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Generative AI Development Services for Enterprise Applications

Liquid Technologies provides generative AI development services for businesses building AI-powered applications around specific workflows, business data, and existing software. As a generative AI development company focused on application delivery, we help shape use cases such as LLM-powered applications, enterprise knowledge assistants, and retrieval-augmented generation (RAG). The approach connects model capabilities with the application, data, and operating requirements needed for real business use.

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what is generative ai development

What Is Generative AI Development?

Generative AI development is the process of designing, building, and integrating AI applications that generate text, code, summaries, images, and other outputs based on business data and user instructions. It can include large language models (LLMs), retrieval-augmented generation (RAG), AI-powered knowledge assistants, and custom generative AI solutions connected to existing business systems.

Custom Generative AI Development Services for Enterprise Applications

The right generative AI solution depends on the problem, the information it needs, and how people will use it. We develop application-level AI capabilities around defined business requirements, with attention to usability, integration, evaluation, and ongoing operation.

Custom Generative AI Development

Custom generative AI development helps tailor an AI-powered application to a specific business or product need. We define the user experience, connect suitable model capabilities, and build the supporting software so teams can use generative AI within their day-to-day work instead of relying on a disconnected demo.

LLM Application Development

Develop applications powered by large language models for tasks such as drafting, summarization, question answering, and conversational interfaces. Model selection and application design should reflect the use case, expected output, data requirements, and operating constraints.

RAG Development and Enterprise Knowledge Retrieval

RAG development connects a generative AI application to approved business information so responses can draw on retrieved documents or data. Depending on requirements, a retrieval-augmented generation solution may include document processing, search and retrieval, access-aware data handling, source references, and answer-quality evaluation. RAG can improve grounding in relevant information, but it does not guarantee that every answer is correct.

Enterprise Knowledge Assistants

Make internal information easier to find and use through assistants designed around approved knowledge sources. Potential applications include policy search, technical documentation, internal procedures, and employee support. Access permissions and information governance should be considered in solution design.

Multimodal Generative AI Applications

For use cases involving more than text, multimodal applications can work with supported combinations of documents, images, audio, or video. The appropriate design depends on available models, source material, accuracy requirements, and the workflow.

Generative AI Features for Existing Products

Generative AI application development can also add AI capabilities to an existing application or digital product. Examples may include in-product assistants, content generation, natural-language interfaces, or workflow copilots. The goal is to make each feature useful within the product experience rather than create a disconnected tool.

Where Generative AI Can Support Business Work

Generative AI can support a defined business task when it uses appropriate information and fits into an existing process. These enterprise generative AI solutions may support the following use cases, depending on data readiness, risk, and workflow requirements:

Document Processing and Summarization

Extract, organize, or summarize information from business documents to help teams review material and prepare follow-up work. Human review may remain necessary for sensitive or high-impact decisions.

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Internal Knowledge Search

Help employees locate answers across approved policies, manuals, procedures, and technical documentation, with access controls and source context appropriate to the use case.

 

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Customer and Employee Support

Create knowledge-based assistants that draft responses or answer routine questions using approved information. Define escalation paths for uncertain, sensitive, or out-of-scope requests.

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Content and Communication Workflows

Support first drafts of product descriptions, internal communications, reports, and other repeatable content. Review processes and brand or policy guidelines can help teams assess outputs before use.

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Product Copilots and Business Applications

Build AI-assisted features into software that help users search, summarize, draft, or navigate information in context. Evaluate features against real user tasks, not only a model demonstration.

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Have a Generative AI Use Case in Mind?

Already identified a workflow, knowledge-access challenge, or product feature to improve? Let’s discuss your business requirements, available data, and integration needs to determine the right approach.

Discuss Your GenAI Use Case

From Use Case to Working Generative AI Application

Enterprise generative AI application development requires more than connecting an API to a model. The process should connect the business goal to data, application design, evaluation, deployment, and ongoing ownership.

Define the Use Case

Identify the users, task, current process, desired output, and constraints. Agree on what success should mean before selecting a technical approach.

