It literally took decades for technologies like the PC and the internet to reach billions of people, but generative AI reached 53% of the world's population in just three years. The Stanford AI Index 2026 reports that AI use among organizations increased from 78% to 88% in a single year, showing how quickly LLMs have moved into everyday business work.
Implementing AI and putting it to work at scale are two different challenges. The report shows that AI agent use is still in the single digits across most business functions. Many companies use AI, but only a few are using AI agents for regular business work at scale.
If you're looking for ways to use LLMs in your product or business, the biggest challenge is choosing the right use case and building it well. At GoML, our Applied AI FDEs help clients choose and test the right models, identify use cases worth building, and take ideas to a 4-day POC and then to production. This guide covers LLM use cases across different industries and job roles.
What is a Large Language Model (LLM)?
A large language model (LLM) is an AI system trained on large amounts of text to understand and generate language. It can answer questions, write content, translate languages, and write code.
Most LLMs use transformers, which understand relationships between words and context and use that to predict the most likely next word. During pre-training, LLMs learn grammar, language patterns, and general information from text without human labeling. Unlike older training methodologies that process text one word at a time, transformers process multiple parts of a sequence at once, making LLMs better at contextualizing content. .
How LLMs are different from traditional AI
Traditional AI and large language models (LLMs) can both help businesses automate work, but they work differently. Traditional AI usually follows fixed rules or learns from labeled data to perform a specific task, while LLMs learn from large amounts of text and can handle many different language tasks using one model.
What work can LLMs handle?
LLMs started as text tools, but they can now read, write, analyze, code, and take action when connected to business systems. Here are the main tasks they handle and how businesses use them.
1. Content generation and rewriting
LLMs can create new content or rewrite existing text quickly.
- Writing: Draft emails, blog posts, product descriptions, reports and documentation.
- Rewriting: Change the tone, style or length of existing text.
- Personalization: Adapt messages for different customers, roles, or markets.
Example: Ojje used AI for story generation to alter its publishing time by 98%.
2. Summarization
LLMs can turn long documents and conversations into shorter summaries.
- Summarize contracts, research papers, financial filings and policy documents.
- Turn meeting and call transcripts into action items.
- Create short summaries of lengthy reports.
Example: Patient7 improved clinical documentation with AI medical transcription and real-time SOAP notes.
3. Text analysis and data extraction
LLMs can turn unstructured text into structured data that businesses can use.
- Classification: Sort support tickets, emails or documents by topic or urgency.
- Sentiment analysis: Identify positive, negative or neutral feedback.
- Information extraction: Pull names, dates, amounts, and clauses from documents.
Example: Ledgebrook used AI for liability code extraction from insurance documents and achieved 95% classification accuracy.
4. Question answering and knowledge search
LLMs can search company knowledge and answer questions in plain language.
- Answer employee and customer questions.
- Search internal wikis, policies and document libraries.
- Use retrieval-augmented generation (RAG) to answer questions using company data.
Example: Ledgebrook's underwriting chatbot cut document retrieval time by 70%.
5. Conversational AI
LLMs can power chatbots and virtual assistants that handle multi-turn conversations.
- Answer customer support questions.
- Guide users through onboarding, troubleshooting or bookings.
- Support internal help desks and learning assistants.
Example: We built a conversational AI chatbot for Miden, a modern issuer processor, that reduced its support workload by 58%.
6. Translation and localization
LLMs can translate content while keeping its meaning and tone.
- Translate product content, support articles and documents.
- Adapt content for different markets.
- Support multilingual customer service.
Example: Lokal used Gen AI to summarize news in local languages, helping it serve readers in the languages they speak.
7. Coding and development
LLMs can help developers write, review, and improve code.
- Write code in Python, JavaScript, SQL and other languages.
- Find bugs, explain errors and suggest fixes.
- Generate tests and documentation.
- Turn business questions into SQL queries.
Example: At GoML, AI coding is a part of how we form solutions for our clients. Our engineers use AI Matic, which is our in-house platform that generates production-ready code from proven blueprints, alongside AI coding agents such as Claude Code. We follow spec-driven development (SDD), defining what the system should do before coding begins so that agents can build against a clear specification. Over the past six months, AI Matic has been used in more than 95% of our AI builds which supports delivery of 4-day POCs to 12-week production systems. This year we have deployed more than 40+ AI systems into production.
