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Table of Contents Introduction Imagine having a friendly, professional customer service rep who’s always on time, never takes a break, speaks your language, and handles any question with ease. Sounds ideal, right? That’s exactly what the AI kiosk  brings to life. Powered by advanced conversational AI and ultra-realistic avatars, this AI kiosk doesn’t just respond, it interacts.  Whether it’s guiding someone through a service, answering complex queries, or simply welcoming visitors, the kiosk delivers a human-like, multilingual experience that scales effortlessly and does it all with a digital smile. It’s perfect blend of efficiency and empathy can be experienced with the CRM integration. How It Works: Step-by-Step Magic Behind the Avatar User Speaks or Taps The user either asks a question or selects an option on the screen. AI Understands You (Literally) Using NLP (Natural Language Processing), the AI decodes not just the words, but the intent behind them. Smart, Relevant Responses The system instantly crafts a response that’s accurate, context-aware, and easy to understand. Avatar Comes to Life The response is lip-synced and animated with facial expressions, thanks to ’s advanced video synthesis tech. Voices You Actually Want to Hear This isn’t your typical robotic voice. Uses Interactive AI avatar chatbot that’s warm, clear, and matches the avatar’s personality, whether it’s friendly, formal, or fun. Real-Time Emotion Adaptation The AI senses your reaction, confused? Unresponsive? It adjusts tone, pace, or repeats itself, just like a good human would. The Conversation Continues Naturally It loops smoothly, making each exchange feel like a live, two-way conversation. Real-World Industry Use Cases AI Kiosks aren’t just cool tech, they’re practical solutions solving real-world problems. Here’s where they’re already making a difference: Banking & Finance Long queues? Outdated. With the Self-check-in AI kiosk, banks can deploy 24/7 virtual bankers that greet customers, guide them through transactions, and answer service-related queries, all with a human touch. It’s smarter banking that never sleeps. Retail & Malls From product discovery to multilingual support, these AI kiosks do more than just assist, they transform foot traffic into meaningful engagement. Customers feel seen, understood, and served, faster than ever before. Hospitals & Clinics Let your staff focus on care while the AI kiosk handles everything else. AI streamlines check-ins, answers patient questions, and reduces wait times, all while delivering a warm, efficient front-desk experience. Government & Smart Cities Whether it’s tourism help desks or service centers, citizens can now access information and services, any time, any language. Also, businesses cut costs without cutting corners on experience. It’s smarter service, minus the staffing strain. Cloud or On-Premise? Flexible Deployment That Fits Your Business No two organizations are the same and  AI gets that. That’s why our kiosks come with two deployment options to fit your exact operational needs: Deployment Type Best For Key Benefits Cloud-Based Retail stores, info desks, mid-sized chains Quick setup, lower upfront cost, scalable updates via cloud On-Premise Banks, hospitals, secured facilities Ultra-fast response, full data control, high compliance Whether you’re looking for cost efficiency or complete control, you get the same lifelike experience, just tailored to your infrastructure. Plug Into Your Ecosystem: Kiosk Real-time CRM-sync kiosk solution Bi-Directional CRM Connectivity Ever wish your self-service kiosks could talk directly to your CRM? There are built-in plug-and-play connectors for Salesforce, HubSpot, Microsoft Dynamics, Zendesk and more, so you’ll be live in minutes, not months. Our drag-and-drop field-mapping UI lets you wire up “First Name,” “Loyalty Tier,” or “Last Interaction Date” to any screen element without writing a single line of code. And the magic really happens with real-time sync: The moment a customer’s profile updates in your CRM, the kiosk reflects that change instantly and every interaction (from purchases to satisfaction scores) is written back immediately.  The result? A single, up-to-date 360° view of every customer. Flexible User Recognition Tired of clunky logins at your kiosks? We get it.  That’s why AI Kiosk supports QR and barcode scanning, phone-number or loyalty-ID entry, and even lightning-fast, edge-powered facial recognition with no cloud lag, guaranteed. Whether your customers tap a code on their phone, speak their number, or simply look at the camera, they’re recognised in under a second. That means personalised greetings, seamless check-ins, and built-in fraud prevention, all wrapped up in one smooth, intuitive experience that your users will actually enjoy. CRM intergration made it all easier. Enterprise-Grade Identity & Token Systems Security and compliance keeping you up at night? AI Kiosks integrate seamlessly with your existing SSO providers, think Okta, Auth0, or Azure AD, so employees and VIP members can log in using the credentials they already trust. Need temporary access for event attendees or contractors? Our custom token workflows issue one-time codes via SMS, email, or print voucher, no new accounts required. And for industries with heavy KYC demands, you can plug in ID-scan providers like Jumio or Onfido to verify passports, driver’s licenses, or government IDs on the spot. Ready for human-like AI interactions? Reserve your AI Kiosk Consultation now! Contact Us Secure Payment & Wallet Integrations Imagine a kiosk that’s also a fully compliant POS. Make it real with PCI-certified card readers, NFC tap-to-pay modules for Apple Pay or Google Pay, and EMV support for credit and debit cards. Returning customers can save their preferred payment method via Stripe, Braintree, or Adyen, turning future transactions into a single touch. Plus, loyalty points and gift cards work hand-in-hand, users redeem value at the kiosk, and your loyalty platform updates in real time. It’s a seamless, secure, and stress-free checkout every time. Plug-and-Play Extensibility Why settle for one-size-fits-all when you can customize?  Modular SDK and webhook framework let your developers build micro-apps, think targeted promotions, feedback surveys, or bespoke workflows, in JavaScript or Python. Deploy to your entire fleet with a single click, then watch our analytics dashboard for health checks, API logs, and error reports across every AI kiosk. You’ll have all the insights you need to optimize performance, iterate quickly, and deliver standout self-service experiences. Built for Business AI Kiosks are enterprise-ready from

