLM application, and eventually get to the point of where we should use Retrieval-Augmented Generation (RAG) or model fine tuning to improve the results. # Retrieval augmented generation (RAG) Retrieval Augmented Generation (RAG) is one of the most advanced techniques in LLMs. This RAG approach integrates the power of retrieval (searching) into LLM text generation. The Retriever of an RAG system can have access to internal and external knowledge sources of information. It combines a retriever system, which fetches relevant document information, and an LLM, which produces answers using the information from those snippets. # Chunking techniques Splitting the documents into appropriate chunks which depends on nature of the document content, selection of the embedding model, chunk length, and use case application. Here are the chunking strategies. Chunking is very important for LLM performance. 1.     Character splitter 2.     Recursive character splitter   3.     Sentence splitter 4.     Semantic splitter 5. Propositions

    • 1. Store the various format data into database. 2. Pull the required data related to medical and healthcare. 3. Split text corpus into chunks. A chunk will represent a single piece of context to be queried 4. Embedding model to transform each of the chunks into a vector representation embedding. 5. Store vector embeddings in a Vector Database. 6. Retrieval augmented generation (RAG) – Retrieve the relevant information with respect to user prompt or query. There are many RAG based techniques such as rerank-english-v3.0, reciprocal rank fusion (RRF), and RAPTOR etc. 7. Query to ask using the same embedding model that was used to embed and the resulting vector embedding to run a query against the index in the Vector DB. 8. Vector DB performs for vector search for vector embedding from the index and returns relevant context vectors. The procedure returns vectors that are most similar in a given Embedding space. Mapping the returned Vector Embeddings to the text chunks that represent them.
  • View organization page for Acunor, graphic
    Acunor

    74,232 followers

    Tech Sector Layoffs Mount Amid AI Investment Frenzy Tech layoffs continue to surge—2023 saw 264,000 layoffs, and in 2024, over 132,900 employees have been affected across 410 tech firms so far. As companies shift focus, leaders like IBM CEO Arvind Krishna have stated that the company won’t be hiring while it transitions to AI. Similarly, Mukesh Ambani, Chairman of Reliance Industries, which cut 42,000 jobs last financial year, described AI as an “enabler for achieving a quantum jump in productivity and efficiency.” At the same time, AI investments are soaring, with global spending projected to reach $500 billion by 2024. Companies are cutting traditional roles but rapidly expanding AI-driven teams, presenting both challenges and opportunities. At Acunor, we help businesses navigate this shift by connecting them with top AI and tech talent. Our staffing solutions have placed over 100 professionals in emerging roles, empowering companies to innovate while helping candidates transition into future-ready positions. 💼💡 Let’s bridge the gap between AI disruption and workforce readiness together! 🌐 Let's connect- Pooja Gautam #TechLayoffs #AIInvestment #GenerativeAI #Acunor #StaffingSolutions #FutureOfWork #USStaffing #ITStaffing

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