7. Software testing and bug fixes
LLMs can help teams test software faster and catch problems before code goes live.
- Find bugs, explain errors, and suggest fixes.
- Generate test cases and review code for common issues.
- Create technical documentation.
Example: We helped DevPlaza use AI agents to review pull requests, identify code vulnerabilities, and suggest improvements to software testing.
8. Reasoning and problem-solving
Reasoning models can work through multi-step problems and compare different options.
- Break down business, math, and logical problems.
- Compare options against specific criteria.
- Support planning and analysis.
Example: Bosch boosted decision-making speed by 60% with a financial analysis AI copilot.
9. Multimodal tasks
Many LLMs can work with text, images, audio, and video.
- Read charts, scanned documents, screenshots, and diagrams.
- Transcribe and analyze calls and meetings.
- Answer questions about images and visual content.
Example: AI for retinal disease detection increased triage speed by 85% for Asia's second-largest eyecare chain.
10. Agentic workflows
When connected to tools, APIs, and business systems, LLMs can complete work instead of just answering questions.
- Break a goal into steps and carry them out.
- Update records, route work, and create actions.
- Pull live data from systems.
- Spin up or call other agents for specialized tasks.
Example: Dairyland Power uses a GoML-built AI powered work order automation that reviews and processes work orders uploaded as PDF or Word files, boosting efficiency by 70%.
Leading LLMs in 2026
Some LLMs perform better at complex reasoning and coding. Others are better at audio and video, offer lower costs, or can be hosted on your own servers. Closed models such as Claude, GPT, and Gemini still lead in overall performance, while open-weight models such as DeepSeek, GLM, and MiMo offer strong performance at a lower cost.
Types of LLMs
LLMs fall into two main types, based on how you access and use them.
- Proprietary (closed) models run on the provider's own servers and you can use them through an API or app. You can't see or change the model itself. The provider handles infrastructure, updates, and safety features, so teams can get started quickly. These models usually lead on convenience and breadth of capabilities, but they come with ongoing usage costs and limited control over token usage and inference residency.
- Open-weight models let you download the model and run it on your own servers or private cloud. You get full control over your data and can fine-tune the model for your needs, which makes them a strong fit for regulated industries and high-volume workloads. The trade-off is that you need the right hardware and skills to host and maintain them.
Many businesses now use both: proprietary models for broad, open-ended work and open-weight models for routine, data-sensitive work that needs to be performed at scale.
Top LLM providers 2026
These are the companies building the most widely used LLMs today.
Different models have different strengths
All the models have different capabilities and what they built to do sets them apart. Some are better at complex reasoning and coding, while others focus on speed, lower costs, multimodal tasks, or running on your own servers.
- Reasoning: Some models handle complex coding, analysis, and planning well.
- Speed and cost: Smaller models can respond faster and cost less, making them a good fit for simple, high-volume tasks.
- Multimodal tasks: Some models can work with images, charts, documents, audio, and video along with text.
- Agentic work: Many models can use tools, browse the web, and complete multi-step tasks.
Propr ietary models such as Claude, GPT, and Gemini still perform strongly across many tasks, while open-weight models such as DeepSeek, GLM and MiMo can offer strong results at a lower cost.
Top LLMs ranked by intelligence
GoML tracks these models and tests them on real enterprise AI client work. We have also listed the leading LLMs based on the Artificial Analysis Intelligence Index, along with what each model does best based our assessments.
How to choose the right LLM for your use case
Choose the right LLM according to your needs, not the model everyone is talking about. Define your requirements, compare models against them by testing their performance with real data before deciding.
Do not consider model at first, begin with the task
Before checking any leaderboard, answer three questions:
- What should the model do? Answer FAQs, summarize reports, extract data from forms, write code, or handle a multi-step agent? Each task needs different model capabilities.
- How complex is the task? A simple FAQ bot needs less reasoning than a tool that reviews contracts or supports complex decisions. Using a top-tier model for a simple task can also increase costs without adding much value.
- What type of data will it handle? Text, scanned documents, images, audio, or video? This can quickly narrow down your model choices.