Table of Contents Introduction Have you ever found yourself overwhelmed by pages of dense medical reports? You’re not alone. Doctors, patients, and healthcare providers often struggle to sift through complex medical jargon to get to the heart of a diagnosis or treatment plan. That’s exactly why medical report summarizers are becoming so popular. These AI-powered tools read through pages of doctor notes, lab results, and discharge summaries, then pull out the important stuff like diagnoses, medications, and next steps and present it in a simple, easy-to-read summary. This means doctors can make quicker decisions without missing critical details, and patients can actually understand what’s going on with their health. Key Features of a Medical Report Summarizer Natural Language Processing (NLP) Expertise Medical reports are full of tricky terms and abbreviations, but NLP helps the AI make sense of it all. It’s like having a digital expert that can read complex doctor notes and actually understand them. Summarization Accuracy In healthcare, getting the details right is everything. The summarizer needs to pull out the right diagnoses, meds, and treatment plans without missing anything important. Data Privacy & Compliance Since it’s dealing with private health info, keeping data safe is a top priority. That means following strict rules like HIPAA and using top-notch security to protect patient records. Seamless Integration with EHR/EMR Systems Doctors don’t want to juggle multiple systems, so the summarizer should fit right into the tools they already use. Seamless integration makes it easy to access summaries without breaking their workflow. User-Friendly Interface No one wants to wrestle with complicated software, especially in a busy clinic. A clean, simple interface lets users get the info they need quickly and move on with their day. Technology Stack Used Component Description AI/ML Models Large language models (e.g., GPT, custom-trained medical models) to understand and process medical text. Cloud Infrastructure Platforms like AWS, Azure, or Google Cloud for storage, processing, scalability, and security. Data Annotation Medical experts label real medical reports to train and fine-tune AI models accurately. Backend Development APIs, databases, and server logic to handle data flow between the AI models and front-end applications. Front-End Interface Clean, user-friendly dashboard where doctors and staff can easily view summarized reports. Security & Compliance HIPAA-compliant encryption, access controls, and data privacy measures to protect sensitive patient information. Factors Affecting the Cost Custom AI Model vs. Pre-trained Models If you’re building your own AI model from scratch, get ready for serious time and money investment. Using existing models like GPT or fine-tuning a pre-trained model is usually much faster and more budget-friendly. Data Collection & Annotation AI needs high-quality, real-world medical data to learn from and getting that data isn’t cheap. You’ll often need medical professionals to review and label reports, which adds to the cost. Compliance & Security Requirements Healthcare software must meet strict data protection standards like HIPAA, GDPR, and local regulations. Implementing strong encryption, audits, and legal compliance adds significant upfront and ongoing expenses. Integration with Existing Systems If you want your summarizer to work seamlessly with hospitals’ existing EHR or EMR systems, expect extra development costs. Custom APIs, middleware, and testing are usually required to make everything work together smoothly. Maintenance & Continuous Improvement Building the tool is just the beginning. Ongoing updates, model fine-tuning, security patches, and customer support all require continuous investment. This ensures your summarizer stays accurate, compliant, and user-friendly over time. Cost Breakdown of Medical Report Summarizer Cost Component Estimated Cost (USD) AI Model Development $50,000 – $200,000 (if custom) Pre-trained Model Fine-tuning $10,000 – $50,000 Data Collection & Annotation $20,000 – $100,000+ (depending on data size & complexity) Cloud Infrastructure (annual) $5,000 – $30,000 Backend & API Development $15,000 – $50,000 Front-End Development $10,000 – $30,000 Security & Compliance Setup $20,000 – $60,000 Ongoing Maintenance (annual) $10,000 – $40,000 Total Initial Development Cost: $150,000 to $500,000+ Annual Maintenance & Cloud Cost: $20,000 to $70,000/year Hidden Costs You Shouldn’t Ignore When budgeting for a medical report summarizer, there are a few sneaky costs that can catch teams off guard: Data Privacy Audits Even if you build everything securely, regular audits are often required to prove you’re compliant with regulations like HIPAA or GDPR. These audits can involve external consultants and legal teams, which means extra recurring costs. Legal & Regulatory Approvals Depending on where you operate, you may need government or institutional approvals before deploying your solution. Navigating this red tape and paying for expert legal advice can quickly add up. Staff Training No matter how user-friendly your summarizer is, staff will still need some training to use it efficiently. Creating training materials, onboarding sessions, and ongoing support can require both time and budget. Continuous Model Updates Medical AI isn’t a “set it and forget it” product, new drugs, protocols, and research constantly emerge. You’ll need to budget for regular model fine-tuning and updates to keep your