Generative AI Services

Generic AI tools can generate content, code, images, and responses, but they don't understand your business context out of the box. Cyntexa is a generative AI services company that helps you build and integrate generative AI solutions that connect models to your data, workflows, and existing systems.

From custom LLM applications and RAG to AI-powered content, image, voice, and code generation, we build solutions on top of your business data and remain flexible across models and cloud environments.

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Generative AI Applications Built Around How Your Business Works

Whether it's a chatbot that understands your product catalog, a tool that drafts on-brand marketing copy, or a system that generates code inside your existing repositories, our enterprise generative AI services connect the model to the data and systems behind the use case. We then build the evaluation, monitoring, and controls needed to keep the output reliable as the solution moves into production. Here's what that looks like in practice:

Custom LLM applications built around your business, processes, and user workflows, not a generic model applied to every use case.

Content, image, and code generation pipelines connected to the tools your teams already use.

Production-ready AI systems with monitoring, guardrails, and governance built into the architecture.

Autonomous agents that use generative AI to plan and complete multi-step tasks inside your systems, within the defined permissions.

Generative AI Services We Offer

Generative AI work rarely starts and ends at the same stage. Depending on where you are, start with consulting to define the use case, development to build it, or integration to connect it into the systems you already run

Generative AI Consulting Services

Custom Generative AI Development Services

Generative AI Integration Services

Generative AI Proof of Concept

Generative AI Chatbot and Assistant Development

Generative AI Security and Governance

Generative AI Operations and Support

Generative AI Training and Adoption

Data Engineering for Generative AI

Generative AI Consulting Services

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Custom Generative AI Development Services

Off-the-shelf tools rarely match your business logic or connect to your systems. Our custom generative AI development services build LLM applications, RAG solutions, fine-tuned models, and content, image, voice, and code generation systems around your business requirements.

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Generative AI Integration Services

A generative AI tool sitting outside your business workflow rarely provides better outputs. Our generative AI integration services connect generative AI directly into the CRM, ERP, content, and support systems, so your team works with it inside the tools they already use every day.

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Generative AI Proof of Concept

A pilot that looks good in a demo can still fail on your real data. We test your use case against your actual data and constraints first, so you know whether it works before it goes into production.

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Generative AI Chatbot and Assistant Development

Customers and employees lose time waiting for answers that already exist somewhere in your systems. We build chatbots and assistants that retrieve answers from your data, respond in your business tone, and act inside connected systems.

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Generative AI Security and Governance

Generative AI can expose sensitive data or produce output nobody approved. We set access controls, data handling rules, and review processes, so you can scale usage without losing control of your data.

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Generative AI Operations and Support

Salesforce DevOps and CI/CD Implementation

Model performance and costs shift after Generative AI model launch. After implementation, we monitor output quality, usage, and spend, and keep the solution updated as your data, users, and models change.

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Generative AI Training and Adoption

A solution your teams don’t use delivers no return on the investment. We train your teams to use, review, and improve generative AI output, so adoption follows the launch and delivers value.

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Data Engineering for Generative AI

Your AI model is only as reliable as your data. We prepare and structure your data and build the pipelines that feed your models, so responses stay accurate as your data changes.

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Every successful AI initiative begins with a focused conversation

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Modalities We Build

Text and Language

Content generation, summarization, and conversational AI assistants grounded in your own documentation and tone.

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Image and Visual

Product imagery, marketing assets, and other visual content generated in line with your brand guidelines and production workflows.

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Audio and Voice

Text-to-speech and voice AI agents for conversational systems, IVR, and accessibility use cases.

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Code and Structured Output

Code generation, structured data extraction, and formatted output built into your existing development and data workflows.

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Foundation Models and Technology We Build With

Large Language Models (LLMs)

LLM Orchestration & Agent Frameworks

RAG & Vector Databases

Machine Learning Frameworks

AI Cloud Platforms

Enterprise Platforms

AI Solutions & Applications

MLOps & AI Infrastructure

As your AI services provider, we build on OpenAI's models where speed and general-purpose reasoning are the priority.

We integrate Claude for use cases where reasoning accuracy and safety guardrails matter most.

We build with Gemini for use cases that benefit from native multimodal reasoning and deep integration with Google Cloud's data and AI ecosystem.

We work with Nemotron for enterprises that need optimized, high-throughput inference on NVIDIA's infrastructure, particularly for large-scale deployment.

Our model-agnostic approach lets you switch AI providers without rebuilding your application.

