Find answers to the most common questions about Kernshell’s AI development, enterprise technology services, and proprietary products. Browse by category or search below.
Kernshell Technologies is an enterprise AI development and digital transformation company headquartered in Ahmedabad, India. Since 2016, we have served 170+ global clients, including Mars, Jabil, Hitachi Energy, Johnson & Johnson, and SkyWest Airlines.
Kernshell delivers enterprise AI across six practice areas: Generative AI (custom LLMs, RAG systems, AI copilots on Azure OpenAI and AWS Bedrock), Conversational AI (chatbots, voice AI, virtual assistants), Core Machine Learning (predictive analytics, computer vision, classification), MLOps and AI Operations (automated pipelines, model monitoring, drift detection), AI Data and Governance (GDPR/HIPAA frameworks, explainable AI), and Natural Language Processing. Kernshell also develops LexOps AI and ScreenX Health as proprietary enterprise AI products.
Kernshell builds and deploys AI on Microsoft Azure (Azure OpenAI Service, Azure AI Foundry, Azure Machine Learning), Amazon Web Services (SageMaker, AWS Bedrock, Comprehend), and Google Cloud (Vertex AI, AutoML). LLMs used include GPT-4o, Anthropic Claude 3.5, Meta LLaMA 3, and Google Gemini 1.5 — selected based on client data residency, performance, and cost requirements.
Kernshell serves manufacturing (predictive maintenance, ETQ Reliance AI integration, computer vision quality inspection — clients Jabil, Hitachi Energy), healthcare (ScreenX Health AI patient screening, HIPAA-compliant AI, EHR integration), legal (LexOps AI contract review), financial services (fraud detection, risk modeling), retail (demand forecasting, recommendation engines), and logistics.
Kernshell uses a Proof-of-Concept methodology — validating the AI approach on real company data in 3–6 weeks before committing to full production development. Full production AI applications typically take 8–16 weeks covering use case definition, model selection, development, integration, testing, and deployment with monitoring infrastructure.
Yes. Kernshell has developed LexOps AI (Generative AI contract review platform built on Azure OpenAI) and ScreenX Health (HIPAA-compliant AI patient screening platform built on AWS Bedrock and Anthropic Claude). Both are available as enterprise software licenses with full implementation, customisation, and managed support services.
Kernshell provides GenAI strategy and use case assessment, custom LLM development and fine-tuning (GPT-4, Claude, LLaMA, Gemini), Retrieval-Augmented Generation (RAG) system development, AI copilot and virtual assistant development, multi-modal AI, prompt engineering, GenAI integration with CRM and ERP systems, and AI security and governance frameworks for regulated industries.
Kernshell selects LLMs based on client requirements. Models used include GPT-4o via Azure OpenAI Service, Anthropic Claude 3.5 via AWS Bedrock (preferred for long-context document analysis such as LexOps AI contract review), Meta LLaMA 3 deployed on-premises for maximum data control, Google Gemini 1.5 via Vertex AI for multimodal applications, and Mistral for cost-sensitive workloads.
Retrieval-Augmented Generation (RAG) connects an LLM to your organisation’s specific knowledge base — retrieving relevant documents before generating answers. This grounds responses in your actual company data, dramatically reducing hallucination risk and enabling source citations. Kernshell implements RAG for knowledge systems, contract review (LexOps AI), and clinical screening (ScreenX Health).
Yes. Kernshell builds GenAI for manufacturing including ETQ Reliance AI integration (surfacing quality insights from production data), predictive maintenance knowledge systems using RAG over equipment manuals and sensor data, and automated compliance report generation for ISO and FDA-regulated environments. Manufacturing clients include Jabil and Hitachi Energy.