summarizer accurate and clinically relevant. Build vs Buy: Which is Better for You? Building Your Own Solution If you have very specific needs, full control over your data, and the technical resources to pull it off, building your own summarizer might make sense. You can fully customize features, train models on your proprietary data, and integrate deeply with your internal systems, but be ready for high upfront costs, long development timelines, and ongoing maintenance responsibilities. Buying an Existing Solution On the other hand, buying a ready-made solution gets you up and running much faster. Many SaaS providers already offer medical summarization tools that are secure, compliant, and easy to integrate. While you may sacrifice some customization, you’ll save big on development costs, reduce your risk, and start seeing ROI much sooner. If you’re a large hospital network with deep pockets and long-term plans, building might be worth it. But for most healthcare providers, startups, and even many enterprises, buying or licensing an existing solution is often the smarter, safer, and more cost-effective path at least to start. Experience Faster Clinical Workflows Book a Free consultation Contact Us Real-World

Table of Contents What is a Domain-Specific AI Assistant? Think of a custom AI assistant as your company’s super-smart teammate, but trained just on your industry’s language, data, and challenges. Whether you’re in healthcare, finance, legal, or logistics, these assistants aren’t just pulling info from the internet, they’re tuned into your documents, your processes, and  context. That’s what makes them so powerful. Unlike generic chatbots, they give precise, relevant, and trustworthy answers. If you’re serious about building AI that actually works for your business, going domain-specific isn’t just smart, it’s essential. Why RAG Is the Right Approach for Specialized Domains? Let’s be real, if your AI assistant is making up answers in a regulated or high-stakes industry, it’s doing more harm than good. That’s why RAG (Retrieval-Augmented Generation) is such a smart move. It connects a powerful language model with your actual business data, think SOPs, compliance docs, internal FAQs. So every answer is backed by something real. No more hallucinations, just accurate, brand-aligned responses. If you’re building an AI assistant for healthcare, finance, legal, or enterprise ops, RAG isn’t optional, it’s essential. The Core Architecture of Our RAG-Based Assistants So, how does a RAG based AI assistant actually work behind the scenes? It starts with a user query  that gets embedded and compared against a vector database (like ChromaDB or FAISS) filled with your company’s documents. The most relevant pieces are retrieved and passed into the language model (LLM) along with the query. That combo helps the model generate accurate, context-aware answers and not guesses. This architecture lets us build assistants that don’t just sound smart, but actually aren’t smart,  because they pull insights directly from your trusted data sources every time they respond. Choosing the Right Models, Embeddings, and Tools When it comes to building a high-performing RAG assistant, your choice of tools can make or break the experience. We carefully select the right LLMs (like GPT-4o, Claude, or Mistral) based on the domain, response quality, and budget. For embeddings, we rely on OpenAI, Hugging Face, or custom-trained ones that align better with niche vocabularies. On the tooling side, frameworks like LangChain, LlamaIndex, and vector stores like ChromaDB or Weaviate help us stitch everything together efficiently.  Each choice impacts accuracy, latency, and cost, so we balance flexibility with production-readiness every step of the way. Infrastructure and Hosting: Where Your RAG Assistant Lives (and What It Costs) Once your AI assistant is up and running, where you host it can seriously affect cost, speed, and compliance. For most businesses, cloud platforms like AWS, GCP, or Azure offer flexible, scalable hosting, especially when your assistant needs to handle spikes in traffic or multilingual queries in real time.  But if you’re in a region with strict data laws (like Saudi Arabia, UAE, or the EU), on-premise or region-specific hosting may be non-negotiable. Hosting a RAG stack means provisioning GPU-enabled servers, securing persistent storage, and sometimes setting up Kubernetes clusters or serverless APIs. It’s not just about uptime , it’s about performance, compliance, and long-term scalability. Maintenance & Support: Keeping Your AI Assistant Sharp Over Time Building a custom RAG-based AI assistant is just the start, keeping it accurate, relevant, and reliable is an ongoing process. As your business evolves, new documents, terms, and edge cases will pop up, and your assistant needs to keep up. That’s where continuous fine-tuning, retrieval evaluation, and feedback loops come in. You’ll also want to monitor for hallucinations, especially in low-resource languages where models can drift. For sensitive industries, a human-in-the-loop (HITL) setup ensures the AI escalates anything unclear or risky. Think of maintenance not as a cost, but as a safeguard, one that keeps your assistant trustworthy and useful at scale. Evaluating Accuracy, Relevance, and Explainability It’s not enough for your AI assistant to sound smart, it needs to be right. Evaluating a RAG system means digging into how accurate, how relevant, and how explainable its answers are. We use metrics like precision@k, retrieval overlap, and human-graded QA scores to see if the assistant is pulling the right context and delivering useful responses. For regulated industries, explainability becomes critical, your AI needs to show why it gave an answer and where the information came from. We build feedback loops and dashboards so you can track performance and continuously improve. In short: if you can’t measure it, you can’t trust it! Let’s explore how we can tailor one for your industry. Book a free strategy session Contact Us Real-World Use Cases: How We Deploy RAG Assistants in the Wild RAG-powered AI assistants