Set you requirements before comparing models
Use benchmarks to shortlist, then test with real data
Public leaderboards can help narrow dozens of models down to two or three. But a high score does not guarantee good results for your use case. A model may perform well on general tests but struggle with industry terms, document formats, schema, or customer questions.
The final choice should come from testing the models on your own work. Collect real examples from your workflow and define what a good answer looks like for each one. Run them through every shortlisted model and compare:
- Accuracy and completeness
- Latency
- Performance on edge cases
- Token and cost per activity
Use the same scoring criteria for every model. For larger test sets, an LLM can help evaluate responses, with human reviewers checking the results. Keep adding real failures to the test set so future model and prompt changes are tested against known issues.
Score each model side by side
Rate each shortlisted model from 1 to 5 on the factors that matter most to your use case, such as accuracy, speed, cost, privacy, and integration. Give higher weight to the factors that matter more. For a legal review tool, accuracy may count twice. For a high-volume support bot, cost and speed may matter more.
This process may show that no single model performs best across every category. That is normal. Many teams use more than one model, with a stronger model for complex tasks and a faster, lower-cost alternative model for routine work.
Test one use case first
When your model choice is made, built out the first iteration or prototype of your use case. Set clear goals such as time saved, accuracy, or faster results. If it meets your goals, you can expand its use.
At GoML, we adopt the same methodology with our clients. We define the ask, set the requirements, test a few models with real data, and then compare the results. This helps us choose a model based on actual results. After that, we move from a quick proof of concept to a pilot and finally to production.
The drawbacks with subjective model testing
Many teams test an LLM by trying a few prompts, reading the answers, and choosing the model that feels best. I get it, it's easy and we’re trained to form immediate impressions. The approach can be disastrous for picking LLMs for enterprise use cases. It may work for an initial check, but it is just not enough to choose an LLM for production scale. Here’s why.
The same prompt can give different answers
Traditional software is deterministic. The path from input to output is clear, programmed, and verifiable. But LLMs can give different outputs each time because their responses are based on probabilities.
Even small changes in wording or punctuation can affect the result. Model providers regularly release updates that can change how a model responds, so a prompt that worked well on a different model may behave differently on a newer (or older) model without manual testing catching the change. Prompt regressions are still not an established practice.
People judge answers differently
Most LLM tasks do not have one correct answer, so an individual may find a response clear and useful while another may not. Reviewers also prefer answers that sound confident or look well written even when the facts are wrong. And LLMs are very good at confidently sharing responses that are plain wrong.
Without the same scoring rules for everyone, test results can depend more on who reviews the answer than on the quality of the model.
A few prompts are not enough
Real users ask questions in many different ways. They may make typos, leave out details, ask vague questions, or have long conversations. Testing only a few prompts covers a small part of real use. This means the problems that appear in production can easily be missed during manual testing.
Manual testing takes too much time
Writing prompts, checking responses, and recording issues by hand takes time. Every new model or prompt change means repeating the same work. This can slow down testing and make it harder to catch problems before launch.
Hidden risks can be missed
Quick manual tests may not catch issues such as prompt injection, attempts to bypass safety rules, sensitive data leaks, biased responses, or confident but incorrect answers. These issues need planned testing to find them.
Benchmark scores are not enough
Public benchmarks can help compare models, but they do not show how a model will perform in your system. Your prompts, data, retrieval setup, and tools also affect the final response. A model with a high benchmark score can still fail when working with your documents, terms, or workflows.
LLM use cases in different industries
Every industry works with large amounts of information, much of it in language, from patient records and financial reports to contracts, support tickets, and policy documents. LLMs can help across industries, but their use differs by business needs: hospitals need accurate clinical notes, banks need reliable risk analysis plus retailers need fast customer support.
At GoML, we have delivered 100+ generative AI projects across healthcare, financial services, manufacturing, retail, education, SaaS etc. The strongest LLM use cases solve a clear industry problem rather than using AI for its own good.
This section covers practical LLM use cases across major industries, with real examples from our work.
LLM use cases in healthcare
Healthcare teams handle large amounts of information, including clinical notes, patient histories, medical records, and care plans. LLMs help process this information faster, reduce paperwork, and support better patient care. This gives clinicians more time to focus on their patients.