Use RAG when your knowledge base changes frequently, you need source citations, or you have large diverse document sets. Use fine-tuning when you need consistent domain-specific writing style, vocabulary, or specialised reasoning patterns. Best practice: combine both — fine-tune for domain expertise, RAG for current knowledge access. They are complementary, not competing approaches
Agentic AI involves autonomous AI agents that plan, reason, and execute multi-step tasks across enterprise systems — going beyond single-turn chatbots to independently completing complex workflows. Kernshell’s KERN Agentic Gen AI platform enables multi-agent orchestration for enterprise automation, where specialist agents coordinate to handle procurement, compliance, document processing, and customer onboarding workflows.
Key use cases: procurement automation (sourcing, PO creation, vendor communication), compliance monitoring (continuous regulatory document analysis and gap detection), document processing pipelines (contract ingestion, classification, data extraction), field service coordination (work order assignment, parts ordering, technician routing), and customer onboarding (multi-system data collection, verification, and account provisioning).
KERN Agentic Gen AI is Kernshell’s proprietary multi-agent orchestration platform enabling autonomous enterprise workflow automation. It coordinates specialist AI agents across tools, APIs, and enterprise systems — deployed for Fortune 500 clients across manufacturing, healthcare, and financial services. The platform supports tool use, memory, planning, and human-in-the-loop checkpoints for high-stakes decisions.
Automated ML pipeline development (CI/CD/CT), model registry and version control, model deployment and containerisation on AWS/Azure/GCP, production performance monitoring, data drift detection, automated retraining pipelines, compute cost optimisation, and LLMOps for Generative AI applications — including prompt versioning, hallucination monitoring, RAG retrieval quality tracking, and token cost management.
MLflow for experiment tracking and model registry, Kubeflow for Kubernetes-native pipeline orchestration, AWS SageMaker Pipelines and Step Functions for AWS environments, Azure ML Pipelines for Azure, Evidently AI for drift detection, Langsmith and Langfuse for LLMOps tracing and evaluation, GitHub Actions for CI/CD, and Terraform and Kubernetes for infrastructure as code.
LLMOps extends MLOps practices for Large Language Model applications. Traditional MLOps monitors prediction accuracy and data drift. LLMOps adds: prompt versioning and evaluation, hallucination monitoring to detect factually incorrect LLM outputs, RAG retrieval quality monitoring, token cost management for usage-billed LLM APIs, and content safety monitoring for prompt injection attempts and policy violations.
Data pipeline development, data quality management, data lineage tracking, GDPR/CCPA/HIPAA compliance framework design, bias detection and mitigation, explainable AI using SHAP and LIME with model card documentation, AI audit trails for regulatory examination, NIST AI RMF alignment, and EU AI Act readiness assessment for enterprise organisations operating in regulated industries.
AWS (S3, Glue, Redshift, Lake Formation), Azure (Data Factory, Synapse Analytics, Purview), Google Cloud (Dataflow, BigQuery, Dataplex), Databricks and Delta Lake, Snowflake, Apache Airflow and Dagster for orchestration, Apache Kafka for streaming, Informatica and Collibra for data governance. Visualisation: Tableau and Power BI.
Kernshell assesses each AI system against the EU AI Act’s risk tiers (Unacceptable / High / Limited / Minimal risk), prepares model documentation and conformity assessments for High-Risk systems, implements human oversight mechanisms, establishes bias testing protocols across protected characteristics, and builds audit trail infrastructure required for regulatory examination by national competent authorities.
Named Entity Recognition, sentiment and emotion analysis, document intelligence and classification, question answering systems, text summarisation, language translation and localisation, and custom NLP model development trained on domain-specific data. A production NLP application is LexOps AI, which uses NLP to extract legal clauses and classify contract terms across MSAs, SOWs, and NDAs.
BERT, RoBERTa, and DistilBERT via Hugging Face Transformers; domain-fine-tuned LLMs (GPT-4, Claude, LLaMA); spaCy for NER and dependency parsing; NLTK and Gensim for topic modelling; Azure AI Language for text analytics and entity recognition; AWS Comprehend for custom classifiers; Google Cloud Natural Language API; and FastAPI with Azure ML or SageMaker for production deployment.