really shine when tailored to real business workflows  and we’ve deployed them across industries. In healthcare, assistants can retrieve medical protocols, summarize patient histories, and support triage conversations, all while staying HIPAA-compliant. In legal, they pull case law, draft clauses, and flag risky terms using internal precedents. In ecommerce, they power multilingual product search, handle customer queries, and support live order tracking. What ties it all together? The assistant isn’t “guessing”, it’s retrieving verified info from your content and speaking in your brand’s tone. That’s where the magic (and ROI) happens. Security, Privacy, and Access Control: Protecting Your Data with RAG When you’re building an AI assistant that taps into sensitive business data, security can’t be an afterthought. With RAG systems, we implement strict access controls, ensuring the assistant only retrieves and responds with documents the user is authorized to see. That means integrating role-based permissions, API key management, and even document-level encryption when needed. For industries operating under GDPR, HIPAA, or GCC regulations, we support region-specific hosting, audit trails, and enterprise-grade compliance workflows. Bottom line: your assistant should be as secure and private as the data it was built on and we make sure it is. From POC to Production: What It Takes to Scale a RAG Assistant Launching a prototype is easy, but scaling a domain-specific AI assistant into production? That’s where most teams hit a wall.  We guide our partners through every phase: from building a lean proof of concept

Table of Contents Introduction Ever chatted with a support bot that only speaks English when you really needed help in Arabic or Spanish? That’s exactly the kind of gap multilingual AI agents are built to fix. These smart assistants don’t just answer questions, they understand and respond in multiple languages, making it easier for businesses to connect with customers across the globe. Whether you’re running a global e-commerce brand, scaling a SaaS product, or just want to offer better support in local languages, multilingual AI agents can make it happen. In this article, we’ll break down what they are, how they work, and what it actually costs to build one in 2025. Tech Stack & Cost Breakdown for Multilingual AI Agent (2025) Language Model To build a multilingual AI agent, you’ll be stitching together a few key technologies and the choices you make can greatly affect the cost. At the heart of it is your language model. Options like GPT-4o, Claude, or Gemini can handle multiple languages out of the box, and their pricing usually depends on usage, think around $5 to $30 per million tokens. If you’re looking to keep costs low or need more control, you could go with open-source models like LLaMA 3 or Mistral, though you’ll need to host them yourself, which might run you $500 to $2,000 a month depending on the scale. Translation/ NLU For translation needs beyond what your base model handles, APIs like DeepL or Google Translate do the trick, usually costing around $20 to $60 per million characters. Conversational Engine You’ll also need a way to manage the conversation flow, especially if you’re switching between languages or handling fallback logic. Tools like Rasa, LangChain, or Dialogflow are great for that and can be free or priced modestly depending on features. Front-end, Voice and Cloud Hosting Your front-end, maybe a React app or a simple widget, won’t cost you much aside from dev time. But if you want voice capabilities, tools like ElevenLabs or Whisper can add another $15 to $200 a month. And of course, everything needs to live somewhere, so cloud hosting (AWS, Azure, or GCP in the Saudi region if you’re focused on data compliance) could range from $300 to $2,000+ depending on traffic. All in all, if you’re building a solid mid-scale AI agent that supports multiple languages and has live translation, voice, and integrations with your CRM or helpdesk, you’re probably looking at a monthly cost between $1,100 and $3,700. Pretty reasonable when you compare it to hiring and training multilingual support teams for every region. Guide on what to choose in accordance to cost models: SaaS-based multilingual AI agent If you’re just getting started or testing the waters, a SaaS-based multilingual AI agent is the fastest and most affordable route. You can plug into tools like GPT-4o, use a basic translation API, and deploy a simple front-end widget, all for around $1,500 a month.  It’s perfect for startups or businesses needing support in 1 to 3 core languages without worrying about infrastructure. Open-source self-hosted setup Now, if your team has some technical muscle and cares about data privacy or cost control, an open-source self-hosted setup might be the way to go. You can run open-source models like LLaMA or Mistral, use free translation tools like SeamlessM4T, and host everything on your own servers. It takes a bit more engineering effort but offers a lot more flexibility, usually costing around $1,500 to $2,200 per month. Enterprise-level multilingual agents On the other hand, if you’re scaling across regions with a full-blown support strategy, enterprise-level multilingual agents give you the horsepower you need. These setups often include advanced AI models like GPT-4o or Claude, voice support with ElevenLabs, deep CRM integrations, and human-in-the-loop monitoring. Development of Enterprise-level multilingual AI agents cost around $20,000 to $30,000 rendering secure data, advanced language support and robust security protocols. In this case, the AI agent is trained on your own data, making it fully tailored to your business environment and use cases. So, the monthly cost for enterprise custom models is lesser than SAAS, which is around 1000 to 1200 USD. Key Cost Drivers to Consider 1. Scope and Use Case Chatbot vs. Autonomous Agent A basic chatbot answers predefined questions with scripted replies, cheaper and quicker to build. An autonomous AI agent, however, can handle dynamic workflows, access external tools, and reason through