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Custom generative AI solutions built for enterprise use cases

From domain-tuned LLMs to multi-agent simulation environments, we build secure, production-grade generative AI infrastructure tailored to your business logic, data, and workflows.

Xenos delivers engineering services to help enterprises move beyond prototypes, from model integration and fine-tuning to custom tools for content generation, simulation, personalization, and synthetic data workflows.

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Leaders trusting our AI solutions:

10+

bringing complex AI concepts to life

100+

bespoke solutions deployed across industries, solving real challenges

40%

faster adoption rates with tailored, measurable results

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End-to-end generative AI engineering services

Generative AI consulting

Get strategic and technical guidance on building Gen AI systems that align with your data, infrastructure, and business logic. We help define use cases, evaluate model fit, and architect a path to deployment—from R&D to production.

Custom generative AI solutions

We engineer tailored LLM systems for enterprise-grade use cases, including synthetic data generation and simulation, internal copilots, content engines, and automated reasoning pipelines.

Generative AI accelerators

Accelerate your time to value with reusable components: Fine-tuning pipelines, RAG frameworks, agent orchestration modules, and prebuilt integration layers for internal tools and APIs.

Turn your data and workflows into production-grade generative AI

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Real-world generative AI solutions we can deliver

Built for enterprise needs, grounded in your data, and engineered for secure, scalable deployment.

Generative AI
Synthetic audience simulation for campaign testing

Synthetic audience simulation for campaign testing

Simulate realistic user cohorts to test ad creatives and targeting strategies in silico. LLMs generate synthetic personas trained on CRM and behavioral data, while reinforcement learning agents simulate interactions to optimize campaign outcomes pre-launch.

Internal knowledge copilots

Internal knowledge copilots

Deploy Gen AI assistants that understand your internal documentation, tools, and policies, enabling employees to ask complex, context-rich questions and receive source-grounded answers with traceability.

Synthetic data generation pipelines

Synthetic data generation pipelines

Build synthetic datasets for model training, experimentation, or data augmentation, reducing privacy risk while improving model generalization in finance, healthcare, or cybersecurity domains.

Autonomous content engines

Autonomous content engines

Automate content workflows across product, marketing, or compliance, with LLMs generating, evaluating, and optimizing long-form content based on templates, tone, and editorial logic.

Automated RFP / RFI response systems

Automated RFP / RFI response systems

Generate compliant, context-aware responses to inbound RFP/RFI documents by referencing internal repositories, historical submissions, and product knowledge bases.

Dynamic personalization frameworks

Dynamic personalization frameworks

Real-time LLM systems that tailor emails, offers, and on-site experiences to user context and behavioral signals are deployed via APIs or embedded in outbound tools.

LLM-augmented document processing

LLM-augmented document processing

Parse, summarize, and extract insights from contracts, regulatory filings, or internal documents—with built-in audit trails, confidence scoring, and fallback logic.

Multi-agent orchestration frameworks

Multi-agent orchestration frameworks

Deploy teams of AI agents that share memory, delegate tasks, and reason across workflows, enabling intelligent internal automation or multi-step external interactions.

Xenoss engineering approach to building generative AI systems

We architect, build, and deploy enterprise-grade generative AI infrastructure—grounded in your data, integrated into your stack, and designed for scale.

Architecture-first discovery

We scope the use case, define system boundaries, and design the architecture before touching a model or prompt.

Data readiness and domain alignment

We work with your structured and unstructured data to power domain-specific generation. Based on your actual systems, we implement vectorization, RAG pipelines, and grounding techniques.

LLM integration, fine-tuning, and evaluation

We select, tune, and integrate LLMs (OpenAI, Claude, Mistral, etc.) with fallback logic, eval frameworks, and dynamic prompt flows. Building model-agnostic systems with no vendor lock-in.

Agent logic, integration, and production deployment

We design multi-agent systems with shared memory, tool use, and runtime control logic and deploy them as secure, containerized services inside your infrastructure. Every system includes RBAC, audit trails, prompt versioning, and observability out of the box.

