Every enterprise deals with manual grunt work that can be easily automated, even in 2025. However, finding (or in some cases) creating the right solution may be a huge drain on time, money and other resources. Accelerating AI adoption is one way to solve them today. That’s where LLM boilerplates hold immense promise.
Building an AI-powered system involves a complex set of steps like choosing the right model, orchestrating data, integrating with existing systems and making sure it aligns with compliance.
To cut through all that complexity, GoML is pioneering a proprietary approach to AI templates for accelerating AI development, testing, and implementation within enterprises. Our LLM boilerplates and are acceleration frameworks designed for real-world scale.
What are LLM boilerplates?
Simply put, they’re a pre-configured framework for deploying AI to solve a business workflow. LLM boilerplates are a combination of orchestration, integration, compliance and domain-specific logic.
These frameworks are shaped by real-world use cases with real problems on real data. GoML offers 6 LLM boilerplates that let you start with an enterprise-grade foundation, then adapt it to your context, without having to reinvent best practices every time.
Here are the six LLM boilerplates GoML offers:
- Conversational agents
- Search and retrieval
- Agentic workflows
- Content generation
- Data insights
- Synthetic data generation
Each of these boilerplates has been used to accelerate gen AI development and implementations for enterprises around the world.
Do LLM boilerplates really work?
In one instance, a major hospital was facing delays with clinical treatments. This wasn't because of lack of medical talent, but because teams spent hours wrangling data, trying to get a single up-to-date snapshot of each patient. Lots of solutions have tried to close that gap, but the breakthrough came with a LLM boilerplate.
When you examine case studies across regulated industries, you find similar leaps:
- Financial portfolios analyzed 99% faster.
- Pharma audits compressed from months to days.
- Regulatory compliance checked in real time and not as an afterthought.
The common denominator is field-tested methodology and execution for AI acceleration through LLM boilerplates. Many of thse results are from customers who went from idea to pilot to production in 8 weeks with our LLM boilerplates.
How is an LLM boilerplate different from AI APIs?
Anyone can download a model or assemble a list of a few APIs. However, what is the real difference between an “okay” automation and something that transforms the way teams work?
Most times it lies in the architecture, which requires deep Gen AI and domain expertise.
GoML LLM boilerplates don’t show up as static, fixed solutions. They dynamically evolve as frameworks, shaped by deployments in everything from clinical summarization to real-time fraud scoring.
Here is how our LLM boilerplates are built for accelerating AI:
- Deep domain experience
- Tested on hundreds of live use cases
- Compliance built-in from day one
- Patterns that anticipate change, not just optimize for the current state
- Expert prompt engineering for models and use cases
- Comprehensive tech stack to assemble the right Gen AI solutions
- Engineering for low latency and scale
It's popular to assume that custom frameworks always wins. This may not be the case if your outcomes are tight on time, effort or money. If you choose to build a custom solution, chances are you’ll spend 10X the resources, that you could’ve skipped if you deployed an LLM boilerplate.
Results from using GoML’s LLM boilerplates
Numbers get thrown around often, but they only matter when there's context:
- Clinical decision-making: 10X faster when automation is done accurately.
- Life sciences audits: 99% faster when the AI can read and validate requirements across massive doc sets in minutes, not weeks.
- Treatment plans: 90% faster in instances where data integration and summarization happen on the fly.
- Fraud detection: 99% time savings by automating what used to be multi-analyst reviews.
- Marketing campaigns: 30% lift in ROI by using custom templates to target the right message.
Every one of these gains originated from LLM boilerplates that were carefully customized and rethinking the “last mile” of legacy workflows.
Are GoML’s LLM boilerplates plug and play?
No, let’s be clear that templates are not a universal answer. Doesn’t matter how great the tech is, it still needs careful scoping and a clear sense of the unique environment it’s deployed in. However, the organizations running at the front are leveraging acceleration frameworks, iterating quickly, and adapting best practices to go live fast. Our LLM boilerplates are just that. Instead of 6 months, you can pilot, test, and implement gen AI In your enterprise in less than 8 weeks.
How to select the right LLM boilerplate?
Some will sell you a toolkit and walk away. Others will learn your world, adapt the solution in partnership with your team and anticipate where the process will break. Make sure to look for:
- Track record: Proven use in your field that go beyond PoCs.
- Customization: Flexibility to adapt templates for a dynamic use case.
- Continuous improvement: Commitment to update with regulatory and technical shifts.
- Partnership mindset: Willing to engage, not just deliver code.
Organizations going this route, working with someone steeped in both tech and domain context, find themselves with more success than others.
The future of enterprise AI development with LLM boilerplates
There’s a reason so many top healthcare, finance and life sciences leaders are building gen AI solutions with LLM boilerplates. The next leap will be finding rapid ways of leveraging the tech you already have, grounded in proven, expert-built patterns.
There’s no single perfect way, but there are proven shortcuts. LLM boilerplates, especially the kind you’ll find at GoML, are more than accelerators. They’re the new standard for making AI work in complex, regulated, high-stakes environments, without the slow pace of building a bespoke solution.