Yes. Kernshell builds domain-specific NLP for legal document analysis (contract clause extraction and risk classification in LexOps AI), clinical NLP (extracting structured data from unstructured medical notes, ICD-10 coding assistance), and manufacturing (technical manual parsing, quality report classification, and automated CAPA narrative generation for ETQ Reliance integration).
SharePoint Online intranet development and SPFx custom web parts, Power Apps custom application development (canvas and model-driven), Power Automate workflow automation, Power BI dashboards and analytics, Power Pages external portals, Microsoft Copilot Studio AI chatbot development integrated with Teams, SharePoint, and Dynamics 365, and Microsoft 365 migration from on-premise environments. Clients: Mars, Jabil, Hitachi Energy, Kimberly-Clark.
Yes. Kernshell builds SharePoint Online intranets, document management systems, custom SPFx (SharePoint Framework) web parts, modern communication and team sites, SharePoint migration from on-premises environments, SharePoint-based intranet portals, and SharePoint CMS for content publishing workflows — for Fortune 500 enterprises across manufacturing, healthcare, and financial services.
Power Automate eliminates manual repetitive tasks. Common Kernshell implementations: purchase order approval workflows, HR leave request automation, SharePoint document routing, Teams notifications triggered by business events, ERP-to-CRM data synchronisation, expense report processing, new employee onboarding flows, and email-to-SharePoint document ingestion — typically reducing manual process time by 60–80%.
Power Pages are Microsoft’s low-code platform for building externally-facing business websites connected to Dataverse and Dynamics 365. Kernshell builds Power Pages for customer self-service portals, partner portals, supplier portals, and employee-facing sites — enabling organisations to deliver professional external experiences without the cost and time of custom web development.
Microsoft Copilot Studio (formerly Power Virtual Agents, renamed November 2023) is Microsoft’s AI chatbot development platform. Kernshell builds enterprise Copilot Studio deployments with generative AI responses via Azure OpenAI, multi-channel deployment across Teams and web, and integration with existing business systems for HR helpdesk, customer service, and IT support automation.
Yes. Kernshell develops executive dashboards, operational reporting, and self-service BI using Power BI for clients including Jabil and Hitachi Energy. Services include Star Schema data modelling, custom DAX measures, DirectQuery and scheduled refresh, row-level security, mobile-optimised layouts, and paginated reports. Kernshell also builds Power BI embedded analytics for custom applications.
Yes. Kernshell Technologies is a certified Progress Software Sitefinity partner. Services include Sitefinity implementation and configuration, custom .NET MVC widget development, Sitefinity Cloud deployment and managed hosting, Sitefinity Insight customer data platform and personalisation, multi-site architecture, headless CMS integration via the Content Delivery REST API, and migration from legacy CMS platforms to Sitefinity.
Sitefinity Insight is Progress’s customer data platform and marketing analytics layer providing visitor tracking, behavioural segmentation, journey analytics, campaign performance measurement, and AI-powered content personalisation. Yes, Kernshell implements Sitefinity Insight as part of enterprise Sitefinity DX deployments for healthcare, financial services, and manufacturing clients requiring content personalisation at scale.
Yes. Kernshell implements Sitefinity in headless mode — content managed in Sitefinity’s editorial interface, delivered via the Sitefinity Content Delivery REST API to decoupled frontends including Next.js, React, and Vue.js. Editorial governance, approval workflows, multi-site management, and Sitefinity Insight analytics are fully preserved while the frontend uses modern frameworks for optimal performance.
Custom WordPress theme development from Figma designs, custom plugin development, WooCommerce e-commerce implementation, WordPress multisite deployments, Core Web Vitals performance optimisation (LCP, INP, CLS), security hardening, and migration from legacy CMS platforms to WordPress. Recent WordPress client: Trinity Consultants website redevelopment.
Healthcare (HIPAA-compliant patient portals, EHR-integrated websites), financial services and banking (GDPR and PCI DSS-compliant content management with approval workflows — see Sitefinity Banking Platform case study), manufacturing (dealer portals, technical documentation management, ERP-integrated product sites), and education (LMS-integrated learning portals and accessibility-compliant public sites).