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Assess Data and Requirements

Review the information the application needs, where it lives, who can access it, and whether it is suitable for the proposed use. Identify privacy, security, and governance requirements early.

2

Design the Application and Architecture

Choose an approach that fits the use case, which may include a hosted model, RAG, structured tools, or a combination. Map the user experience, system connections, permissions, and failure paths.

3

Build and Integrate

Develop the application components and connect them to approved data sources or existing software where required and technically supported. This may include LLM integration with business systems, APIs, or internal knowledge sources, with scope determined by the project requirements.

4

Evaluate Relevant Scenarios

Test representative inputs and failure cases. Assess output quality, relevance, latency, and handling of uncertain responses against criteria defined for the project.

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Deploy and Enable Users

Prepare the application for the agreed environment, document operational needs, and help users understand how to use it and when to review or escalate outputs.

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Monitor and Improve

Where included in the engagement, review feedback, usage, output quality, and changing requirements to guide ongoing improvements.

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Generative AI Integration, Evaluation, and Deployment

Generative AI applications need to work within the context of the business using them. Depending on the project, design considerations may include:

Data Grounding

Connect outputs to approved source material where the use case requires it.

Access and Permissions

Align information access with defined user and data permissions.

Evaluation

Define representative test cases and review output quality before and after launch.

Integration

Connect to existing applications, APIs, or data sources when required and technically supported.

Deployment Requirements

Assess the appropriate environment based on security, data handling, latency, cost, and operational needs.

Monitoring and Ownership

Establish how issues, feedback, changes, and maintenance will be handled.

Generative AI Use Cases Across Industries

The application should be shaped around the sector’s workflows, data, and review requirements. The examples below are potential use cases, not claims of completed client work.

Potential applications include administrative document summarization, internal policy search, and support for locating approved operational information. Clinical decisions and patient-facing use require appropriate domain review, safeguards, and validation.

Potential applications include internal policy assistants, document analysis, report drafting, and knowledge retrieval for approved business information. Outputs should be reviewed according to the use case and applicable controls.

Potential applications include product-content drafting, catalog enrichment, and knowledge-based customer-support assistance using approved product and policy information.

Potential applications include searching technical documentation, summarizing maintenance records, and retrieving operational procedures. Outputs should not replace required safety processes or expert judgment.

Potential applications include summarizing shipment documents, drafting operational updates, and helping teams review exceptions across available information sources.

Potential applications include in-product copilots, natural-language interfaces, documentation assistants, and generative features embedded into existing software products.

Engineering GenAI for Real Business Use

A generative AI development company should be able to explain how the application will solve a defined problem, fit into the way people work, and be evaluated and operated after release. Liquid Technologies approaches GenAI development as a software engineering effort shaped by business requirements, not simply as a model demonstration.

Use-case-led development

Define the task and expected outcome before choosing a model or framework.

Application-focused engineering

Design the surrounding software and user experience, not only the model interaction.

Integration-aware planning

Consider data sources, existing platforms, permissions, and operational dependencies.

Evaluation before release

Test against relevant scenarios and agree on how quality will be assessed.

Practical deployment planning

Account for the target environment and the team responsible for ongoing operation.

About Us

Technologies Selected for the Use Case

Use the existing tabbed technology UI only with technologies the delivery team currently supports. Organize the verified list into categories such as:

  • AI Models
  • Frameworks
  • AI Infrastructure
  • AI Deployment
  • Clouds
  • Data
  • DevOps

Generative AI and AI Automation We Have Delivered

Group 1948758919 1793370544 388
  • Private AI
  • Generative AI

Futurum AI shows how a private generative AI deployment works when data cannot leave the client’s cloud: Anthropic Claude via Amazon Bedrock, constrained agent permissions, audit logging, and human approval before any record entry. Sulzer shows the same engineering approach applied to operations: signals the floor already produces become live status, with SAP kept as the system of record.

