nexos.ai raises €30M Series A to accelerate enterprise AI adoption.
nexos.ai raises €30M Series A to accelerate enterprise AI adoption.
AI for developers: Build top-notch products with quality and speed
- Build, deploy, and innovate AI-powered products faster
- Connect internal databases for hyper-specific output
- Reduce interruptions with routing and fallback logic
What is AI for developers?
AI for developers leverages key
Primarily, AI for software development is used for coding, code completion, incident reporting, and anomaly detection. Some developer AI tools take it even further with
Apart from directly development-related tasks, AI models also assist devs with day-to-day operations like documentation, onboarding, and sprint planning. Keep your team focused on building, get AI-powered coding assistance to develop quality products that ship faster, reduce
How nexos.ai helps drive AI adoption
- Developers
- Marketing
- HR
- Finance
Oxylabs doubles AI-driven feature development and deployment in one quarter
- Developers
- Marketing
- HR
- Finance
Key benefits of AI for developers
Over 71% of companies already use AI for developers. Get one step ahead and integrate a single API
to connect, manage, and govern all your AI model usage with access to
Workspace
Faster development
AI for developers streamlines coding, debugging, anomaly detection, and more. Get quick access to technical documentation and company knowledge, plan sprints, and ship products faster.
Single API for all things AI
Building your own AI gateway would take months and a huge dev budget. AI Gateway provides one endpoint to connect and orchestrate all your models and avoid AI sprawl from the get-go. Scale efficiently with integrated AI tools and eliminate the need for costly custom infrastructure.
Reduced interruptions with multiple LLM models
Direct prompts to the best model for the task by provider, latency, cost, or performance. Maintain thread execution and handle model failures with smart fallbacks, retries, and load balancing to minimize interruptions during outages.
Less non-engineering workload
AI development tools also automate non-technical tasks like documentation, onboarding, pull requests, updates, and more, so that your dev team can stop wasting time on operations and focus entirely on building the product.
Cost reduction, more models
Instead of paying for each individual LLM for different development needs, access all models via one AI orchestration platform. Pay less, get more, and switch between models anytime. Compare outputs and find the best LLM for the task.
Compliance with built-in guardrails
AI for developers is secure by design. With
How do businesses use AI
in development?
There are multiple use cases of AI in development. See examples of AI for developers in action below.
How to integrate AI into your development strategy
First, think about what you want AI development tools to achieve? For example, do you need AI to optimize debugging, automate repetitive coding tasks, or forecast performance bottlenecks?
This will help you find the best AI orchestration solution that offers LLMs that your developers already use, but in a more secure and streamlined way.
If you deal with sensitive information or want to avoid data leaks and AI training on your input, prioritize AI platforms with secure guardrails and controls.
AI thrives on high-quality data.Ensure your documentation, knowledge bases, and code is clean, well-structured, and shared across relevant systems without silos.
FAQ
nexos.ai: all-in-one AI platform for both developers and other teams. It lets you interact with multiple AI models simultaneously, compare outputs, and optimize results. Its guardrails and observability features provide transparency and safety while coding.
Copilot: AI code completion tool that integrates directly into your code editor to suggest lines or blocks of code based on your input. It streamlines coding by automating repetitive tasks.
Snyk: A tool for code security and vulnerability scanning. It identifies security risks across open-source dependencies, container images, and infrastructure-as-code.
Hugging Face: NLP (Natural Language Processing) hub of tools and pre-trained models. Perfect for chatbot applications, language translation, or summaries.
Amazon SageMaker: build, train, and deploy machine learning models at scale. It enables developers to integrate ML into existing software.