Sales Cloud and Service Cloud implementation, Marketing Cloud Engagement (B2C) and Marketing Cloud Account Engagement/Pardot (B2B) configuration, Experience Cloud Communities portals, Revenue Cloud (CPQ) for configure-price-quote automation, Tableau CRM analytics, AppExchange package implementation, custom Apex and LWC development, Salesforce integrations with ERP and M365, and Salesforce migration and data cleanup.
Sales Cloud manages the pre-sale process — leads, opportunities, pipeline forecasting, and quota management. Service Cloud manages post-sale customer relationships — support cases, omni-channel routing (email, phone, chat, social), knowledge base, and field service dispatch. Many Kernshell clients deploy both, sharing Account and Contact records for a unified customer view across sales and support teams.
Implement CPQ when: your products require complex configuration (100+ options, bundling rules, compatibility constraints), quote creation takes more than 30 minutes per opportunity with errors common, you have variable pricing rules (volume discounts, customer-specific pricing) needing consistent application, or you have subscription/recurring revenue requiring automatic renewal quote generation. CPQ typically reduces quote creation time by 75%.
Yes. Kernshell builds Salesforce-M365 integrations including Salesforce for Outlook (bidirectional email and calendar sync), Salesforce-SharePoint document integration, Power Automate flows triggered by Salesforce events (Teams notifications, approval routing), and Power BI dashboards connected to Salesforce datasets for unified executive reporting across CRM and operational data.
Starting with dirty legacy data (migrating unclean data contaminates the new CRM from day one), over-customising before exhausting standard configuration, skipping user training (salespeople revert to spreadsheets), neglecting the data model (requiring expensive refactoring later), and no post-go-live admin plan. Kernshell’s implementations include a dedicated admin handover and 90-day post-launch support period to address these failure points.
Yes. Kernshell Technologies is an official Hexagon certified partner for ETQ Reliance — the enterprise Quality Management System platform for EHS, compliance, and quality management. Kernshell delivers ETQ Reliance implementation, module configuration, workflow design, system integration, AI enhancement, user training, and ongoing managed support for manufacturing and life sciences enterprises.
Document Control, CAPA (Corrective and Preventive Action), Audit Management, Nonconformance Management, Training Management, Risk Management, Supplier Quality Management, Change Management, and EHS modules. Kernshell also configures ETQ Reliance integrations with ERP systems (SAP, Oracle, Dynamics 365) and enterprise data platforms for unified quality and operational reporting.
Yes. Kernshell builds Generative AI integrations on top of ETQ Reliance — AI-powered quality anomaly detection, automated compliance report generation, RAG-based knowledge systems that surface quality insights from production data, NLP-based document classification for CAPA and audit records, and predictive analytics for supplier quality risk scoring. Manufacturing clients include Jabil and Hitachi Energy.
ETQ Reliance is used in manufacturing (automotive, electronics, consumer goods — Jabil is a Kernshell ETQ client), life sciences (pharma, medical devices — FDA 21 CFR Part 11 compliance), aerospace and defence (AS9100 certification management), and energy (ISO 45001 EHS management, Hitachi Energy is a Kernshell ETQ client). Kernshell has 8+ years of ETQ Reliance implementation experience across these sectors.
Data pipeline development (ETL/ELT), data warehouse and lakehouse architecture (Snowflake, Databricks, BigQuery, Redshift), Power BI and Tableau dashboard development, data quality management and observability, real-time streaming analytics (Kafka, Flink), dbt-based transformation frameworks, DataOps implementation with Airflow and Dagster orchestration, and self-service analytics enablement for business teams.
ETL transforms data before loading to the destination — suited for on-premises data warehouses where destination compute is expensive. ELT loads raw data to the destination first, then transforms using the warehouse’s elastic compute — preferred for cloud platforms (Snowflake, BigQuery, Redshift). Kernshell recommends ELT with dbt for modern cloud data stacks — it is more flexible, enables reprocessing of raw data, and aligns with software engineering best practices.
