We design and operate AIaaS platforms so teams can deploy sophisticated models without staffing an entire MLOps function internally with architecture flexible enough to expand as demand grows.
We remove the repetitive layer — approvals, data entry, status routing, so teams focus on judgment calls, with automation that scales with the business instead of requiring rebuilds.
AI Content Moderation, Recommendation Engines, Sentiment and Trend Analysis, Fake Account Detection, Engagement Prediction Models
Player Behavior Analytics, Matchmaking Algorithms, Sports Performance Analytics, Fantasy Sports Fraud Detection, Predictive Odds Modeling
We build high-performance AI solutions for US enterprises. Machine learning, generative AI, and intelligent automation, engineered for HIPAA, GLBA, and state AI law compliance, and delivered on time.
Years in Business
IT Professionals
Clients Worldwide
Projects Executed
We start every engagement with your business outcomes, not our technology preferences. Requirements, workflows, and success metrics are defined before a single line of architecture is drafted. This keeps every AI solution grounded in the operational reality of your organization instead of a showcase of technical capability for its own sake.
We work in short, visible sprints with regular checkpoints, so you see progress, not just promises. Roadmaps, risks, and trade-offs are communicated openly at every stage. This structured transparency lets stakeholders course-correct early, reduces surprises late in the project, and keeps delivery timelines realistic rather than aspirational.
Our engineers, data scientists, and solution architects bring hands-on experience shipping AI systems into production, not just research environments. Continuous upskilling keeps the team current with evolving models, frameworks, and best practices. That depth translates into fewer costly missteps and solutions built right the first time around.
We prioritize early wins alongside long-term architecture, so organizations see measurable results well before a project reaches full scale. Proof-of-concept work is designed to double as a foundation for production, not a throwaway exercise. That approach shortens the distance between initial investment and demonstrable business impact.
Our relationship with clients doesn't end at deployment. We stay engaged through monitoring, iteration, and evolving business requirements, functioning as an extension of your internal team rather than a vendor that disappears after handoff. That continuity means AI systems keep improving as your organization and its data change.
We design AI systems with total cost of ownership in mind, not just upfront development cost. Infrastructure choices, model selection, and automation decisions are all weighed against long-term operating expense. This disciplined approach helps organizations avoid over-engineered solutions and keeps AI investments sustainable well beyond the first year.
Our engineers architect Agentic AI systems capable of coordinating tasks across enterprise applications, data sources, and business workflows. By combining planning, reasoning, memory, and tool execution, we develop autonomous solutions that manage complex processes, support operational teams, and improve execution across dynamic business environments.
We build Generative AI applications tailored to enterprise use cases, combining foundation models with business knowledge, governance frameworks, and domain-specific data. Our expertise spans intelligent assistants, content generation, document intelligence, enterprise search, and AI-powered experiences designed for production environments.
Our data scientists design analytical AI frameworks that convert enterprise information into measurable business value. From data preparation and feature engineering to statistical modelling and visualization, we create solutions that improve reporting, operational planning, business intelligence, and strategic decision-making across organizations.
We engineer machine learning models that address industry-specific business requirements using supervised, unsupervised, and reinforcement learning techniques. Our expertise covers model development, validation, deployment, and continuous refinement, enabling AI systems to deliver reliable outcomes across evolving enterprise environments.
Our specialists develop deep learning architectures for applications involving complex visual, textual, and audio data. Using convolutional, recurrent, and transformer-based neural networks, we build intelligent solutions capable of handling advanced recognition, classification, prediction, and automation requirements at enterprise scale.
We develop NLP solutions that process business documents, customer interactions, and enterprise knowledge with contextual understanding. Our capabilities include language modelling, information extraction, text classification, multilingual processing, conversational AI, and document intelligence built around real-world operational requirements.
We implement intelligent document processing solutions that extract, validate, classify, and organize information from physical and digital documents. By combining OCR with AI-driven data interpretation, we automate document-intensive workflows while improving processing consistency, traceability, and enterprise data accessibility.
Our predictive analytics capabilities combine historical data, statistical techniques, and machine learning to develop forecasting solutions for enterprise operations. We build models that support planning, resource allocation, demand estimation, financial analysis, and operational optimization across diverse business functions.
We design recommendation engines that evaluate customer interactions, transactional data, contextual signals, and behavioral trends to deliver relevant suggestions. Our solutions support personalization across digital platforms while improving engagement, conversion, customer retention, and content discovery through intelligent recommendations.
Our computer vision expertise extends to developing AI applications that analyze images, video streams, and visual documents with high accuracy. We build solutions for inspection, monitoring, defect detection, document interpretation, object tracking, and visual intelligence across enterprise operations and industrial environments.
We develop facial recognition applications that support secure identity management across enterprise environments. Our solutions incorporate facial detection, feature extraction, identity verification, and access management capabilities designed for authentication, attendance, surveillance, and personalized user interactions while supporting enterprise security standards.
