We help you identify the right use cases, assess feasibility, and shape a clear implementation roadmap, so generative AI adoption starts on a practical, well-informed foundation.
We build custom generative AI systems around your workflows, data, and environment, with reliable performance, clean integration with existing tools, and room to scale across teams.
We build conversational AI that handles real customer and employee conversations across web, mobile, and messaging channels, going beyond scripted replies to resolve requests quickly.
We build AI-powered search and knowledge systems that surface relevant information from across your organization, breaking down silos and helping teams make better-informed decisions.
We extract and structure data from contracts, invoices, forms, and reports at volume, cutting manual review time and improving processing accuracy.
We develop multimodal AI solutions that understand and generate text, images, audio, and documents, supporting richer enterprise use cases than text-only models allow.
We integrate and customize LLMs inside your existing applications and workflows, with prompts, guardrails, and evaluations tuned to your specific operational needs.
We fine-tune and optimize models to improve accuracy, reduce latency and cost, and align outputs with your industry's terminology and standards.
We take your generative AI solution from prototype to production, deploying it securely inside your existing cloud or on-premise infrastructure with monitoring in place from day one.
We monitor, optimize, and update your generative AI solutions after launch, keeping them accurate, secure, and aligned with changing business requirements and newer model releases.
| Range of Developers | Junior Developers | Mid-Level Developers | Senior Developers |
|---|---|---|---|
| Hourly Rate | $25/hr | $35/hr | $45/hr |
| Years of Experience | 1–3 Years | 3–5 Years | 5+ Years |
| Project Manager Support | Yes | Yes | Yes |
| Time Zone Flexibility | Available | Available | Available |
| Quality Assurance | Included | Included | Included |
| Working Hours | 40 hours/Week | 40 hours/Week | 40 hours/Week |
Years in Business
IT Professionals
Clients Worldwide
Projects Executed











We design, structure, and iteratively refine prompts to improve model output quality, reduce hallucinations, and align responses with business intent by applying chain-of-thought, few-shot, and structured output techniques across diverse enterprise use cases.
We design and manage vector stores using Pinecone, Weaviate, Qdrant, and pgvector, handling embedding generation, indexing strategies, metadata filtering, and retrieval optimization to keep semantic search fast and contextually precise at scale.
We build systems that process and generate content across text, images, audio, and documents using multimodal foundation models, enabling richer enterprise use cases that go beyond single-modality inputs and outputs in real-world deployments.
We implement structured evaluation frameworks, output validation, content filtering, and safety guardrails, ensuring AI systems behave predictably, stay within defined boundaries, and meet quality and compliance standards across production environments.
We design scalable AI infrastructure for model serving, versioning, monitoring, and cost management using cloud-native tooling and open-source MLOps stacks to keep generative AI systems reliable, observable, and efficient in production.
We build synthetic data pipelines and structured training datasets that help organizations improve model performance, address data scarcity, and stay privacy compliant without relying on sensitive or limited real-world data.
We help organizations establish responsible AI frameworks covering bias assessment, explainability, audit trails, and policy compliance, ensuring generative AI systems operate transparently, ethically, and in line with evolving regulatory requirements.
We architect long-context and memory strategies for AI systems, managing conversation history, session state, and knowledge persistence across interactions to keep responses coherent, relevant, and accurate over extended enterprise workflows.
We design and implement data preprocessing and chunking strategies that prepare enterprise content for AI consumption, improving retrieval quality, reducing noise, and ensuring language models receive well-structured, contextually meaningful input.
We implement security best practices across AI systems, covering data anonymization, access controls, secure model serving, and compliance with privacy regulations, ensuring enterprise AI deployments handle sensitive information responsibly and safely.
Share your business goals, technical specifications, and AI development needs with our experts. They'll match you with generative AI developers who fit your use case, industry, and deployment environment.
Review curated CVs of our generative AI developers and evaluate their experience in LLM fine-tuning, RAG architecture, AI agent development, or multimodal AI to find the right fit.
Interview shortlisted developers to assess LLM proficiency, framework knowledge, prompt engineering skills, and communication before you make a final hiring decision.