Power BI is preferred for Microsoft 365 organisations (Kernshell delivers CEO dashboards, operational reports, and self-service analytics for Jabil and Hitachi Energy). Tableau for organisations with existing Tableau investments. Looker for Google Cloud-native deployments. Embedded analytics using Apache Superset or Metabase for custom application dashboards where end users need analytics without a separate BI tool license.
Yes. Kernshell builds streaming data pipelines using Apache Kafka for event ingestion, Apache Flink or Spark Streaming for real-time processing, and cloud-native services (AWS Kinesis, Azure Event Hubs, Google Pub/Sub) for managed streaming. Use cases include real-time fraud detection, live operational dashboards, IoT sensor stream processing, and customer behaviour analytics feeding real-time personalisation engines.
Custom web application development, enterprise API development and integration, SaaS product development, legacy system modernisation, microservices architecture design, DevOps and CI/CD pipeline setup, and cloud-native application development on AWS, Azure, and Google Cloud. Technology stack: React, Next.js, Angular, Vue.js, Node.js, Python (FastAPI, Django), .NET Core, Java (Spring Boot), and Go.
Yes. Kernshell uses Scrum with 2-week sprint cycles, daily standups, sprint reviews, and retrospectives. Clients receive sprint velocity reports, a backlog management portal, and direct access to the development team via Teams or Slack. Fixed-price milestone engagements are available for well-defined scope projects. Kernshell integrates with client project management tools including Jira, Azure DevOps, and Monday.com.
Yes. Kernshell assesses legacy systems and designs modernisation roadmaps using strangler-fig architecture (gradually replacing legacy components with modern APIs without big-bang rewrites), microservices decomposition, cloud migration (lift-and-shift, re-platform, or re-architect depending on ROI analysis), and API-first transformation enabling legacy systems to connect with modern applications and AI tools.
Kernshell uses a shift-left testing approach — QA engineers are involved from the requirements phase, not just post-development. Services include unit and integration testing, automated test suite development (Selenium, Cypress, Playwright), performance and load testing (k6, JMeter), security vulnerability assessments (OWASP Top 10), and UAT facilitation with client stakeholders before every production release.
Native iOS development (Swift, SwiftUI), native Android development (Kotlin, Jetpack Compose), cross-platform apps using React Native and Flutter, Progressive Web Apps (PWA), enterprise mobile apps with ERP/CRM integration (Salesforce, Dynamics 365, SAP), and mobile app AI features including on-device ML inference and conversational AI integration via LLM APIs.
Choose React Native when your team already uses React or JavaScript, when web and mobile share significant business logic, or when deep JavaScript ecosystem integration matters. Choose Flutter when maximum UI consistency across Android and iOS is critical, when targeting unusual screen sizes (tablet, TV, desktop), or when top-tier animation performance is required. Both deliver near-native performance for most enterprise use cases.
Yes. Kernshell integrates AI into mobile apps including on-device ML inference using Core ML (iOS) or TensorFlow Lite and ML Kit (Android), cloud-connected AI features via LLM APIs for conversational interfaces within the app, computer vision for real-time image analysis (quality inspection, document scanning, AR overlays), and voice AI integration using VAPI or OpenAI Whisper for speech-to-text features.
A simple mobile app (5–10 screens, standard UI patterns, REST API integration) takes 10–16 weeks. A mid-complexity enterprise app (15–30 screens, complex workflows, multiple integrations) takes 16–28 weeks. A full-featured platform with AI, payments, and enterprise system integration typically takes 28–48 weeks. Kernshell provides fixed-scope, milestone-based proposals for all engagements.
IoT platform development (device connectivity, data ingestion, real-time dashboards), industrial IoT for manufacturing (predictive maintenance, OEE monitoring, energy management), IoT-cloud integration on AWS IoT Core and Azure IoT Hub, digital twin development, edge computing deployments using NVIDIA Jetson and AWS Greengrass, and IoT data pipeline development feeding analytics and AI model training.