complex scenarios. Autonomous agents require advanced orchestration, larger models, and more computers making them significantly more expensive. Static FAQs vs. Real-Time Conversation Static FAQ bots fetch answers from a database or a spreadsheet with minimal compute cost. Real-time conversation bots use large language models (LLMs) that process intent, maintain context, and generate responses on-the-fly, incurring API/token costs per message. Industry-Specific Complexity Healthcare needs high precision, compliance (HIPAA, etc.), and medical NLU. Finance may require real-time integration with databases, strong audit trails, and fraud detection. E-commerce often needs multilingual product lookups, cart integration, and personalized recommendations. Make AI truly conversational across every language, every market. Deploy your multilingual AI now. Contact Us 2. Number of Languages Supported Popular Language Combinations, Regional Dialects & Cultural Adaptation Multilingual Agents Support English, Arabic, Spanish, and Mandarin covering a massive global audience, but each language adds cost in UI translation, model tuning, QA, and voice support. For languages like Arabic or Hindi, dialect variation (e.g., Gulf Arabic vs. Egyptian Arabic) means generic translation fails. You’ll need dialect-aware prompts or fine-tuned models raising cost. Cost Per Language Integration Each new language adds UI updates (RTL, translations), Voice support (TTS/STT), Translation/model testing and Culture-specific moderation. Thus, Estimated cost per language: $1,000–$3,000+ depending on depth. 3. Language Translation & NLU Layer Built-in Multilingual Models vs. APIs and NER (Named Entity Recognition) Cost Models like GPT-4o are multilingual, but accuracy drops in complex domain-specific cases. You may need fallback APIs like DeepL, Google Translate, or custom-trained NLU for better intent recognition. Accurate NER is critical in languages like Arabic, Hindi, or Chinese. Off-the-shelf tools often miss context

Table of Contents Introduction Think of LLMs as AI systems that can read, write, and understand language almost like a human would. Why are people fascinated? LLMs are fast changing the way businesses work. Tools like GPT-4, Claude, and Gemini are already being used to handle everything from writing reports and answering customer queries to summarizing long documents and making internal knowledge easier to access. But here’s the thing, not every use case is a good fit. To get the most out of LLMs, it’s all about finding the right opportunities where automation makes sense, risks are low, and results are easy to measure. The Reality check! LLMs like GPT-4 and Claude have made waves across industries, but not every use case is a fit. For enterprises, the real question isn’t “Can we use an LLM?” it’s “Should we?”. So, what makes a great LLM use case in the enterprise? Arenas where LLMs shine! Processes that are Text based LLMs excel where there’s a lot of unstructured or semi-structured texts like customer service logs, contracts, emails, reports, and policies. Thus, LLMs become ideal in processes like, customer support email drafting, internal knowledge base search assistants, contract clause summarization and meeting note generation. Repetitive tasks Firstly, look for tasks where teams spend hours reading, writing, or tagging like report generation, data classification, or help desk replies. These repetitive, text-heavy workflows are perfect for LLM-driven automation, saving time while boosting productivity with minimal disruption. Low-Risk, High-Cognitive Load Tasks It is important to understand that we can consider LLMs to be effective, only if the use cases don’t replace critical human decisions, they reduce cognitive load. So, the best fit scenarios under this can be drafting marketing content, generating proposals or documentation and suggesting next-best actions in sales or CRM tools. Embedded in existing workflows and tools In this fast moving world, LLMs shouldn’t be a new tool people must learn. The best use cases embed AI into tools teams already use CRMs, email clients, ticketing systems, etc.  It is ideal for AI copilots in customer support dashboards, auto-tagging and routing inside ticketing systems and on-the-fly answer generation in internal search. It is considered effective as it involves minimal disruption and maximum option. Closed Data Loops for Continuous Learning Strong use cases create feedback loops that let the system improve over time. This includes thumbs up/down, edits, or structured validation. It is ideal for AI knowledge assistants, internal policy search engines and legal clause suggestions with human review. Human-in-the-loop refinement keeps the model aligned and trustworthy. Source: Arize AI Bad use cases Knowing the bad use cases is as important as knowing the good ones. We must be mindful of where we deploy LLM. LLMs can be super god machines, but not everywhere.  For example, real-time decisions with legal/financial risk, tasks requiring deep domain reasoning or common sense, use cases where human verification is not possible and deployments with unclear data privacy or compliance policies. Here’s the checklist to evaluate an LLM use case in your business: Question Answer Yes/No Is it text-heavy or unstructured?           ✅ Can part of it be automated safely?           ✅ Will it save time or improve outcomes?           ✅ Can it integrate with existing tools?           ✅ Can results be measured or reviewed?           ✅ If you are checking at least three, you are good to go. Transform your workflows with targeted LLM integration Book your Consultation Contact Us LLM Use Cases for Different Enterprise Departments Now who doesn’t prefer an organized, flawless workflow? LLM does exactly what you’re looking for. Here’s the breakdown of usage of LLM in different departments. Sales Generating tailored pitch decks: LLMs can efficiently help you analyze client data, industry reports, and sales history to create highly personalized pitch decks, saving sales reps hours of manual work while improving relevance and conversion rates. Drafting follow-up emails: AI can auto-generate concise, context-aware follow-ups based on CRM notes and previous interactions, increasing outreach speed without sacrificing personalization. HR Screening resumes and summarizing applications: You can leverage LLMs to quickly parse large volumes of resumes and generate candidate summaries aligned with job descriptions, reducing time-to-hire and recruiter workload. Writing internal communications and policies: No more worries about policy updates and company announcements, as LLMs assist HR teams in drafting clear,compliant communication tailored to different employee groups. Legal Summarizing legal documents and extracting key clauses: A huge amount of time can be saved as LLMs help legal teams sift through long contracts and surface relevant clauses, risks, and compliance flags in seconds. Assisting with contract review: LLMs can highlight anomalies, suggest standard wording, and cross-check contract terms against organizational templates and prior versions. Customer Support Auto-drafting responses for common inquiries: LLMs can draft helpful, on-brand responses to FAQs, allowing support agents to respond faster while focusing on more complex tickets. Always remember that quick responses also mean satisfied customers. Categorizing and tagging tickets: AI can automatically classify support requests by topic, priority, or department, improving response time and routing accuracy. This way, the work gets organised and effective at the same time. Finance Generating periodic reports: LLMs can pull data from finance systems and generate summaries, dashboards, or narratives for reports like monthly performance, audit readiness, or budget reviews. Answering internal policy-related questions: Employees can ask natural language questions about finance policies or procedures and get immediate, AI-generated answers that are referred from official documentation. Thus, nothing is promised off the plate. Real Examples of successful LLM Applications in Enterprises A global consulting firm saved 30% of time in proposal writing By implementing GPT-powered templates that auto-generate proposal drafts based on deal history, client objectives, and service offerings, the global consulting reduced manual content creation and allowed consultants to focus on personalization and strategy. A healthcare company used LLMs to automate medical policy search This easily enabled physicians and staff to retrieve relevant documentation in

Table of Contents Introduction Enterprises are widely adopting AI email co-pilot for obvious benefits such as reduced operational costs, cutting humungous manual time and hence increasing productivity. These intelligent assistants leverage natural language processing (NLP) to summarize long email threads, suggest context aware replies, automate scheduling, and enforce compliance policies. By integrating with CRMs, calendars, and internal tools, AI copilots enable seamless workflows and data-driven insights. Third-party integration possibilities with AI Email Co Pilot Integration methods are the backbone of a powerful AI Email Co-Pilot system. They allow the co-pilot to seamlessly connect with email platforms, calendars, CRMs, helpdesks, and productivity tools enabling it to access, interpret, and act on data across systems in real time. These integrations empower the AI to generate context-aware replies, automate workflows, extract insights, and improve response times. Whether through secure authentication protocols like OAuth, real-time event handling via webhooks, or no-code automation platforms, these methods ensure that the AI co-pilot fits effortlessly into existing business environments while amplifying efficiency and reducing manual effort. CRM Integrations By integrating AI Email Co Pilot, enhance sales, customer engagement by syncing contact and deal data. You can auto log email threads to lead records, enrich the replies with CRM data, and also automatically change the sequences, follow-ups and deal stages. In the case of Gong/Outreach/Apollo.io, you can summarize and do email interactions and suggest follow-ups based on past interactions. Calendar & Scheduling Tools By integrating Google Calendar / Microsoft Outlook / Calendly, you can automate meeting scheduling and reminders contextually with integration. Also, offer smart time suggestions based on context, auto-send calendar invites post-email agreement and block meeting slots from email body parsing. Project Management Tools By integrating Asana / Trello / Notion / ClickUp / Monday.com, you can convert email content into actionable tasks. Also, parse email into task with a due date, add comments and tags from conversation context and sync email updates with task progress. In the case of Jira, create tickets from email bugs and update status or comments via email reply. Communication & Collaboration Tools Centralize email context into team workflows with Slack / Microsoft Teams. Also, summarize and send email threads as Slack messages, tag teammates for action within email summaries and route replies to chat if marked urgent. File Storage & Cloud Tools Extract insights from attachments and manage documents, auto-upload and link attachments from email, summarize PDF reports or scans and alert users when updated versions are received with Google Drive / Dropbox / OneDrive. Also by integrating DocuSign / Adobe Sign, trigger signature workflows from contract emails and track document status within the inbox. Customer Support & Ticketing By integrating Zendesk / Freshdesk / Intercom with AI Email Co pilot, you can create support tickets from emails, use AI to auto-categorize and prioritize tickets and suggest KB article replies. ERP & Finance Tools You can now automate transactional workflows, generate payment reminders from email intent, auto-attach invoices or financial summaries and track approvals within threaded discussions by integrating SAP / Oracle / QuickBooks / Xero. Security & Compliance Get smarter with AI Email Co Pilot as it facilitates secure and policy-compliant email usage. With Proofpoint / Mimecast / Vade / Zscaler, you can