AI Orchestration Explained | Ways to Integrate AI in Your Business

In the age of AI, businesses that want to leverage AI in their applications or processes need to learn how to integrate, configure, and manage a deluge of AI models, tools, and systems. The entire process of using AI, from development to deployment, can be made more efficient and streamlined using AI orchestration.

In this article, let’s explore AI orchestration, its benefits, and its wide range of uses in modernizing various business processes.

What is AI orchestration?

AI orchestration is the process of coordinating siloed AI components and systems so that they run seamlessly in an end-to-end automated workflow.

In AI orchestration, a central platform coordinates the interactions between different components, such as databases, algorithms, AI models, and other neural networks, so they can work together efficiently to complete some process. The central platform tracks the overall progress across components, managing data flow and memory, optimizing resource utilization, and handling failure scenarios.

Why use AI orchestration to build AI-enabled apps?

By abstracting away the details of such developmental efforts, AI orchestration serves as the connective tissue between multiple AI models, services, databases, algorithms, and humans.

Leveraging AI or LLM capabilities can be as simple as a chatbot application that takes a user query and returns an LLM-generated answer. However, more development effort is required when it comes to harnessing the power of AI in complex business flows like recommendation engines, large-scale data processing pipelines, fraud detection, or other autonomous & semi-autonomous systems. These high-impact applications can also serve as AI co-pilots or assistants to the human workforce, which necessitates seamless coordination between human-oriented tasks and AI applications.

Typically, such AI-powered processes involve multiple steps across human involvement, data preprocessing for AI ingestion, model fine-tuning, prompt engineering, context retrieval from vector databases, and so on. AI orchestration provides the architectural pattern that connects different prompt chains, APIs, databases, and function calls alongside human actions where needed.

Using AI orchestration, these steps can be clearly modeled to reflect your business logic while leveraging powerful programming models to automate the flows end-to-end, enabling businesses to easily weave AI or LLM components into existing workflows. In short, AI orchestration empowers enterprises to implement AI without getting tripped up by the complexities of its operational demands.

Using the right orchestration tool to build AI-enabled applications also brings along these benefits:

• Centralized control and governance: In a single platform, you get clarity into the entire process across disparate systems, making it easier to build, monitor, and troubleshoot AI-enabled workflows. The system’s state can be tracked, providing complete visibility into transient states at each moment as it progresses through the coordinated tasks.
• Enhanced efficiency: Computing resources can be optimized using task routing, dynamic scaling, and parallel processing, maximizing the efficiency and throughput of resource-intensive tasks across disparate services. This is especially important in AI tasks, where large volumes of data are processed at any point in time.
• Greater flexibility and scalability: It is seamless to add new tasks or to switch up the algorithms, datasets, or AI models in existing workflows, allowing businesses to quickly adapt to and keep up with changing requirements and the latest innovations.
• Increased reliability: With automated scheduling and failure-handling mechanisms, orchestration tools help minimize downtime and ensure continuous service delivery.
• Real-time analytics: Get insights into the workflow performance, making it easy to optimize resource utilization or to detect anomalies in AI systems.

Five practical ideas for using AI in your applications

Here are five ways for businesses to tap into AI.

Watch the webinar: Taking GenAI Applications from POC to Production, jointly hosted by AWS x Orkes.

1. Dynamic content generation

Content is king, especially for persuasion and capturing attention on the internet landscape. Using generative AI models, businesses can rapidly produce and iterate content snippets for a variety of use cases:

• In e-commerce: Generate product descriptions, tags, and categories across a vast product catalog.
• In social media: Generate engaging captions, hashtags, and content ideas for content planning.
• In media & entertainment: Generate synopses, tags, and categories for TV shows, dramas, films, and so on.

You can also harness AI models for content generation across media types, such as text-to-audio transcription and subtitling. Another possibility is turning wireframes or text into UI designs and then into working code.

Using AI orchestration, these AI-driven tasks can be easily incorporated into more complex workflows. For example, a media processing pipeline for online streaming may involve a host of steps — media transcoding, subtitle generation, synopsis generation, artwork generation, and classification into the appropriate categories, all of which can be completed using AI. Even if different AI approaches are used for each step, AI orchestration helps seamlessly integrate each step into an end-to-end workflow.

2. Knowledge retrieval and semantic search

With content comes the need to search and retrieve. Development in AI technology has advanced search capabilities, such that retrieved content does not just match the words in the query, but rather matches the meaning and intent of the query. In other words, semantic search.

Using an AI technique called RAG (retrieval-augmented generation), semantic search is made more accessible to businesses that want powerful search functionalities or chatbots for surfacing business-specific knowledge. AI-powered search is particularly useful for serving tailored responses rather than a simple link or a static content snippet, and can be deployed in:

• Specialized or private knowledge bases that return relevant, contextual information
• Document repositories that provide relevant documents and a pertinent summary of each document
• Service chatbots that provide relevant answers based on the customer’s queries.