Kernshell builds end-to-end pipelines from IoT sensor data to AI models — ingesting sensor streams via Kafka or Azure Event Hubs, processing and storing in data lakehouses (Databricks, Snowflake), training ML models for predictive maintenance, anomaly detection, and demand forecasting on historical sensor data. Real-time inference runs at the edge for low-latency requirements or via cloud APIs for complex models.
Manufacturing (OEE monitoring, predictive maintenance, quality sensor integration with ETQ Reliance — clients Jabil and Hitachi Energy), energy (grid monitoring, renewable energy asset management), logistics (fleet tracking, cold chain monitoring, last-mile visibility), healthcare (medical device connectivity, patient monitoring integrations), and smart building management (energy optimisation, occupancy analytics).
User research and persona development, information architecture design, wireframing and user flow mapping, interactive prototype development, visual design and design system creation (component libraries), usability testing, accessibility compliance assessment and remediation (WCAG 2.1 AA), and design-to-development handover documentation. Tools: Figma (primary), Adobe XD, Sketch, InVision, and Maze for user testing.
Yes. Kernshell’s UI/UX team designs interfaces for AI products — including the LexOps AI contract review interface (document upload, clause flagging visualisation, risk scoring dashboard) and ScreenX Health patient screening interfaces (voice agent call flows, chat widget design, clinical coordinator review dashboards). AI UX requires specialist patterns for surfacing confidence scores, citations, and human-override controls.
Kernshell designs to WCAG 2.1 AA — the accessibility compliance level required for most enterprise and government contracts. This includes colour contrast ratios (minimum 4.5:1 for normal text), full keyboard navigability, screen reader compatibility with ARIA labels, visible focus indicators, captioning for media content, and alt text for all images. Accessibility is designed in from wireframe stage, not retrofitted post-development.
AI/ML engineers (LLM development, RAG systems, LangChain, PyTorch, TensorFlow), cloud architects (AWS, Azure, GCP), Microsoft Power Platform developers (Power Apps, Power Automate, Power BI, Copilot Studio, SharePoint), Sitefinity CMS developers, full-stack developers (React, Next.js, Node.js, Python, .NET Core, Java), data engineers (dbt, Airflow, Kafka, Snowflake), DevOps engineers, mobile developers (React Native, Flutter, Swift, Kotlin), and QA/test engineers
Staff augmentation places individual specialists directly into your existing team under your management — best for filling specific skill gaps. A dedicated team is a complete development unit with Kernshell team management and established sprint workflows — best for ongoing product development. Full project outsourcing provides end-to-end delivery where Kernshell owns technical execution and outcomes from planning through launch — best for clearly defined projects where you want to focus on business outcomes.
Individual specialist deployment: 5–10 business days from requirements confirmation. Dedicated team deployment: 10–15 business days. Timeline includes technical skills assessment, candidate matching against specific requirements, client-side interview process, and structured onboarding. Kernshell’s delivery centre is in Ahmedabad, India with account management in Plano, Texas. All staff operate in overlapping US business hours.
Yes. Over 8+ years, Kernshell has placed technology professionals at Mars, Johnson and Johnson, Jabil, Hitachi Energy, Kimberly-Clark, L’Oreal, The Coca-Cola Company, and SkyWest Airlines across AI/ML, Microsoft 365, software development, data engineering, and cloud infrastructure disciplines. Kernshell has a track record of vetting developers who integrate successfully into Fortune 500 engineering cultures.
Blockchain (smart contracts, supply chain traceability, digital identity), Augmented Reality and Virtual Reality (AR-assisted manufacturing maintenance, VR training simulations), Edge AI (on-device ML inference for IoT and industrial applications), Web3 infrastructure for enterprises exploring tokenisation, and Quantum-readiness assessments for organisations evaluating post-quantum cryptography migration timelines.
