Ai Development Platform
Generative AI Development Platform
Don't Let Slow AI Development Cost You Market Share
Building a generative AI solution from scratch often means wrestling with complex architecture design, fragmented toolchains, and unpredictable model performance. Delays in deployment can stall innovation, while a lack of governance risks spiraling costs and inconsistent outputs. The result? Missed market opportunities and wasted development cycles.
The Codewave Generative AI Development Platform is built to change that. Designed for high adoption, engagement, and retention, it helps you move from concept to production 3X faster with fewer iterations and lower operational risk. The platform works with both open-source and commercial large language models (LLMs), giving you flexibility in selecting the best fit for your needs.
Using frameworks like LangChain and Hugging Face, we make it easier to build and connect AI features. Everything runs on scalable cloud services such as AWS, Azure, or Google Cloud to ensure reliable performance. A built-in MLOps pipeline manages deployment, updates, and monitoring, so your AI remains reliable and high-performing over time.
Whether you’re launching a new AI-powered product or upgrading an existing one, the platform provides all the essential tools in one place, without the usual complexity.
3x
Faster Gen AI Development
30-40%
Reduced Cost
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From Agent Building to Secure Deployment, All in One Platform
Building and scaling generative AI is a complex process. Most projects fail due to poor context handling, weak integration, security gaps, or lack of optimization. Our platform brings together every stage of Gen AI development, from agent creation to secure deployment, enabling you to launch faster and operate with confidence.
Many AI agents fail because they lack context or can’t handle complex tasks.
Our platform enables you to easily build AI agents that can comprehend your business data, utilize connected tools, and automate repetitive or complex tasks. These agents pull the proper context from your systems, databases, and even other AI agents, so their answers are accurate and useful.
Furthermore, they can communicate with people through text, voice, or video, providing quick and natural responses that often feel more effective than a human reply.
Behind the scenes, we utilize trusted technologies such as LangChain (to organize and connect AI actions), Hugging Face (to work with various AI models), and vector databases like Pinecone or Weaviate (to quickly find information). You can see what your agents are doing in real time, and the system continually learns from each interaction, so performance improves over time.
Example: If a customer requests to return a product, a basic AI agent might only provide information on the return policy. AI agents built on our platform can check the customer’s purchase history, verify product eligibility, and update the inventory system. Furthermore, it can process refunds through the payment gateway and send a confirmation email, all in one interaction. This will resolve the query instantly, improving efficiency and customer satisfaction.
Launching AI without proper testing can lead to serious errors, such as giving outdated information to customers, generating inaccurate compliance reports, or producing biased recommendations.
Our platform’s AI Testing & Evaluation feature enables you to test various AI models, including GPT-4, Cohere Command, and Llama 2, and determine the one that best suits your needs. You can also test prompts and set up retrieval-augmented generation (RAG) pipelines to improve how the AI finds and uses information.
Moreover, we combine automated checks with human reviews to ensure the AI is reliable, safe, and accurate before it is launched. Within the platform, you can create test cases, set clear success measures, and have subject matter experts review results. This approach lets you understand precisely how your AI will perform in the real world.
Example: If your AI generates financial summaries for clients, an untested system might miss recent transactions or miscalculate totals. Running it on historical data and cross-checking with verified reports will help catch errors early, ensuring accurate results from the very first day and maintaining client confidence.
Disconnected AI tools often create bottlenecks, as different systems work in isolation and require manual effort to transfer data between them.
Our platform solves this through API orchestration. It manages how your AI connects and communicates with other business applications, including marketing automation and sales CRMs, as well as compliance tracking and knowledge management systems.
We design custom architectures that ensure smooth, secure, and real-time integration, using trusted technologies such as REST and GraphQL APIs, middleware connectors, and event-driven services. This means your AI becomes an integral part of your workflows, rather than a separate, siloed tool.
Example: Suppose your sales team needs to follow up with a lead. Without connected systems, they may spend valuable time manually checking the CRM, campaign responses, and support tickets. However, AI can automatically consolidate this data, update the CRM with the latest activity, and alert the sales rep with a ready-to-use profile, helping them act faster and with better information.
Building and deploying large language models often requires significant infrastructure, ongoing maintenance, and in-house expertise. For many businesses, this means slow project timelines, high operational costs, and security risks when handling sensitive data.
Our platform delivers LLM as a Service, providing a dedicated and secure environment for developing and deploying generative AI products without the burden of managing infrastructure. You can start with proven, off-the-shelf models for quick deployment. Additionally, we manage hosting, scaling, updates, and security, allowing you to focus on delivering business value.
Example: A healthcare startup launching an AI-driven symptom checker may face high server costs, compliance challenges, and lengthy development cycles if it is built from scratch. Using a ready-to-deploy model in a secure, compliant environment will expedite rollout, scale automatically as usage increases, and maintain patient data protection at all times.
Managing large language models (LLMs) effectively requires more than just selecting the right one. It’s about striking a balance between performance and cost while maintaining consistently high quality.
Through LLM customization, we help you fine-tune models to handle complex, domain-specific tasks. These may include processing industry jargon, following multi-step business rules, and integrating them directly into your existing systems.
We also keep them running at their best through ongoing LLM optimization. This includes evaluating their performance in real-world situations, tracking their speed and accuracy, and managing costs by monitoring the amount of text they process per request. Since most AI models charge based on tokens, using fewer while maintaining high quality means faster responses and lower operating costs.