Longitudinal patient data analysis
Longitudinal patient data analysis means reviewing a patient's health records over time to see how their condition changes. AI helps by bringing records and clinical notes from different sources together, making it easier to understand a patient's health history. For Max Healthcare, GoML built a generative AI copilot that connects to the hospital's health record systems and maps each patient's medical history. So that doctors can track changes in a patient's health and spot diagnosis trend which helps them provide the right care early.
Patient health risk prediction
Patient health risk prediction means using patient data to identify people who may face serious health problems. AI helps by reviewing large amounts of medical history and flagging risks that may be missed during manual reviews. At Atria, up to 40% of high-risk cases were missed or flagged too late, so GoML built an AI platform for patient health risk prediction with a simple dashboard for clinicians. Atria can now predict and prevent patient health risks 50% better.
Medical transcription and SOAP note generation
Medical transcription and SOAP note generation means turning conversations between doctors and patients into structured clinical notes. AI can listen to these conversations and sort the details into the right sections automatically. For Patient7, GoML built an AI system for medical transcription and SOAP note generation that creates clinical notes in real time. This reduces the time doctors spend on documentation and gives them more time to focus on patients.
Radiology report generation
Radiology report generation means turning a radiologist's findings into clear, structured reports. AI speeds up this process by converting spoken dictation into organized reports, reducing manual typing. For NewVue, GoML built an AI assistant for radiology report generation that uses medical-grade speech recognition to create reports from dictation. This gave radiologists 2× visibility into their reporting.
Clinical intake automation
Clinical intake automation means digitizing and organizing the forms and documents patients fill out before care begins. AI reads unstructured intake documents and turns them into structured, editable forms automatically. For WizTherapy, GoML delivered a 4-week, HIPAA-compliant solution for clinical intake automation. This cut manual intake effort by 80% and sped up clinical onboarding.
Mental health support chatbot
A mental health support chatbot gives people a place to talk and get wellness guidance whenever they need it. It can respond naturally and suggest support based on each person’s needs. For Enrichly, GoML built an AI mental health support chatbot that combines conversations with personalized recommendations, making wellness support more accessible to young people.
Personalized pediatric nutrition planning
Personalized pediatric nutrition planning helps create meal plans based on each child’s health and dietary needs. AI can adjust recipes and food recommendations to suit these needs. For Heartful Sprout, GoML built an app for personalized pediatric nutrition planning that adapts recipes to each child’s medical conditions and dietary requirements, improving dietary consistency by 50%.
Value-based care settlement analysis
Value-based care settlement analysis involves reviewing healthcare contracts where payments depend on patient outcomes and care costs. AI helps simplify this by analyzing financial and clinical data to calculate settlements. For Paramean, GoML built an AI financial assistant for value-based care settlement analysis that helps teams manage complex settlements. This made settlement analysis faster and easier for Paramean’s teams.
LLM use cases in finance and insurance
Banks, insurers, and fintech companies process large amounts of transactions, reports, policies, and claims every day. LLMs help them analyze information faster, identify risks sooner, and provide customers with quicker, more personalized service.
Fraud detection and transaction monitoring
Fraud detection and transaction monitoring involves checking financial transactions for suspicious activity. AI speeds up this process by scanning large numbers of transactions and flagging unusual patterns in real time. For Miden, a modern issuer processor, GoML built an AI tool for fraud detection and transaction monitoring that flags risky transactions for analysts to review. This reduced fraud detection time by 82%.
Transaction classification
Transaction classification means sorting financial transactions into categories such as groceries, rent, and travel. AI automates this by reading transaction details and assigning the right category, even when descriptions are unclear. For GetAsset’s personal finance app, GoML migrated the system to Amazon Bedrock and built AI for transaction classification. This helped the app achieve 90% data accuracy.
Personalized financial advice
Personalized financial advice helps people make money decisions based on their income, spending habits, and financial goals. AI supports this by analyzing personal financial data and suggesting options suited to each user. For Kashvisor, GoML built a generative AI financial advisor that provides personalized financial advice using each user’s banking data. This gives users financial guidance tailored to their needs without requiring a human advisor for every interaction.
Financial report generation
Financial report generation involves turning accounting data into clear and accurate reports. AI can speed up the process by collecting relevant figures and preparing report drafts automatically. For AccountBridge, which helps nonprofits manage their accounting, GoML built generative AI software for financial report generation. This helped nonprofit teams spend less time preparing reports and made their accounting work easier to manage.
