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.
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
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.
Built for enterprise needs, grounded in your data, and engineered for secure, scalable deployment.
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
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
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
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
Generate compliant, context-aware responses to inbound RFP/RFI documents by referencing internal repositories, historical submissions, and product knowledge bases.
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
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
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.
Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks
Challenge briefing
Tech assessment
Discovery phase
Proof of concept
MVP in production
Featured projects
Xenoss builds systems you can monitor, control, and scale with no vendor lock-in
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,
CEO and founder, AdLib
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AI capabilities
Machine Learning and automation
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.