scan and flag suspicious content, enforce encryption or redaction policies and audit email trails for legal or compliance needs. AI & Analytics Platforms Enable deeper personalization and insight, visualize email sentiment trends or response time, dashboards for email performance (sales, support, etc.) with Power BI / Tableau / Looker. With OpenAI / HuggingFace / Cohere, custom LLM integration for personalized replies and embed vector search for email content memory. More integration methods Integration Method Description Use Case Examples OAuth 2.0 Auth protocol for secure user access Connect Google, Microsoft, or CRM accounts REST APIs Web APIs for data exchange (JSON/XML) Fetch/send emails, calendar events, CRM data Webhooks Real-time event triggers to other systems Create task in Asana when email says ‘urget’ Graph API (Microsoft) API to access Outlook, Teams, OneDrive Read Outlook mail, sync Teams, manage calendar IMAP/SMTP Protocols APIs for Gmail, Calendar, Contacts Sync emails, send mail, check availability RPA Tools UI-based automation of manual workflows Connect custom or non-Google/Microsoft mail servers No-code Tools (Drag-code tools (Zapier, Make)) Drag-drop builders for fast setup Auto-copy content to ERP from email Features of an AI Email Co-Pilot To create a competitive AI email assistant, consider integrating the following features: Natural Language Processing (NLP): Enables the assistant to understand and generate human-like text. Contextual Email Summarization: Provides concise summaries of lengthy email threads. Smart Reply Suggestions: Offers context-aware response options to streamline communication. Calendar and Task Integration: Synchronizes with calendars to schedule meetings and set reminders. Voice Command Support: Allows users to dictate emails and commands using voice input. Security and Compliance: Ensures data privacy through encryption and adherence to regulations like GDPR. Estimated Cost to Build an AI Email Co-Pilot The development cost for an AI email assistant varies based on complexity, features, and team expertise. Basic MVP (Minimum Viable Product): $20,000–$50,000 Includes essential functionalities like email summarization and basic NLP integration. Mid-Tier Application: $60,000–$150,000 Adds features such as smart replies, calendar integration, and enhanced NLP capabilities. Advanced Enterprise Solution: $200,000–$500,000+Incorporates custom AI models, real-time analytics, multi-platform support, and robust security measures. Feature-wise cost breakdown for building an AI Email Co-Pilot 1. NLP-Powered Email Summarization(H3) Cost: $8,000 – $20,000 Reason: Requires fine-tuned transformer models (e.g., GPT, T5) to distill long email threads contextually. 2. Smart Reply Suggestions(H3) Cost: $6,000 – $15,000 Reason: Context-aware generation with tone-matching, user history modeling, and fallback options for sensitive responses. 3. Calendar & Meeting Integration(H3) Cost: $4,000 – $10,000 Reason: Integration with Google Calendar, Outlook, scheduling APIs; includes timezone handling and conflict resolution. 4. Security, Privacy & Compliance Layer(H3) Cost: $10,000 – $25,000 Reason: End-to-end encryption, role-based access, data anonymization, GDPR/HIPAA/SOC2 compliance automation. 5. Voice Command & Dictation(H3) Cost: $5,000 – $12,000 Reason: Uses speech-to-text engines (like Whisper or Google STT), with NLP context parsing for commands. 6. CRM & Workflow

Table of Contents Introduction In this current age of AI, Saudi Arabia is moving ahead of other countries, embracing Agentic AI and Generative AI not just as technology upgrades, but as a bold step toward a smarter and diversified future.  From powering smart cities to improving healthcare and digital security, these AI tools are being tailored to local needs right down to Arabic-language models. With strong global partnerships and a clear national vision, the country is carving out its place as a rising force in the AI world. Where and how Agentic AI and Gen AI is used in Saudi Arabia Generative AI in Personalized Healthcare & Education From AI-generated medical insights at Seha Virtual Hospital, world’s largest AI-powered telehealth network, to adaptive learning content in schools, Generative AI is transforming key sectors by delivering real-time, human-like intelligence tailored to individual needs. Healthcare is hence becoming error free. Autonomous Systems for Smart Governance Saudi Arabia partnered with the U.S. and they maintain a long standing strategic partnership that spans energy, defense, trade and technology. Saudi Arabia’s smart city projects like NEOM and The line, aren’t just using isolated AI systems, they’re implementing multi-agent architectures, where autonomous agents collaborate to manage energy, traffic, and emergency services in real time. These MAS systems include: Reactive agents for immediate response (e.g., fire detection, rerouting traffic). Deliberative agents for goal-based reasoning (e.g., optimizing water grid flow). Learning agents using federated reinforcement learning to improve over time. Arabic Large Language Models for Cultural & Economic Growth Through national AI initiatives like Humain, Saudi is developing Arabic-centric LLMs that power intelligent assistants, enterprise tools, and digital public services localizing GenAI for economic impact and digital sovereignty. To handle the diversity of Arabic dialects while remaining compute-efficient, local AI labs are training Mixture-of-Experts LLMs. These selectively activate subnetworks based on input routing, achieving: Better dialect generalization (Egyptian, Najdi, Levantine, etc.) Lower latency for inference in edge devices Cost savings in fine-tuning across multiple downstream tasks (translation, Q&A, document parsing) Cybersecurity and Infrastructure Resilience Agentic AI is deployed to detect and respond to cyber threats in real-time, ensuring robust digital security for government, finance, and enterprise systems. In national cybersecurity operations, GNNs are being used to model network traffic as a dynamic graph, enabling: Detection of complex Advanced