Natural language financial data querying
Natural language financial data querying lets users ask questions about financial information in plain English instead of writing code or waiting for data teams. AI interprets these questions, retrieves relevant data, and returns answers quickly. For Indus Valley Partners, GoML built AI hedge fund software for natural language financial data querying. This reduced data turnaround time by 85%.
DeFi investment assistance
DeFi investment assistance helps users compare opportunities in decentralized finance, where investment options and returns can change quickly. AI can analyze yield farming opportunities and explain the available options in simple terms. For The Connecter, GoML built an AI assistant for DeFi investment assistance that analyzes yield farming options. This helped increase user engagement by 72%.
Underwriting knowledge search
Underwriting knowledge search helps insurance teams find relevant information in policies, guidelines, and documents when assessing risk. AI makes this easier by understanding questions and retrieving relevant answers from documents. For Ledgebrook, GoML built an AI chatbot for underwriting knowledge search that helps underwriters find information quickly. This reduced document retrieval time by 70%.
LLM use cases in manufacturing
Manufacturers manage complex technical documents, supply chains, and operational data across teams and systems. LLMs help them understand this information, automate routine tasks, and make better decisions about product planning, quality checks, and daily operations.
Market intelligence for product planning
Market intelligence for product planning means studying market trends, demand, and competitors to decide which products to build next. AI makes this faster by collecting signals from many sources and turning them into clear insights. For Semtech, a semiconductor company, GoML built an AI system for market intelligence that analyzes market signals to guide product decisions. This helps Semtech make informed choices about which chips to design next.
PDF data extraction
PDF data extraction means pulling useful information out of documents like technical drawings, specs, and reports. AI handles this by reading complex PDFs and capturing the right data automatically, even from detailed technical layouts. For Podium Automation, which designs industrial control panels, GoML combined machine learning and LLM models to build a solution for PDF data extraction. This sped up data extraction across its design workflows.
Workforce analytics
Workforce analytics means studying employee data, like headcount, skills, and attrition, to make better people decisions. AI makes this easier by letting teams ask questions in plain language instead of building reports by hand. For Bosch, GoML built a conversational AI tool for workforce analytics that gives HR teams instant answers from workforce data. This delivered workforce insights 80% faster.
Semiconductor defect detection
Semiconductor defect detection means finding tiny flaws in chips during manufacturing before they reach customers. AI speeds this up by analyzing inspection images automatically and spotting defects that are hard to catch by eye. For Sigray, which builds X-ray inspection systems, GoML built an AI system for semiconductor defect detection that reviews X-ray images automatically. This helped automate the inspection process.
Order processing automation
Order processing automation means handling orders from the moment they arrive until they’re fulfilled, without manual steps. Agentic AI makes this possible by reading orders, checking details, and completing each step on its own. For StockyAI, which connects agricultural producers, distributors, and retailers, GoML built an agentic AI engine for order processing automation. This made supply chain operations easier to manage.
LLM use cases in SaaS
SaaS companies use LLMs to improve internal workflows and add smarter features to their products, helping them offer better user experiences, faster support along with new AI capabilities.
Knowledge search
Knowledge search helps users quickly find the right answers across large collections of documents and technical material. AI speeds this up by grasping what the user is asking and linking related details from many sources. For Tungsten Automation, GoML built a GraphRAG chatbot for knowledge search that handles detailed certification questions. This gave users rich, accurate answers at scale.
Customer onboarding automation
Customer onboarding automation helps new users get set up and start getting value quickly. Agentic AI does this by guiding users through every step and handling tasks for them along the way. For Experio Labs, GoML built an AI onboarding agent designed for consulting and professional services teams. This sped up onboarding for enterprise clients.
Conversational data insights
Conversational data insights let users ask questions about business data in everyday language and get answers right away. AI converts each question into the correct data query and presents the results in a clear way. For MyIntelliSource, GoML built a conversational data insights engine focused on income statement analytics. This allows business users to explore financial data without needing technical skills.