We use LangChain to orchestrate complex LLM workflows, chaining retrieval, reasoning, and tool use into a single pipeline.

We use LangGraph to build stateful, multi-step agent workflows that need more control than a simple chain allows.

We use CrewAI to orchestrate multiple specialized agents collaborating on a single complex task.

We use AutoGen for multi-agent systems that require dynamic, conversational coordination between agents.

We build retrieval-augmented generation systems that ground AI responses in your actual data, not generic training knowledge.

We use Pinecone, Weaviate, pgvector, and FAISS for fast semantic search, choosing based on your scale, latency, and hosting needs.

We use PyTorch to build and train custom deep learning models tailored to your specific use case.

Our teams use TensorFlow for production-grade machine learning models that need to scale reliably.

We deploy and scale AI workloads on AWS, using its infrastructure for training, hosting, and integration.

Our AI services extend into Azure environments, particularly for enterprises already standardized on Microsoft's ecosystem.

We build and deploy models on Google Cloud and Vertex AI for teams that need integrated MLOps and model management in one platform.

As a multi-cloud AI services company, we design and develop vendor-independent solutions, catering directly to your growing business needs.

We build AI-powered Salesforce solutions on Agentforce, with Claude as the reasoning model, and connect Salesforce data and workflows to Claude to automate sales and service work.

We implement Now Assist to automate IT and business workflows, streamline service operations, and help teams resolve requests faster using AI.

We use Nintex to automate business processes, streamline workflows, and reduce manual tasks across enterprise systems.

We build conversational AI systems that handle customer and internal queries with context-aware, natural responses.

We build voice-driven AI agents for phone-based and hands-free interaction use cases.

As an AI-as-a-Service provider, we let businesses access enterprise-grade AI capability without building infrastructure from scratch.

We build MLOps pipelines that keep your models trained, monitored, and deployed consistently across environments.

We use tools like LangSmith, MLflow, and Weights & Biases to track quality, cost, and drift, so you can see how your AI performs in production.

Our data engineering work builds the pipelines that feed every AI system we deliver with clean, reliable, analytics-ready data.

We run AI workloads on Kubernetes to ensure scalability and resilience for enterprise-grade AI systems.

Our Technical Approach to Generative AI

We choose the technical approach based on what your use case needs, whether that means grounding responses in changing business data, adapting model behavior, controlling outputs through prompts, or combining multiple techniques.

Retrieval-Augmented Generation (RAG)	Fine-Tuning	Prompt Engineering	Hybrid Approach
Ground the model in your business data	Adapt the model to your specific requirements	Shape how the model responds	Combine grounding with model adaptation
Connects a foundation model to your live documentation and business data so responses can use information that changes after model training.	Adapts a model using task- or domain-specific examples to improve consistency in a defined use case, style, format, or behavior.	Uses instructions, context, examples, and output requirements to control model behavior without retraining it.	Combines RAG with fine-tuning or prompt engineering when a use case needs both business context and consistent behavior.
Best suited for: Support assistants, internal search, knowledge bases, documentation, and other information-heavy applications.	Best suited for: Domain-specific generation, specialized workflows, and use cases where consistent output matters.	Best suited for: Structured tasks, content generation, and use cases that can be improved through better instructions and context.	Best suited for: Customer-facing assistants and other applications where accuracy, context, tone, and consistency all matter.

Retrieval-Augmented Generation (RAG)

Ground the model in your business data

Fine-Tuning

Adapt the model to your specific requirements

Prompt Engineering

Shape how the model responds

Hybrid Approach

Combine grounding with model adaptation

Retrieval-Augmented Generation (RAG)

Connects a foundation model to your live documentation and business data so responses can use information that changes after model training.

Fine-Tuning

Adapts a model using task- or domain-specific examples to improve consistency in a defined use case, style, format, or behavior.

Prompt Engineering

Uses instructions, context, examples, and output requirements to control model behavior without retraining it.

Hybrid Approach

Combines RAG with fine-tuning or prompt engineering when a use case needs both business context and consistent behavior.

Retrieval-Augmented Generation (RAG)

Best suited for: Support assistants, internal search, knowledge bases, documentation, and other information-heavy applications.

Fine-Tuning

Best suited for: Domain-specific generation, specialized workflows, and use

Prompt Engineering

Best suited for: Structured tasks, content generation, and use cases that can be improved

Hybrid Approach

Best suited for: Customer-facing assistants and other applications where accuracy, context

Every Successful Generative AI Project starts with a clear use case.