Example: When a legal firm relies on AI to draft contracts, a generic model might produce vague clauses or miss jurisdiction-specific terms. Fine-tuning it with past legal documents, testing on real cases, and monitoring for accuracy will ensure every draft meets professional standards while keeping processing costs predictable.
Managing AI model deployments in-house can be risky and inefficient. Many businesses face challenges, including limited control over where their data is stored, difficulty securing access for authorized users, and a lack of visibility into model performance.
Our platform enables you to run your AI models in your secure cloud, whether that’s AWS, Azure, or Google Cloud. This allows you to maintain complete control over your data. You can manage everything from a single, intuitive dashboard. Decide who gets access with role-based access control (RBAC), set up secure logins with SAML single sign-on (SSO), and keep your API keys safe.
Furthermore, creating or updating deployments is straightforward, and you can scale resources up or down without taking the system offline. You’ll also receive clear, real-time dashboards that show how the models are performing and how they’re being utilized. This makes it easier to identify issues and maintain smooth operation.
Example: A bank introducing an AI tool to review loan application risks data leaks or downtime without proper infrastructure. Deploying the model in a private, access-controlled cloud will enable the secure handling of sensitive information, real-time performance tracking, resource scaling during peak periods, and system updates without disruption. This will keep services safe, fast, and consistently available.
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Your Industry, Powered by Gen AI
| Industry | How Our Generative AI Development Platform Helps |
| Fintech | Our platform detects fraud in real time and automates compliance checks using fine-tuned LLMs and API orchestration. This keeps financial operations secure and compliant. |
| Education | Edtech businesses can deliver personalized learning paths, automate grading, and power AI tutors with agent building and LLM fine-tuning. Moreover, our platform’s AI testing feature ensures fairness and protects student privacy. |
| Healthcare | You can create accurate patient summaries, support diagnosis, and automate administrative tasks with LLM customization. This protects patient data and ensures compliance with relevant requirements. |
| Energy | SMEs in the energy sector can predict equipment maintenance needs, optimize power distribution, and manage energy consumption with AI testing, LLM optimization, and monitoring. This improves efficiency and reliability. |
| Transportation | Our platform helps you optimize routes, automate logistics, and validate autonomous systems. This enhances safety and reduces operational errors. |
| Retail | Our Gen AI development platform powers personalized recommendations, automates inventory control, and enables dynamic pricing through AI agents and API integrations. This boosts sales and customer engagement. |
| Travel | Our platform offers AI-driven itinerary planning, dynamic pricing, and real-time travel updates using LLM as a Service and AI agents. This improves the customer browsing experience. |
Inside the Engine: Our Gen AI Tech Stack
| Technology | Purpose |
| LangChain | Coordinate AI workflows and connect multiple AI actions |
| Hugging Face | Integrate and manage various AI/LLM models |
| Pinecone / Weaviate | Store and quickly retrieve vector embeddings for accurate responses |
| REST & GraphQL APIs | Connect AI with other business systems for smooth data exchange |
| AWS / Azure / Google Cloud | Secure and scalable cloud environments for AI model deployment |
| RBAC (Role-Based Access Control) | Control who can access and manage AI resources |
| Security Assertion Markup Language (SAML) SSO | Enable secure, single sign-on authentication |
| MLOps Pipelines | Automate model deployment, monitoring, and updates |
| Middleware Connectors | Link AI agents to existing business applications without custom coding |
| Vector Databases | Enable fast semantic search and context retrieval for AI agents |
Real SME Wins with Gen AI
Discover how SMEs utilize our Generative AI Development Platform to prevent payment fraud in fintech, safeguard patient records in healthcare, and reduce delivery times in logistics.
Their wins speak for themselves. Glance through our portfolio to witness these tangible results.
We transform companies!
Codewave is an award-winning company that transforms businesses by generating ideas, building products, and accelerating growth.
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Frequently asked questions
A Generative AI development platform provides the tools, infrastructure, and services to build, deploy, and manage GenAI applications. It supports tasks like model training, fine-tuning, integration, and monitoring, enabling businesses to create scalable, secure, and domain-specific AI solutions.
A vector database stores information as numerical vectors, which represent the meaning of data rather than just exact words. This enables AI to search and compare items based on similarity. For example, if you search “running shoes,” it can also suggest “jogging sneakers” because it recognizes the related concept, not just the keyword. It is commonly used in Generative AI for tasks like semantic search, recommendations, and question answering.
Code Accelerate simplifies Generative AI development with pre-built frameworks, tested workflows, and optimization tools. It helps businesses choose the right models, integrate them seamlessly, and launch up to 3X faster, ensuring accuracy, scalability, and security throughout the AI lifecycle.
In Generative AI, a Large Language Model (LLM) is an AI system trained on vast text datasets to understand and generate human-like language. It powers tasks such as content creation, chatbots, code generation, and summarization by predicting and producing text based on context.
A Retrieval-Augmented Generation (RAG) pipeline is an AI approach that first searches for relevant, up-to-date information from trusted sources. Then, it uses a language model to create a response. For example, a support bot can fetch product details from your database before generating an accurate, conversational answer.
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Most AI projects fail before launch due to weak testing, poor integration, and security gaps.
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