Kernshell implements supply chain traceability on Ethereum, Hyperledger Fabric, and Polygon — recording production events, material provenance, quality certifications, and logistics handoffs on an immutable ledger. This enables real-time supply chain visibility, counterfeit detection, and automated compliance reporting for manufacturing clients, including integration with ETQ Reliance quality events for end-to-end traceability.
Yes. Kernshell builds AR-assisted maintenance applications (technicians see step-by-step repair guidance overlaid on live equipment via HoloLens or mobile AR), VR training simulations (safe environment for hazardous procedure training), and digital twin visualisations showing real-time sensor data in 3D equipment models. Platforms used: Unity, Unreal Engine, Microsoft HoloLens, and WebXR for browser-based AR experiences.
Enterprise platform consulting covers ERP integration and extension, enterprise API gateway design and management using Kong and Azure API Management, identity and access management (Azure AD, Okta, SAML, OAuth 2.0), enterprise integration platform as a service (iPaaS) using MuleSoft and Azure Integration Services, and Microsoft Dynamics 365 configuration, customisation, and managed support.
Yes. Kernshell designs and implements enterprise integration architectures connecting ERP (SAP, Oracle, Dynamics 365), CRM (Salesforce, Dynamics 365 CRM), QMS (ETQ Reliance), HRMS (Workday, ADP), and cloud platforms (M365, AWS, Azure) — using RESTful APIs, GraphQL, event-driven messaging (Kafka, Azure Service Bus), and iPaaS platforms for complex multi-system orchestration requiring business process coordination across organisations.
Yes. Kernshell implements and customises Dynamics 365 across Sales, Customer Service, Field Service, Finance and Operations, and Business Central modules. Dynamics 365 implementations include Dataverse data modelling, Power Platform integration (Power Apps, Power Automate, Power BI on Dynamics 365 data), and Azure integration for enterprise data exchange. Kernshell also migrates organisations from legacy CRM and ERP systems to Dynamics 365.
LexOps AI is Kernshell’s Generative AI platform for contract review and legal operations — built on Azure OpenAI, Azure AI Search, and Azure Document Intelligence. It analyses MSAs, SOWs, NDAs, and vendor contracts against documented negotiation standards, flags risk deviations, and generates prioritised revision recommendations. Designed for corporate legal teams, in-house counsel, contract management departments, and law firms handling high volumes of commercial contracts.
ScreenX Health is Kernshell’s HIPAA-compliant AI patient screening and clinical intake platform — built on AWS Bedrock, Anthropic Claude, and VAPI voice AI. It screens patients 24/7 via AI Voice Agent (phone) and AI Chat Agent (web/SMS), applies eligibility criteria automatically, and routes qualified patients. Integrates with Epic and Cerner via HL7 FHIR. Designed for clinical trial sponsors, CROs, health systems, urgent care centres, and behavioural health programmes.
LexOps AI runs on a private-instance architecture — all document processing occurs in an isolated, single-tenant environment. Your contracts are never used to train shared AI models and are not retained after analysis. The platform uses Microsoft Azure with AES-256 encryption at rest, TLS 1.2+ in transit, enterprise access controls, full audit logging of every access event, and Kernshell executes enterprise data processing agreements as part of the deployment process.
Yes. ScreenX Health runs on HIPAA-eligible AWS infrastructure with Business Associate Agreement availability. PHI is encrypted in transit (TLS 1.2+) and at rest (AES-256), with role-based access controls, complete audit logging of all PHI access events, configurable data retention policies, and HITECH-compliant breach notification procedures. Security documentation including SOC 2 artefacts is available to enterprise clients during procurement evaluation.
KERN Agentic Gen AI is Kernshell’s proprietary multi-agent orchestration platform for enterprise workflow automation. It coordinates specialist AI agents across tools, APIs, and enterprise systems — autonomously planning and executing complex multi-step workflows for procurement, compliance monitoring, document processing, and field service coordination at Fortune 500 clients. The platform supports tool use, memory, planning cycles, and human-in-the-loop checkpoints for high-stakes decisions.
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