Persistent Threat patterns. Correlation of seemingly unrelated anomalies (e.g., login time + endpoint behavior). Real-time adaptation without labeled data. Saudi CERT and financial institutions are moving toward zero-trust architectures enriched with GNN-driven anomaly scoring. Plan for Saudi Arabia’s AI transformation today Book A Consultation Now Contact Us AI-Powered Port Operations & Vessel Traffic Management (VTM) Saudi ports like King Abdulaziz Port (Dammam) and Jeddah Islamic Port are deploying AI systems for real-time: Berth scheduling using reinforcement learning. Vessel arrival forecasting using LSTM + Kalman filtering. Automated tug assignment and route optimization. These systems integrate AIS (Automatic Identification System), weather APIs, and historical traffic logs to predict delays and minimize idle time. Satellite and AI Convergence for Desert Agriculture Saudi Arabia is applying hybrid AI models that combine satellite remote sensing (from SAR and hyperspectral imagery) with transformer based time-series forecasting to, Detect underground water sources Monitor crop health in vertical and desert farming Optimize yield under extreme conditions These models are trained using few-shot learning due to data scarcity, combined with Bayesian uncertainty estimation to ensure reliability under variable environmental conditions. Agentic AI & Gen AI in Saudi Arabia Book your slot now! Contact Us

Table of Contents Introduction We are excited to announce that Nyx Wolves has been included in Techreviewer’s list of the top software development companies for startups in 2025. This recognition is a testament to our unwavering commitment to innovation, quality, and client success in the fast-evolving world of technology. Our Journey: From Vision to Reality Since our inception in 2018, we at Nyx Wolves have dedicated ourselves to transforming ambitious ideas into digital realities. As a leading AI and technology consultancy headquartered in Singapore, our team has delivered over 100 websites and applications across a broad range of industries, including Ed-tech, Insurance Tech, Food & Beverages, Hospitality, Real Estate, Education, FinTech, and E-Commerce. Our approach is rooted in empathy and partnership. We take the time to understand the unique challenges our clients face, enabling us to craft tailored solutions that drive real business value. Being recognized by Techreviewer.co validates our methodology and reinforces our position as a trusted technology partner for startups aiming to leverage digital innovation for growth. Empowering Startups with Comprehensive Solutions We understand that startups operate in a dynamic and competitive landscape. That’s why we offer a comprehensive suite of services designed to empower startups at every stage of their journey: Digital Transformation: We provide AI/ML-based solutions, ERP software, CRM, business process automation, and cloud solutions to streamline operations and enhance efficiency. Product Development: Our expertise in SaaS products, DevOps, CI/CD pipelines, testing, quality assurance, and major cloud platforms (Azure, AWS, GCP) helps startups accelerate their time to market. Web Design & Development: Leveraging technologies like HTML, CSS, JavaScript, WordPress, ReactJS, Node.js, Laravel, PHP, Angular, MySQL, and MongoDB, we create engaging and robust web experiences. Mobile Application Development: Our proficiency in RESTful APIs, Flutter, Android, iOS, and Firebase allows us to deliver high-quality mobile applications tailored to user needs. UI/UX Design & Branding: Through Figma, Adobe XD, prototyping, animation, logos, and creative assets, we help startups build a memorable and impactful brand identity. Proven Success and Industry Validation Our inclusion in Techreviewer’s list is a reflection of our proven track record. We have delivered more than 20 complete end-to-end software products that have successfully attracted venture capital funding. Our ability to rapidly transform ideas into MVPs and scale applications to support 100,000+ users has made us a preferred partner for startups seeking to make a significant impact. Let’s build something great together, start here Book A Consultation Now Contact Us Pioneering the Future with AI and Advanced Technologies Looking ahead, we remain committed to staying at the forefront of technological advancement. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) is opening new possibilities across sectors such as manufacturing, healthcare, and HR. In 2025, businesses that harness LLM automation are gaining a competitive edge, and we are proud to help our clients implement scalable, tailored LLM solutions that drive efficiency and growth. Global Reach, Local Expertise With offices and partner locations in Singapore, UAE, USA, UK, and South Africa, our diverse team of more than 50 professionals brings a global perspective to every project. We combine deep expertise in Artificial Intelligence, Digital Transformation, Web and Mobile Development, IoT, Product Development, Computer Vision, Chatbot Development, Generative AI, Deep Learning, Machine Learning, Edge AI, and AR/VR Development to deliver world-class solutions. Our Commitment to Clients As a team, we are immensely proud of this recognition and grateful for the trust our clients place in us. This milestone is not only a reflection of our technical capabilities but also of our dedication to client satisfaction and long-term success. We believe technology should be an enabler, not a barrier, and we strive to turn every idea into a powerful digital solution. Looking Forward This achievement motivates us to continue pushing boundaries and delivering bespoke AI products and automation solutions trusted by billion-dollar enterprises. We look forward to new opportunities and challenges as we help startups and enterprises alike achieve their technology goals and exceed expectations. For startups seeking a reliable, innovative technology partner, Nyx Wolves offers the expertise, creativity, and dedication needed to transform visions into reality. Schedule a Free Intro Call Book your slot now! Contact Us