Onboard your selected generative AI developers through our quick onboarding process, with clear sprint planning and smooth collaboration from day one.
Fixed Price Model
Best for well-defined AI projects, this model ensures clear scope, budget predictability, and timely delivery without surprises.
Most Popular
Dedicated Teams Model
Ideal for businesses seeking a long-term AI development partner, this model provides a dedicated team of AI engineers working exclusively on your project.
Time & Material Model
Perfect for AI projects with dynamic requirements, this model offers agility, cost control, and adaptability to continuous innovation.
Before any sourcing begins, the process starts with a thorough understanding of your AI initiative, covering intended use cases, data landscape, compliance considerations, and the technical depth each role genuinely requires to deliver results.
Using the brief as a filter, suitable developers are identified based on hands-on generative AI experience that aligns with your specific requirements, prioritizing real delivery experience over surface-level familiarity with tools and frameworks.
Shortlisted developers go through a structured technical review before the client speaks with them, covering AI architecture thinking, past project outcomes, and hands-on capability across the relevant stack.
The final evaluation sits entirely with the client. Shortlisted candidates are made available for direct interviews, technical discussions, or portfolio walkthroughs, giving full confidence before any commitment is made.
With selection confirmed, the engagement is formalized quickly. All commercial terms, deliverables, and working arrangements are documented clearly so both sides begin with complete alignment and no ambiguity.
Once underway, delivery follows a structured rhythm with regular visibility into progress. The team stays engaged beyond launch, handling monitoring, iteration, and scaling as your AI initiatives grow and evolve over time.
Xicom's Generative AI developers handle everything from selecting and integrating the right foundation model for your use case to building RAG pipelines, fine-tuning LLMs on domain-specific data, and deploying production-ready AI applications. They also design AI agents, build multimodal systems, and work closely with your existing tech team to ensure seamless integration with your current infrastructure.
Xicom's generative AI developers are available at hourly rates from $25 to $45, depending on experience level, tech stack specialization, and engagement model. That makes Xicom a cost-effective option for businesses in the US, UK, Australia, and Canada that want production-ready GenAI solutions without the overhead of local hiring. Share your requirements and we will recommend the right fit for your budget.
Generative AI can reduce manual effort across content creation, customer support, document processing, code generation, and knowledge management. When built right, it does not just save time. It creates new capabilities your teams did not have before, like instant access to institutional knowledge, automated report drafting, or AI assistants that understand your products and processes. Xicom's developers help you identify where Gen AI creates the most measurable impact for your specific business.
Our developers are proficient in Python, LangChain, LangGraph, LlamaIndex, and AutoGen for orchestration. They have hands-on experience with foundation models including OpenAI GPT, Claude, Gemini, Mistral, Llama, and DeepSeek. On the infrastructure side, they work with vector databases like Pinecone, Weaviate, and ChromaDB, and cloud platforms including AWS Bedrock, Azure OpenAI, and Google Vertex AI. They are also experienced in fine-tuning techniques like LoRA and PEFT, and evaluation frameworks for testing output quality in production.
A few reasons clients consistently come back to Xicom. First, you get developers with real project experience across multiple industries, not junior talent learning on your budget. Second, hiring from India through Xicom can save up to 65% compared with equivalent talent in Western markets. Third, Xicom handles the screening, vetting, and ongoing management so you spend less time on hiring and more time on building. Fourth, every engagement is backed by an NDA and clear IP ownership terms from day one.
Every engagement begins with a signed NDA before any project details are shared. Xicom follows strict data handling protocols throughout the development lifecycle. Client data is never used to train or fine-tune models without explicit written authorization. Xicom holds ISO/IEC 27001, ISO/IEC 42001, and SOC 2 Type II certifications, and for clients with specific requirements around GDPR, HIPAA, or CCPA, the team adapts its data handling approach accordingly.
Xicom provides post-deployment support that includes performance monitoring, prompt optimization, vector database updates, and model upgrades as newer versions become available. If something breaks or degrades in production, the team responds quickly. Most clients on dedicated engagements retain their developer for ongoing iteration rather than treating the launch as the finish line.