How to start

Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks

Challenge briefing

2 hours

Tech assessment

2-3 days

Discovery phase

1 week

Proof of concept

8-12 weeks

MVP in production

2-3 months

Some tech stack for real-world generative AI engineering

Featured projects

Turn your use case into an integrated generative AI system

Xenoss builds systems you can monitor, control, and scale with no vendor lock-in

stars

Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.

Oli Marlow Thomas

Oli Marlow Thomas,

CEO and founder, AdLib

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What’s your challenge? We are here to help.

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    AI capabilities

    Machine Learning and automation

    • ML & MLOps
    • ML system TCO optimization
    • Model & algorithm development and integration
    • RPA (Robotic Process Automation)

    FAQ

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    What is the difference between generative AI and AI?

    Generative AI is a specific type of artificial intelligence focused on creating new content, such as text, images, or music, based on patterns learned from existing data. For example, ChatGPT generates text that resembles human conversation, while DALL-E creates images based on text prompts. In contrast, AI is a broader term that includes any machine capable of simulating human-like tasks—such as recognizing speech, making decisions, or analyzing data—without necessarily creating new content. Examples of general AI applications include virtual assistants like Siri, which recognize and respond to voice commands, or recommendation algorithms like those used by Netflix to suggest movies.

    What are the top 10 generative AI applications?
    1. ChatGPT (OpenAI) – A language model that generates conversational responses, making it ideal for customer service, content creation, and more.
    2. DALL-E (OpenAI) – Generates images based on text prompts, helping artists, designers, and marketers create visuals quickly.
    3. Midjourney – An AI tool that generates high-quality images based on user prompts, often used by creatives for artistic projects.
    4. Jasper – A content generation tool used by marketers and writers to produce blog posts, ads, and social media content.
    5. DeepArt – Transforms photos into artworks by using the styles of famous artists, allowing unique artistic expressions.
    6. Runway ML – A creative toolkit for generating videos, images, and music, often used by filmmakers and designers.
    7. Synthesia – A platform for generating AI avatars and video content, making video production accessible without a camera or studio.
    8. Soundraw – An AI tool for generating royalty-free music, useful for video creators, game developers, and podcasters.
    9. Stable Diffusion – An open-source image generator that creates high-quality visuals based on text prompts, often used in digital art.
    10. Pictory – Converts text into engaging video content, enabling marketers and social media managers to create videos efficiently.
    How can generative AI be used for marketing?
    1. Content Creation: Generative AI tools like ChatGPT can write blog posts, social media content, product descriptions, or even entire ad campaigns, saving marketers time and effort.
    2. Personalized Email Marketing: AI can craft personalized emails tailored to individual customer preferences, helping brands connect with their audience in a more engaging way.
    3. Visual Content Generation: AI models like DALL-E can create custom graphics, product visuals, or ad creatives, making it easier to produce high-quality, eye-catching visuals without needing a dedicated design team.
    4. Ad Copywriting: AI-powered platforms like Jasper can quickly produce ad copy that is optimized for specific audiences, helping marketers test different messaging approaches more efficiently.
    5. Chatbots for Customer Engagement: AI-generated responses can be used in chatbots to handle customer inquiries, recommend products, or guide users through a sales funnel.
    6. Video and Audio Content: Tools like Synthesia or Pictory help create video content, such as explainer videos or product demos, without the need for extensive production resources.
    7. Idea Generation: Generative AI can be used to brainstorm campaign ideas or variations of creative content, sparking new concepts that marketers can refine.
    8. SEO Optimization: Generative AI can help create keyword-rich content that aligns with search engine optimization (SEO) strategies, helping brands increase their visibility online.
    9. Omnichannel Campaign Management: AI-driven solutions optimize campaign delivery across multiple channels, automatically distributing budgets and bids to maximize performance based on campaign goals. Xenoss has developed an AI-driven multichannel campaign management solution for Moloco, improving cross-channel performance by applying insights across platforms​.
    10. A/B Testing Variations: AI can quickly generate different versions of ad copy, email subject lines, or visual content for A/B testing, enabling marketers to find the best-performing variations faster.