What if you could launch a production-ready Gen AI solution in just 8 weeks, instead of 8 months? Speed-to-market defines market leaders. While most enterprises spend 6–12 months building AI applications from scratch, GoML empowers you to move faster. With 6 ready-to-deploy LLM boilerplates, GoML fast-tracks enterprise Gen AI application development by up to 80%, helping you outpace competitors, reduce costs, and go live in record time.

These boilerplates power enterprise generative AI solutions for healthcare, finance, and life sciences, enabling rapid, secure, and scalable AI adoption.

70% of AI projects fail

Over 70% of AI projects never reach production. The culprit isn't lack of vision, it's the overwhelming complexity of building AI applications from scratch.

Traditional AI development requires machine learning engineers, backend developers, frontend specialists, DevOps engineers, and project managers with AI expertise. Every project starts with the same time-consuming challenges: secure API connections, error handling for unpredictable AI responses, scalable architecture design, user authentication systems, and monitoring frameworks.

GoML's solution?

Pre-built, production-tested boilerplates that eliminate months of foundational work, designed specifically for building enterprise generative AI solutions for healthcare, finance, and life sciences.

GoML's 6 LLM boilerplates to speed up enterprise AI adoption

GoML has developed 6 enterprise-grade LLM boilerplates specifically designed for healthcare, life sciences, and financial services. These battle-tested solutions address critical business challenges with proven results across industries.

1. Conversational agents

With GoML: Pre-built NLP pipelines for text/voice chatbots, including fallback flows, intents, and integration-ready APIs for your systems, allowing you to launch support automation quickly.

Without GoML: You need to assemble NLP frameworks (Rasa/Dialogflow), design intent schemas, build dialogue logic, connect to backend systems, develop fallback strategies, and set up monitoring, all from scratch.

2. Search engine

With GoML: A fully LLM-powered enterprise search boilerplate that uses knowledge graph indexing and intelligent doc search engine templates.

Without GoML: Customizing Elasticsearch or OpenSearch, embedding documents, tuning retrieval pipelines, managing relevance, building UI components, and integrating LLMs is required.

3. Agentic workflows

With GoML: Orchestrate multi-agent logic at scale, automating compliance, audit, process approvals via turnkey patterns in AI Matic.

Without GoML: You'll manually choreograph agents, design task routing, implement error handling, tie into enterprise systems, test processes, and implement governance.

4. Content generation

With GoML: Templates for generating text, images, audio, and video, personalized at scale, already in place.

Without GoML: You must craft prompt libraries, integrate multimodal models, ensure brand consistency, design retry/fallback systems, and manage output auditing.

5. Data analytics and insights

With GoML: Boilerplate for real-time analysis: dashboards, natural language querying, insight generation, multi-modal visualizations (30%+ effort savings).

Without GoML: Building vector DBs, integrating LLM query layers, connecting to BI tools, designing query templates, and setting up pipelines manually is necessary.

6. Data synthesis

With GoML: Synthetic data generation boilerplate, produce high-quality training sets, remove bias, ensure model reliability.

Without GoML: You must construct generative pipelines, ensure statistical validity, build post-generation checks, and test embed utility manually.

Why GoML's boilerplates outperform generic solution building?

Enterprise-grade architecture

• Production-ready patterns with proper error handling

• Built-in logging, monitoring, and security measures

• Scalable infrastructure that grows with your business

Industry-specific optimization

• Healthcare: HIPAA compliance built-in

• Financial services: Regulatory reporting frameworks

• Life sciences: Audit trails and validation protocols

This ensures GoML's boilerplates are not generic, they are purpose-built to deliver enterprise generative AI solutions for healthcare, finance, and life sciences with security, speed, and compliance.

AWS partnership advantage

As an AWS Gen AI competency partner, GoML provides inherited cloud credibility, optimized AWS service integration, cost-effective scaling strategies, and enterprise-grade security standards.

Real-world success stories

Case Study 1: AI doubt solver for competitive exam prep

DoubtBuddy's Gen AI-powered learning copilot achieved 95% accuracy on JEE & Gaokao-level questions while reducing student effort by 80%.

Challenge: Students preparing for high-stakes exams needed expert-level, step-by-step explanations.

Solution: GoML's Gen AI Reasoning Framework using a fine-tuned Llama-3 model.

Results: 95% accuracy in solving complex academic queries, 80% reduction in time spent on difficult doubts, and scalable access to personalized tutoring.

Case Study 2: AI sales analytics for Sun Pharma

GoML built an AI sales analytics assistant using OpenAI and Autogen for Sun Pharma's sales and business teams.

Challenge: Sun Pharma's teams relied on manual processes to retrieve data from relational databases. Business users couldn't intuitively ask questions and waited days for analysts to prepare custom reports.

Solution: GoML's Data Analytics & Insights Boilerplate with multi-agent AI framework using Microsoft Autogen and OpenAI's GPT-4.

Results: 85% faster data retrieval without SQL expertise required and 70% reduction in manual effort through automated analysis

The competitive advantage is speed

GoML's LLM boilerplates give enterprises an unbeatable edge compared to peers who apply traditional IT approaches to gen AI experimentation.

Time-to-market analysis

• Traditional development: 6-12 months concept to launch

• GoML boilerplates: 8 weeks to production-ready pilot

• Competitive impact: 3x higher market penetration for early movers

Cost-benefit comparison

• Development cost reduction: 60% savings on initial build

• Time-to-value: 80% faster deployment

• Resource optimization: Teams focus on differentiation, not infrastructure

Accelerate AI adoption with a 8-week pilot

• Weeks 1-2: Discovery & planning: Use case identification and boilerplate selection  

• Weeks 3-5: Development & integration: Boilerplate deployment and customization  

• Weeks 6-7: Testing & optimization: Performance testing and compliance validation

• Week 8: Deployment & handover: Production deployment and team training

Why begin your AI transformation with LLM boilerplates

• Proven track record: AWS Gen AI Competency Partner with 80% faster development cycles

• Industry expertise: Specialized in Healthcare, Life Sciences, and Financial Services

• Comprehensive support: 8-week pilot guarantee with post-deployment optimization

• Future-proof technology: Built on latest AI/ML frameworks with regular updates

Take action: start your AI journey today

The AI revolution is happening now. While your competitors debate and plan, you can be building and deploying. GoML's 6 Gen AI boilerplates provide the foundation you need to launch faster, reduce costs by 60%, minimize risk with proven solutions, and scale confidently with enterprise-grade architecture.

Ready to transform your business with AI? The choice is clear: spend the next six months building what GoML has already perfected, or spend the next six months building what your competitors can't catch up to.

Contact GoML today or book a demo to discuss your specific use case and get started with your 8-week Gen AI pilot program. Your competitive advantage awaits.

GoML, an AWS Advanced Consulting Partner for the Gen AI Competency, delivers enterprise-grade AI solutions for healthcare, life sciences, and finance and many more. With 6 ready-to-deploy LLM boilerplates and an 8-week pilot process, GoML can accelerate AI transformation by 80%.