Talk to our experts about your goals, data, and systems, and find out what is worth building first.

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Industries We Serve

Retail & eCommerce

Product description generation, personalized marketing content, and visual asset creation at the pace new inventory demands.

Healthcare

Clinical documentation support and patient communication drafting, built with the data handling and compliance requirements healthcare requires.

BFSI

Personalized customer communication and report generation grounded in your actual policies and data, not generic templates.

IT & Software

AI coding assistants and technical documentation generation trained on your codebase and internal standards.

Manufacturing

Technical documentation and maintenance report generation based on equipment data and inspection records, reducing time spent on manual write-ups.

Logistics & Supply Chain

Shipment status updates, delivery communication, and report generation using your tracking and inventory data.

Education

Course content drafting, personalized learning material, and administrative communication generated from your curriculum and student data.

HR & People Operations

Job descriptions, policy communication, and onboarding content generated from your actual HR documentation and company guidelines.

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Our Generative AI Process We Follow

Discovery and Use Case Mapping

We assess your business goals, workflows, data, and existing technology to identify generative AI use cases worth pursuing.

Model Selection

The right foundation model depends on your use case, data requirements, performance expectations, security needs, and budget. These factors guide the model selection process.

Development and Fine-Tuning

Our team build the application, fine-tuning or connecting the model to your data through RAG depending on what the use case needs.

Evaluation and Guardrails

We test the output quality of the AI solution and implement evaluation, monitoring, guardrails, security, and governance before production deployment.

Deployment and Monitoring

We deploy the AI solution into your environment and monitor performance, usage, cost, and model behavior as adoption grows.

Timelines and Deliverables

Our generative AI solutions follow a defined plan with clear timelines. Each stage ends with something you can review, such as a use case list, a model recommendation, and a live deployment.

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Our Approach to LLMOps, Governance, and Guardrails

Evaluation and Testing

We evaluate model outputs for accuracy, relevance, consistency, and safety before and after deployment. To reduce hallucinations, we ground responses in your data through RAG and route high-risk outputs to human review. We also track how often hallucinations occur after launch.

Private Deployment and Data Use

We deploy generative AI inside your private cloud or VPC, so your data stays within your environment. Your business data is used only to ground and tune your own solution, never used to train models for other clients or public models.

Drift and Cost Monitoring

We monitor model behavior, output quality, and API or infrastructure costs over time to identify performance drops and unexpected spend early, and adjust the solution before either affects your users or your budget.

Audit Trails and Output Traceability

We log each request, the sources the AI used, and the response it gave. Your teams can trace any output back to its source, review it during audits, and investigate issues quickly when something goes wrong.

Compliance and Data Governance

We build access controls, data handling rules, and security requirements into the AI architecture. Users can only reach the data they are permitted to see, so sensitive information stays protected as usage grows.

Prompt and Output Guardrails

We implement controls around model inputs and outputs to reduce inaccurate, off-brand, inappropriate, or unsafe responses. It helps us block prompts that may misuse the system, so unwanted responses are identified before they reach your customers.

Incident Detection and Response

We track unusual requests, unexpected outputs, and access patterns to identify potential security issues early and respond before they affect your users or expose sensitive data.

Build Reliable Generative AI Solutions

Plan for model quality, monitoring, governance, and control from the start.

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Why Choose Cyntexa for Generative AI Implementation

One Team for Every Modality

Build text, image, voice, and code generation capabilities with our one team instead of managing separate AI vendors for different use cases.

LLMOps Built into the Architecture

Evaluation, monitoring, cost visibility, and output guardrails are considered from the beginning rather than added after the system reaches production.

Reusable AI Engineering Practices

We apply retrieval architectures, fine-tuning evaluation frameworks, and deployment patterns tested across prior engagements to get into production quickly.

Brand and Domain Context

Connect models to your actual data, workflows, documentation, and business rules so the resulting application works within your operating environment.

You Own What We Build

You receive the source code and documentation for everything we build, so your team can maintain, extend, or move the solution.

Deliver Across the Globe

Cyntexa delivers generative AI development services in the USA, UK, Germany, and more countries, so you can work with a team that operates in your region.

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Explore What’s New in Generative AI

Discover the latest AI developments, practical use cases, and ideas for applying AI to real business challenges.