Report generation
Report generation turns raw research and data into polished reports that are ready to share. AI makes this quicker by collecting information, pulling out key points, and writing the first draft automatically. For Hiswai, GoML built an AI system for report generation that brings together insights from across the web. This made market intelligence gathering much simpler.
Sales signal intelligence
Sales signal intelligence identifies which accounts are most likely to buy based on how they behave. AI improves accuracy by reviewing large amounts of buyer activity and pointing out the best opportunities. For Kinetik, GoML built a gen AI platform for sales signal intelligence serving B2B technology companies. This raised sales signal accuracy by 60%.
Regulatory compliance guidance
Regulatory compliance guidance helps organizations understand and follow complex security and compliance requirements. AI makes this easier by reviewing regulations and turning them into clear, practical advice. For RISCPoint, GoML built gen AI software for regulatory compliance guidance inside its RADAR platform. This helps clients get compliance answers more quickly.
Threat classification
Threat classification groups security threats into known attack types so teams know how to act. AI speeds this up by reviewing threat details and mapping them to frameworks like MITRE ATT&CK automatically. For Rilevera, GoML built AI for threat classification within its security detection platform. This helped security teams categorize threats more accurately.
Software testing
Software testing checks code for bugs, gaps, and security weaknesses before it’s released. AI agents make this quicker by reviewing code, finding the cause of failures, and recommending better tests. For DevPlaza, GoML built AI agents for software testing across the full development lifecycle. This made DevPlaza’s software more reliable.
Decarbonization financial analysis
Decarbonization financial analysis helps businesses estimate the costs and savings of switching to cleaner energy. AI speeds up the work by researching available options, comparing costs, and preparing plans. For Ampere Financial, which helps small and mid-sized businesses reduce their environmental impact, GoML built an AI copilot for decarbonization financial analysis. This reduced the time spent on research and planning by 50%.
LLM use cases in education
Schools, EdTech platforms, and training providers use LLMs to personalize learning support and create educational content faster.
Personalized learning
Personalized learning adapts lessons, practice, and feedback to each student’s level and pace. AI supports this by tracking student performance and adjusting the next learning steps. For Junction Education, GoML built a personalized learning platform that tailors learning content to every student. This allows students to learn at their own pace with material suited to their needs.
AI-assisted coding education
AI-assisted coding education helps beginners learn programming by guiding them through real projects. AI makes this easier by turning learners’ ideas into working code they can test and understand. For AngelQ, GoML built a coding platform for kids with a safe, block-based environment. This helps children learn coding through fun, hands-on activities.
Educational content generation
Educational content generation involves creating stories, lessons, and reading material for learners in less time. AI helps by producing age-appropriate drafts that educators can review and publish. For Ojje, GoML built an AI system for story generation to support its interactive reading platform for children. This alters publishing time by 98%.
Career guidance assistant
A career guidance assistant helps learners understand different career options and the skills needed for each one. AI makes this guidance more personal by answering questions and recommending paths based on individual interests. For BlueRecruit, GoML built and scaled an AI career assistant that provides personalized guidance for careers in skilled trades. This helps users make informed decisions about their future careers.
LLM use cases in retail and e-commerce
Retailers and online stores need to help customers quickly and give them a more personal shopping experience. LLMs help shoppers find the right products, get quick answers to their questions, and receive offers and content based on their interests.
AI shopping assistant
An AI shopping assistant helps customers find suitable products through natural, two-way conversations. AI makes these recommendations more personal by understanding each shopper’s needs, style, and budget. For SeededHome, GoML built a conversational AI shopping assistant that helps customers choose the right furniture. This increased conversions by 40%.
Product search and recommendations
Product search and recommendations help shoppers find what they need across large product catalogs. AI improves this process by understanding what customers are looking for and suggesting relevant products. For Techatomic, GoML built an AI search and recommendation engine for a catalog containing more than 20 million products. This reduced search abandonment by 25%.
Personalized marketing notifications
Personalized marketing notifications send customers offers and updates based on their interests. AI supports this by analyzing customer behavior and selecting suitable messages and delivery times. For Goodiebag, GoML built AI for personalized push notifications on its marketplace. This helped increase customer engagement and sales.
Social media content analysis
Social media content analysis involves reviewing posts and creator content to understand what attracts attention and drives sales. AI speeds up this process by analyzing large amounts of content and identifying key themes, products, and trends. For SimplicityDX, GoML built generative AI for social media content analysis to support brands’ creator storefronts. This helps brands use creator content to improve the shopping experience.