3. Recommendation or personalization engines

Some of the most successful applications today run on recommendation engines that serve content tailored to each user’s unique profile. As with semantic search, AI technology has made it easier to develop robust recommendation engines, using various techniques like RAG (retrieval-augmented generation) blended with collaborative filtering.

Personalization is useful in industries where the vast number of products and services makes it impossible to sift through every option.

• In e-commerce: product recommendations based on the user’s favorites list or shopping history.
• In media & entertainment: video recommendations based on watch time or click-through rate.
• In banking, financial services & insurance: financial product recommendations based on the user demographics and risk appetite.

4. Data classification, processing, and analysis

With AI deep learning and LLM natural language processing capabilities, it is possible to process large amounts of unstructured data fairly quickly and automatically. AI models can adapt to different data types and variability in data formats more easily than traditional scripts. This means they can handle unstructured, semi-structured, or changing data more effectively without as much time and effort as required in script-based approaches.

Here are some ways to use AI for data classification:

• In document management: Classifying unstructured documentsinto different categories, such as resumes or receipts
• In customer service: Identifying the topic, sentiment, or intent of a customer email
• In data science: Cleaning and tagging unstructured and constantly-updated data into a well-defined dataset

With AI orchestration, businesses can build these tasks into an AI-powered data processing pipeline. For example, an expense processing workflow may involve a series of data-intensive tasks: compiling and converting all receipts into the required format, extracting the text using OCR, classifying each expense, and approving the expense based on a policy. Each task may involve different techniques or methods, which can be brought together using AI orchestration.

5. Detection and evaluation

Businesses can also take advantage of AI technology for evaluation processes, such as fraud detection, credit risk assessment, claims evaluation, or diagnosis.

For example, in fraud detection or KYC (know your customer) checks, various deep learning and AI methods can be used to improve accuracy for automated verification and authentication tasks, ensuring compliance while reducing costs.

• Computer vision—computers can interpret and analyze images and videos, such as identification documents, to catch counterfeits.
• Graph neural networks (GNNs)—networks that model graph-structured data, such as social graphs or transaction graphs. GNNs can analyze transaction data across long, complex transaction chains and identify patterns that indicate fraud.

Using Conductor for AI orchestration

Orkes Conductor is an all-purpose orchestration tool with built-in features for AI orchestration. Using Conductor, developers can weave AI-powered tasks into workflows by building custom task workers or by using Orkes’ system LLM tasks for text completion, embedding generation, index searches, and more.

Here’s an overview of orchestrating AI workflows with Orkes Conductor:

1. Build an AI-enabled workflow with a wide variety of custom tasks, operators, and system tasks, including LLM tasks. For system LLM tasks, Orkes enables seamless integrations with dozens of LLM providers and vector databases, including OpenAI, Vertex AI, Gemini AI, AWS Bedrock, Pinecone, Weaviate, MongoDB, and more.

2. Create your LLM prompts using prompt engineering. You can set up and store prompts in Orkes’ AI Prompt studio to be used in the system AI tasks.
3. Execute the workflow via code execution, APIs, webhooks, Conductor’s scheduler, or via external eventing systems like Kafka and SQS.

Document classifier with Conductor and LLMs

To explore how AI orchestration works in practice, here is an example of an automated document classifier using Conductor and LLMs. Imagine a seamless workflow where documents are intelligently identified—W2, Driver's License, Paystub, Employment Verification Letter, or Mortgage Application—without manual intervention.

The classification process begins by checking the file type of the uploaded document. In a simple example, the workflow only accepts .pdf files, which are passed into a Get Document task, a system task that retrieves the file contents for analysis.

Next, another system LLM task, LLM Text Complete task is used to classify the document based on its file content. The task is configured with a custom prompt that instructs the LLM to classify the document as W2, Drivers License, Paystub, Employment Verification Letter, Mortgage Application, or no match, and to explain its decision.

The final step is to transform the LLM output into the desired format for user display. An Inline task, JSON JQ Transform task, or even a custom Worker task can be used to do this.

While this document classifier is rather straightforward, Conductor enables you to easily extend its functionality, with dozens of tasks and integrations available. Easily add tasks to expand the number of accepted file types or to handle cases when the document cannot be classified, such as a Human task for manual classification.

To explore and try out the document classification workflow on your own, head to the Template Explorer in our Playground and select Import for the Document Classifier.

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The Amazon Bedrock integration is now available to enterprise customers. Along with this integration, be sure to check out the rest of the integrations within Orkes in our documentation. Curious to learn more about Orkes? Check out our 14-day free trial or sign up via the AWS Marketplace.