Our machine learning engineers integrate into your team and own a model end to end. They cover feature engineering, model training, validation on held-out data, and deployment into your cloud environment, with each model documented for handover. Models are optimized around the metric your use case depends on and monitored once they are live. Every experiment is tracked and versioned, so any result can be reproduced and explained to stakeholders later.
Our LLM engineers work across leading proprietary and open-weight language models. They build retrieval-augmented assistants, internal copilots grounded in your documents, and agents that act across your systems. Fine-tuning is applied where a base model falls short, a vector database handles retrieval over your own content, and guardrails cover prompt injection and data leakage. Quality is judged on retrieval accuracy, response latency, and token cost.
Our data scientists translate business problems into models that hold up in production. They run exploratory analysis, choose the right statistical method for the question, and build forecasting, classification, and recommendation systems. Each model is validated on data it has never seen, so reported accuracy reflects real-world performance. Findings reach stakeholders as a clear decision they can act on, backed by the evidence behind every figure.
Our data engineers build and own the pipelines that supply every downstream system. They move data from ingestion and transformation into the warehouse or lakehouse your analysts and models depend on. Orchestration, transformation, and quality checks run on a modern stack, with schema contracts enforced and malformed records caught before they reach a report. Streaming and batch are both available; the choice follows how fresh the data must be downstream.
Our MLOps engineers take models from the notebook to a dependable, observable production service. Deployment, monitoring, and retraining run through automated CI/CD pipelines with full version control. Drift detection surfaces degradation early, rollback paths cover a faulty release, and every deployment is logged and traceable end to end. The result is fewer outages, faster recovery, and data scientists who stay focused on the models themselves.
Our computer vision engineers build production systems for visual data. They handle object detection, image classification, OCR, and real-time defect inspection on production lines. Models are trained on annotated datasets, then tuned detection and segmentation networks among them to your specific cameras and lighting. Because a model rarely transfers cleanly between sites, each deployment is calibrated to the cameras and conditions where it runs.
Our NLP engineers convert unstructured text contracts, support tickets, transcripts into structured, queryable data. They build classification, named-entity recognition, sentiment analysis, and summarization on transformer-based architectures. Where general models miss your domain language, they fine-tune on your own corpus. Every system is benchmarked against a labeled set before release, so its accuracy is a measured figure you can rely on.
Our solution architects design the systems that turn an AI concept into something deployable. They translate business goals into technical specifications, weigh managed services against self-hosted infrastructure, and resolve security, latency, and cost before production code is written. The architecture is built to scale with the company as it grows over time. Every trade-off is recorded, so later teams inherit the reasoning behind each decision.
Our prompt engineers make generative features behave reliably under real use. They version prompts, build evaluation sets, and run controlled comparisons that reduce hallucination and hold outputs to your brand voice and policy constraints. Agent workflows are broken into discrete steps that can be tested in isolation, and each change is scored against the set before release. Only a version that outperforms the previous one reaches your end users.
Our AI product managers connect technical capability to measurable product outcomes. They define the roadmap, weigh each feature against its build cost, and keep data scientists, engineers, and stakeholders aligned on a shared set of priorities. Scope is controlled deliberately, and difficult trade-offs are resolved early, before a launch forces them. Every release is measured against a success metric defined with stakeholders before work begins.
We help businesses identify AI opportunities, assess process inefficiencies, and build implementation roadmaps, guiding clients through model selection, data readiness, and regulatory compliance for sustainable adoption.
We build AI prototypes and MVPs that validate technical feasibility and model performance early, helping teams make informed decisions while reducing investment risk before scaling.
We build custom AI systems trained on your proprietary data, tailored to your operations and governance standards, delivering higher accuracy and a sustained efficiency advantage.
We integrate AI directly into ERPs, CRMs, and enterprise systems without disrupting operations, aligning with your data landscape, compliance requirements, and security policies throughout.
We build autonomous AI agents capable of reasoning, planning, and executing tasks using external tools, including multi-agent systems where specialized agents collaborate toward shared goals.
We build adaptable, secure enterprise AI systems that modernize legacy workflows, strengthen operational intelligence, and integrate seamlessly with existing infrastructure while scaling alongside your business.
We build generative AI solutions using leading large language models, grounded in RAG, advanced prompting, and knowledge bases for contextual accuracy across document and knowledge use cases.
We build AIoT solutions connecting AI with IoT systems, enabling intelligent edge processing, real-time sensor data collection, and automated decision-making across secure, scalable architectures.
We build intelligent, production-ready AI applications using scalable architectures and proven engineering practices, embedding intelligence into core business functions including interaction and decision support.
We build conversational AI chatbots using NLP and machine learning for websites, apps, and platforms, supporting lead qualification, sales assistance, and customer support functions.
We build robust data foundations through pipelines, vector databases, and semantic layers, covering ingestion, cleansing, transformation, and governance to ground AI solutions in reliable data.
Contract Analysis, Legal Research Automation, Clause Extraction, Document Review, Compliance Intelligence
At Xicom, we integrate vetted AI specialists into your existing team, aligned with your project requirements and technical goals, ensuring faster delivery and sustained growth.
AI Engineers & Data Scientists
AI Solutions Delivered
AI Models in Production
Industries Served
Our machine learning experts use proven algorithms and frameworks to solve real business problems. They apply machine learning to your data for prediction, classification, and segmentation, handling the work from data preparation through to a deployed, working solution. Each result is tuned to the metric your use case depends on and monitored once live.