NIST AI Risk Management Framework (AI RMF)
NIST Generative AI Profile
Equal Employment Opportunity Commission (EEOC) Guidance on AI in Employment Decisions
Federal Trade Commission (FTC) Section 5 Enforcement on Deceptive AI Practices
Consumer Financial Protection Bureau (CFPB) Guidance on AI in Consumer Finance
Office of Management and Budget (OMB) Federal AI Procurement Memoranda
Colorado Automated Decision-Making Technology Law (SB 26-189)
Texas Responsible Artificial Intelligence Governance Act (TRAIGA)
California AI Transparency Act (SB 942, AB 853)
California Consumer Privacy Act (CCPA) Automated Decision-Making Provisions
Illinois Artificial Intelligence Video Interview Act
New York City Local Law 144 (Automated Employment Decision Tools)
Utah Artificial Intelligence Policy Act
AI Management System Standard (ISO/IEC 42001)
Quality Management Systems (ISO 9001)
Information Security Management (ISO 27001)
Service Organization Control 2 Type II (SOC 2)
Health Insurance Portability and Accountability Act (HIPAA)
Gramm-Leach-Bliley Act (GLBA)
General Data Protection Regulation (GDPR)
Children's Online Privacy Protection Act (COPPA)
OWASP Top 10 for Large Language Model Applications
Model explainability and algorithmic bias testing protocols
Abu Dhabi Healthcare Information & Cyber Security Standard
Data Protection Impact Assessment (DPIA) practices
AWS, Azure & GCP AI/ML Security Compliance Frameworks
FDA Guidance on AI/ML-Based Software as a Medical Device (SaMD)
FINRA Guidance on AI in Financial Services
CFPB Model Risk Management Expectations for AI in Lending
AI enables enterprises to analyze large volumes of structured and unstructured data in real time, transforming information into actionable insights, forecasts, and recommendations. Faster access to reliable intelligence improves strategic planning, operational responsiveness, and executive decision-making across every business function.
Business conditions, customer expectations, and market dynamics continue to evolve rapidly. AI helps enterprises adapt by automating workflows, optimizing business processes, and delivering real-time intelligence that supports faster responses, improved collaboration, and greater operational flexibility across the organization.
AI reduces the time employees spend on repetitive and rule-based activities by automating routine tasks and assisting with data analysis, content generation, and decision support. This allows teams to concentrate on innovation, strategic initiatives, and customer-focused activities that create greater business value.
Enterprise data often exists across disconnected systems, limiting its business value. AI consolidates, analyzes, and interprets information from multiple sources to uncover meaningful patterns, predict outcomes, and generate insights that improve planning, operational performance, and long-term business strategy.
AI enables organizations to anticipate operational risks, identify anomalies, and respond proactively to changing business conditions. Predictive intelligence and continuous monitoring improve business continuity, support informed risk management, and help enterprises maintain stable operations during periods of uncertainty.
AI provides the intelligence required to scale operations, improve customer experiences, and optimize business performance without proportionally increasing operational complexity. Intelligent systems help organizations identify new growth opportunities, improve efficiency, and strengthen long-term competitiveness across evolving markets.
We work closely with stakeholders to understand business challenges, priorities, and expected outcomes. This collaborative approach establishes a clear foundation for successful AI implementation.
Our experts design the solution architecture, implementation roadmap, and delivery strategy. Technology decisions are guided by business objectives rather than short-term trends.
We develop enterprise AI applications using modern engineering practices and scalable architectures. Every component is built for performance, maintainability, and future expansion.
AI solutions are integrated into existing enterprise environments through structured implementation. Comprehensive testing verifies compatibility, stability, and operational readiness before launch.
We provide continuous optimization, technical support, and capability enhancements after deployment. The solution evolves alongside changing business requirements and enterprise growth.
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.
Xicom builds custom AI solutions for US enterprises, including AI consulting, generative AI development, AI software development, and AI-as-a-Service platforms, designed around each business's specific operational requirements and regulatory environment.
AI development costs vary based on project scope, data complexity, and integration requirements, typically ranging from a focused proof-of-concept engagement to a multi-phase enterprise rollout. Xicom scopes each engagement after an initial consultation to match budget to business outcomes rather than quoting a fixed rate upfront.
Xicom builds AI solutions for industries central to the US economy, including banking and finance, healthcare, retail and eCommerce, manufacturing, real estate, and transportation. Each vertical accounts for the regulatory context specific to US operators, such as HIPAA for healthcare data or GLBA for financial services.
Xicom takes a business-first engineering approach, defining requirements and success metrics against US regulatory and operational constraints before architecture work begins, with agile delivery cycles that keep US stakeholders informed of progress and trade-offs at every stage.
Xicom's AIaaS platforms handle backend infrastructure so US teams can deploy AI models without building an internal MLOps function, with architecture designed to scale as demand grows across competitive, fast-moving US markets.
Xicom's generative AI development includes RAG architectures, orchestration layers, vector databases, and human review checkpoints, calibrated to the liability and data-security standards US regulators, insurers, and enterprise legal counsel hold companies to.