3 agents · 1 private AWS environment · 2-stage human approval

Read the Futurum AI case study
image 1494 1793376122 470
See Our More Gen AI Case Studies

What Clients Say About Working With Our Generative AI Team

  • Francisco

    Francisco Zepeda CEO, Vitalog

    "Their desire to make things the way we wanted it built. Their passion to make us a great product is impressive."
  • Procheck testi

    Saim Siddiqui Chief Executive Officer

    "We had a pretty detailed process and everything they delivered was on time and on spec. It was worthwhile to work with them."
  • Rayan

    Ryan Westmeyer Director of Systems and Technologies

    "As other projects and development opportunities arise, we’re confident we can turn to Liquid Technologies to help us."
  • Angie Ojeda

    Angie Ojeda Founder & CEO

    "Liquid Technologies has been of great help and received only positive feedback from our team."
  • Intentomatics testi

    Zaki Mahomed Cheif Operating Officer

    "You don’t work with companies, but with people. In relation to that, make sure you are going to work with the best team — know that you’re going to work with a good company with Liquid Technologies."
  • Precheck testi

    Brian Silver Director of Client Relations

    "I was most impressed by how quickly they were able to propose a design. After the initial product and customer mapping"
Read Case Studies

Ready to Build a Generative AI Solution for Your Business?

From LLM-powered applications and enterprise knowledge assistants to AI integrated with your existing systems, we can help define a practical development approach around your goals, data, and deployment requirements.

Discuss Your Generative AI Development Requirements
generative ai solutions
Hadi Tabani - CEO Liquid Technologies
Forbes Technology Council 2024 Official Member

Hadi Tabani

CEO, Liquid Technologies
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“The first step in creating a successful proof of concept is clearly defining the problem. To do this, you must understand the market’s needs and demonstrate the core functionality of your idea. This validates your idea quickly and effectively. A well-executed POC communicates your concept’s potential, builds stakeholder confidence and garners support. It serves as a crucial step in securing investment in development.”

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Frequently Ask Questions

Generative AI development services can include custom generative AI application development, large language model (LLM) applications, RAG-based knowledge tools, document workflows, enterprise assistants, and generative features within existing products. These generative AI development solutions are scoped around the business use case, available data, required integrations, and operating environment.

Generative AI application development turns model capabilities into software people can use. It may include the user interface, model access, data retrieval, integration with existing systems, evaluation, and controls needed for the intended workflow.

An LLM application uses a large language model to perform a task. A RAG application also retrieves relevant information from a defined knowledge source and supplies it to the model as context. RAG can help ground answers in business information, but it does not guarantee correctness.

A generative AI application may connect to existing software, APIs, or data sources depending on access, compatibility, security requirements, and available interfaces. Generative AI integration should be assessed during discovery so the solution fits the current architecture and information-access rules.

Evaluation should reflect the intended use case. It may include representative test questions, relevance and correctness checks, source-grounding checks, failure scenarios, latency, and review by appropriate subject-matter experts. Agree on the evaluation plan before launch.

Deployment options depend on the selected model, infrastructure, security requirements, data constraints, and operational capacity. Discuss the target environment early to determine feasible options. For requirements centered on private deployment, see our private AI development service.

Cost depends on application scope, model choice, data preparation, integrations, security and governance requirements, evaluation effort, deployment environment, and ongoing support needs. A project assessment can help define a realistic scope.

Post-deployment support should be defined in the project scope. Depending on the agreed engagement, it may include issue resolution, monitoring, evaluation updates, and improvements as requirements change. Confirm available support options with the Liquid Technologies team.

AI FAQs

Discuss Your Generative AI Development Requirements

Have a use case in mind? Let’s discuss the workflow you want to improve, the systems and data involved, and the requirements your application needs to meet. Liquid Technologies can help you assess the next steps for a practical generative AI application, from defining the use case to planning development and integration.

Discuss Your GenAI Project

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Tell us about your project and what you’re looking to build. A Liquid Technologies specialist will help you identify the right AI, data management, mobile app, or website development solution based on your goals, budget, and timeline.

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  • Data-driven solutions — Manage, organize, and leverage your data to support smarter business decisions.
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