Deep learning powers the hardest problems in modern AI, from vision to language to audio. Our experts put it to work in your products, using established models and frameworks to deliver capabilities that simpler methods cannot reach. The outcome is dependable performance on complex tasks, delivered by people who work with these techniques every day.
Generative AI produces text, images, and code from a simple prompt. Our experts use today's leading models to build solutions for your use case, grounding them in your own content and adding guardrails for safety and accuracy. The result is assistants and tools that give your teams and customers useful, on-brand output they rely on every single day.
Our natural language processing experts build solutions that read, interpret, and respond to human language. Using established models, they deliver classification, entity extraction, sentiment analysis, and summarization that turn unstructured text into usable data. Where your domain language is specialized, the system is adapted to your own material.
Computer vision lets software interpret images and video. Our experts use it to build solutions for object detection, classification, and inspection, adapted to your visual data so they perform in your real conditions. These run in real time on the factory floor or in the field, calibrated to the cameras and lighting already in place on site today.
Reinforcement learning suits problems where decisions play out in sequence and depend on a changing environment. Our experts apply it to scheduling, routing, and resource allocation, shaping the reward signals that steer a solution toward the outcomes you care about. They bring sound judgment on where the approach fits and how to make it dependable.
Our speech and audio AI experts build voice and audio features that recognize, transcribe, and generate human speech. Using established models, they deliver transcription, voice interfaces, and audio classification that hold up across accents and noisy conditions, tuned to your vocabulary. The result is spoken interaction that feels natural and dependable.
Neural networks are the engine behind most modern AI. Our experts put them to work in your solutions, using proven architectures and frameworks to deliver capabilities across images, sequences, and structured data. Every implementation is engineered to balance accuracy, speed, and running cost, and tested thoroughly before it goes live in production.
Transformers are the architecture behind today's most capable language and multimodal models. Our experts use them to build solutions across text, vision, and audio, adapting and serving leading models for your specific tasks. They know how to get strong, reliable performance from these models and keep it stable under real production load over time.
Knowledge graphs connect your data into a web of entities and relationships that software can reason over. Our experts design and build them to power search, recommendations, and question answering that stays grounded in your facts. The result is a structured, connected view of your domain that people and AI systems can query with speed and confidence.
Edge AI runs models directly on devices, close to where data is created. Our experts use it to deliver solutions that operate within tight limits on power, memory, and latency, from cameras and sensors to mobile and embedded hardware. Intelligence reaches the field even without a constant connection, keeping responses fast and sensitive data on site.
Our MLOps experts turn working AI into dependable production systems. They automate deployment, monitoring, and retraining, with full version control and traceability built into every step. Drift and degradation are caught early, releases roll back cleanly when needed, and your solution stays accurate and reliable long after it first reaches production.
Share your project goals, AI use cases, preferred technologies, team structure, and engagement expectations. This helps identify the expertise needed for your project.
Based on your requirements, we curate a shortlist of AI professionals whose technical skills, industry experience, and project backgrounds closely match your objectives.
Review profiles, conduct technical interviews, or run practical assessments to validate each candidate's capabilities and ensure they align with your engineering standards.
Select the professionals who best fit your requirements. We handle the engagement formalities, allowing your team to move forward without unnecessary delays.
Your AI specialists integrate with your existing workflows, tools, and communication channels, collaborating as an extension of your in-house team from the very first sprint.
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, 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.
AI staff augmentation is a hiring model where you add specialized AI and machine learning professionals directly into your in-house team, without going through the overhead of a full outsourced project. You get the skill set (LLM engineers, ML researchers, NLP specialists, MLOps experts) while retaining full control over priorities, workflows, and delivery.
It works best when you have a defined AI initiative but not enough in-house specialists to execute it, when hiring full-time would take too long, or when the need is project-based rather than permanent. Companies also use it to test AI capability before committing to a permanent hire.
Cost depends on role seniority, engagement type (part-time, full-time, dedicated team), and project duration. Xicom offers flexible, transparent pricing models built around your specific requirement rather than a flat rate. [Book a free consultation for a tailored quote.]
Outsourcing hands the entire AI project to an external team with limited day-to-day involvement from you. Staff augmentation adds vetted AI talent to your existing team, so you keep managing the process, the roadmap, and the technical decisions.
Yes. It's built for flexibility, you can bring in an AI specialist for a 3-month proof of concept or a 12-month product build, and scale the team up or down as the project evolves.
Depending on the role and stack, most AI specialists can be onboarded within 1 to 2 weeks. For urgent requirements, Xicom maintains a bench of pre-vetted AI and ML professionals ready to join active projects.
Xicom structures overlapping working hours for real-time collaboration and uses async updates for non-urgent items. Teams follow agile sprints with regular standups and sprint reviews, and use tools like Slack, Jira, and Zoom to keep everything transparent.
Yes, professionals assigned to your team work solely on your AI initiatives for the duration of the engagement, not split across multiple client projects.
Yes. Xicom shares pre-vetted candidate profiles and encourages clients to run their own technical interviews before finalizing the hire, which reduces mismatch risk.
Common roles include AI engineers, LLM and generative AI developers, NLP specialists, data engineers, MLOps engineers, and AI solution architects, matched to your specific tech stack and use case.
The quickest route is AI staff augmentation. Instead of running a full hiring cycle, you tap into a bench of pre-vetted AI developers who can join your team within 1 to 2 weeks, or immediately if you need someone right away. You still get to interview and approve the candidate, you just skip the sourcing and screening time.