Employee training
Employee training helps retail and service staff understand work processes and answer customer questions with confidence. AI supports faster training by providing instant answers and learning materials whenever staff need them. For HelloWash, GoML built an AI training platform for its support teams. This improved training efficiency by 60%.
LLM use cases in government and public sector
Government agencies manage large volumes of citizen requests, public records, and policy documents. LLMs help staff respond to queries faster, review documents and communicate with citizens in their preferred language.
Citizen grievance management
Citizen grievance management involves handling public complaints and requests, directing them to the right department, and helping resolve them quickly. AI speeds up this process by understanding complaints, sorting them by topic and urgency, and explaining the steps in simple language. For CPGRAMS, India’s public grievance system, GoML built a conversational AI grievance management engine. This helps citizens file and track complaints more easily while helping government agencies respond faster.
Constituent communication
Constituent communication involves responding to the large number of messages that government offices receive from the public. AI helps staff handle these messages faster by identifying the issue and drafting clear, accurate replies for review. For Civic, which builds communication platforms for congressional offices and state legislatures, GoML built an AI email response generator. This improved efficiency by 62%.
LLM use cases in legal services
Legal teams spend a lot of time reviewing contracts, reading case files, and searching through regulations. LLMs help them find important details faster, review large numbers of agreements, and meet legal requirements, while lawyers make the final decisions.
Contract analysis
Contract analysis involves reviewing agreements to identify key terms, obligations, and important details. AI speeds up this process by reading lengthy contracts and organizing their contents into structured, searchable data. For Plumbata, GoML built contract automation software that extracts and analyzes information from union agreements. This makes complex contract data easier and faster to review.
Due diligence document search
Due diligence document search involves finding important information across large collections of confidential deal and legal documents. AI makes this easier by understanding questions and retrieving answers from relevant documents. For Hadaly, GoML built an AI chatbot system for due diligence, with secure access controls based on each user’s role. This helped M&A teams complete due diligence tasks faster.
Legal compliance automation
Legal compliance automation involves checking that business activities follow the laws and rules that apply to them. AI helps simplify this process by reviewing requirements, assessing their complexity, and preparing compliant campaign setups. For Trymark Promotions, GoML built sweepstakes management software that manages legal compliance for promotional campaigns. This reduced setup time by 70%.
LLM use cases in media and content
Media companies and content platforms face pressure to produce, manage, and personalize content faster. LLMs support these tasks by drafting content, reviewing user posts, organizing media libraries, and tailoring recommendations to audience interests.
Social media content generation
Social media content generation involves creating posts that fit a brand’s voice and keep pace with ongoing conversations. AI speeds up this process by drafting relevant, timely posts that teams can review and publish. For Hemera, GoML built an AI tweet generator that creates engaging posts for the crypto community on X. This helps Hemera stay active and visible in a fast-moving space with many competing voices.
Local-language news summarization
Local-language news summarization involves turning news stories into short, easy-to-read summaries in the languages people speak. AI makes this possible by shortening articles and generating summaries and headlines in regional languages. For Lokal, GoML built AI for news summarization in local languages for its hyperlocal news platform. This helps Lokal deliver quick, easy-to-understand news to readers in their own language.
Content moderation
Content moderation involves reviewing user-uploaded content to keep platforms safe and remove harmful material. AI speeds up this process by scanning content automatically and flagging anything that violates community rules. For Biome, a social platform for Gen Z users, GoML built AI content moderation for images shared by users. This made image moderation faster and helped the platform handle content more safely.
Personalized content recommendations
Personalized content recommendations show users posts, articles, or videos that match their interests. AI improves these recommendations by learning from user behavior and ranking content based on individual preferences. For Mantel, GoML built a generative AI content recommendation system that powers a personalized feed. This helps users see content that is more relevant to their interests.
Media asset management
Media asset management involves organizing large collections of images and other media so teams can find and use them quickly. AI simplifies this process by automatically tagging, sorting, and describing assets. For Dropt, which supports fan loyalty platforms for sports teams and creators, GoML built a generative AI image management system. This changed how sports media teams organize and manage their image libraries.