Frequently Asked Questions

Generative AI development services help companies design, build, and deploy applications that create text, images, audio, code, and other content using foundation models. Cyntexa builds custom LLM applications, RAG solutions, fine-tuned models, chatbots and assistants, and content, image, voice, and code generation systems connected to your business data and systems.

Yes. We integrate generative AI with existing CRM, ERP, content, and support systems through APIs and data pipelines, middleware, and custom integrations. We first review your existing architecture to identify the right integration points and determine what needs to connect, so you can add AI without replacing your current systems.

It depends on your specific use case, data readiness, number of integrations, and the technical approach. A proof of concept on the defined use case moves faster than a production system connected to several platforms. Also, data preparation is often the longest stage when data sits across disconnected systems. We provide you with the timeline after the discovery stage, once we understand your data, systems, and scope.

Generative AI development cost depends on the scope of the use case, the technical approach (RAG, fine-tuning, or both), required data preparation, and system integration. After implementation, ongoing costs include model usage, infrastructure, and monitoring. You can start with a proof of concept to validate the value before the full investment.

Yes, but not completely. Hallucinations can be reduced but not eliminated, because models generate responses based on probability. We reduce them by grounding responses in your data through RAG, test the outputs against accuracy standards, and add human review for high-risk outputs. After launch, we monitor accuracy to identify hallucinations early.

Off-the-shelf tools are general-purpose. By default, they don't have access to your internal data, don't connect to your systems, and don't follow your access rules or brand standards. A custom generative AI solution works on your business data, runs inside your systems, follows user permissions, and includes monitoring. Generative AI can also run on different models, so you are not tied to a specific vendor.

Generative AI creates new content, such as text, images, code, or audio, while broader AI development involves systems that predict, classify, or detect, such as demand forecasting, fraud detection, and recommendation engines. Generative AI projects focus on prompts, RAG, fine-tuning, and output quality. Broader AI projects rely on machine learning models trained on historical data.

Yes, if the access and security controls are not built into retrieval. A RAG system that indexes all document data in one place can provide output to users who are not allowed to see it. Cyntexa’s enterprise generative AI services prevent this by applying your existing permissions at the retrieval stage, so the system only searches documents the user can access.

Yes, if the AI system does not have the right security controls. It can happen when someone tricks the model into ignoring the instructions or when users can access data they are not authorized to see. At Cyntexa, we reduce these risks by implementing role-based data access, input and output checks, security testing, and ongoing monitoring for suspicious requests.

Generative AI Services

Generative AI Applications Built Around How Your Business Works

Generative AI Services We Offer

Generative AI Consulting Services

Custom Generative AI Development Services

Generative AI Integration Services

Generative AI Proof of Concept

Generative AI Chatbot and Assistant Development

Generative AI Security and Governance

Generative AI Operations and Support

Generative AI Training and Adoption

Data Engineering for Generative AI

Generative AI Consulting Services

Custom Generative AI Development Services

Generative AI Integration Services

Generative AI Proof of Concept

Generative AI Chatbot and Assistant Development

Generative AI Security and Governance

Generative AI Operations and Support

Generative AI Training and Adoption

Data Engineering for Generative AI

Modalities We Build

Text and Language

Image and Visual

Audio and Voice

Code and Structured Output

Foundation Models and Technology We Build With

OpenAI / ChatGPT

Anthropic / Claude

Google Gemini

NVIDIA Nemotron

Model-agnostic Architecture

LangChain

LangGraph

CrewAI

AutoGen

Retrieval-Augmented Generation (RAG)

Vector Databases

PyTorch

TensorFlow

AWS

Azure

Google Cloud / Vertex AI

Multi-cloud

Salesforce (Agentforce, Claudeforce)

ServiceNow (Now Assist)

Nintex

Conversational AI

Voice Agent

AI-as-a-Service (AIaaS)

MLOps

Observability & Evaluation

Data Engineering

Kubernetes

Our Technical Approach to Generative AI

Industries We Serve

Retail & eCommerce

Healthcare

BFSI

IT & Software

Manufacturing

Logistics & Supply Chain

Education

HR & People Operations

Our Generative AI Process We Follow

Discovery and Use Case Mapping

Model Selection

Development and Fine-Tuning

Evaluation and Guardrails

Deployment and Monitoring

Timelines and Deliverables

Our Approach to LLMOps, Governance, and Guardrails

Why Choose Cyntexa for Generative AI Implementation

Trusted by 700+ Leading Brands

Explore What’s New in Generative AI

Frequently Asked Questions