AI and ML Services

AI, which stands for Artificial Intelligence, is a technology that mimics human intelligence. With the help of artificial intelligence and machine learning services, these machines help organizations automate routine or repetitive tasks. Also they can empower decision-making through insightful data analysis or offer new ways to engage with customers.

On the one hand, AI can be described as the broader concept of machines that are able to perform complex tasks requiring human-like intelligence. On the other hand, machine learning is a specific type of AI that enables machines to learn from data. Important note here, that such machines can improve themselves over time without being explicitly programmed.

AI and machine learning can likely provide value if your business relies on data, has many repetitive tasks, or needs to improve decision-making processes. We offer AI and ML consulting services to help you explore how AI could benefit your operations.

Sombra is an AI ML development company, so we offer a wide range of flexible services. They include AI consulting, feature implementation, and full-scale AI development. If you don’t know where to start, we recommend exploring the use cases for your industry and finding the best fit for your specific business.

The timeline depends on the complexity of the specific project. Typically, simple implementations might take a few weeks, but developing more advanced or custom AI solutions may take several months with ai and ml services.

Not always. While having good-quality data can improve the results, we can work with what you have or help you set up processes to collect and prepare data for AI use. However, to do it right you need quality AI and machine learning development services

AI should assist, not replace, your core team. As practice shows, AI frees your team to focus on more complex and creative work. To get such a solution, opt for machine learning and AI services.

Additionally to artificial intelligence development services, we provide any kind of technical support and keep an eye that your AI system continues to perform optimally. Monitoring, updates, and necessary adjustments give you peace of mind and confidence in your AI investment.

AI software development costs vary widely because AI is not a single technology or delivery model. Pricing depends on the scope of the problem, data availability and quality, model complexity, integration requirements, and compliance constraints. A lightweight AI feature built on pre-trained models with limited integrations will cost significantly less than a custom solution that involves data engineering, model training, MLOps pipelines, and enterprise system integration. Costs are also influenced by non-obvious factors such as data labeling, infrastructure setup, security reviews, and post-launch monitoring. At Sombra, we focus on aligning investment with business value: we help clients avoid overengineering early and structure the solution so it can scale predictably. Instead of fixed “AI package” pricing, we typically define cost ranges based on delivery stages and measurable outcomes, allowing teams to validate ROI before committing to larger investments.

AI Proof of Concept (PoC) development is designed to answer one question: is this idea technically feasible and business-relevant with your data? A PoC focuses on a narrow use case, limited dataset, and clear success criteria. The goal is not production readiness, but evidence. At Sombra, AI PoCs typically include data exploration, model selection or prototyping, and basic evaluation against agreed metrics. We also document constraints discovered during the process — data gaps, bias risks, performance limits, or integration challenges — so there are no surprises later. A well-executed PoC helps stakeholders make informed decisions: proceed to MVP, adjust the use case, or stop early. This approach reduces risk, avoids sunk costs, and creates a shared understanding between business, engineering, and data teams before scaling begins.

An AI MVP moves beyond feasibility and focuses on real usage. It is a production-adjacent solution designed to validate business impact with actual users, workflows, and data flows. Compared to a PoC, an MVP includes system integrations, basic MLOps setup, security considerations, and user feedback loops. At Sombra, we design AI MVPs to be intentionally minimal but structurally sound — no throwaway architecture. This means choosing models and pipelines that can scale, implementing monitoring from day one, and aligning the solution with enterprise standards where needed. The objective is to test value, not perfection: does the AI improve efficiency, decision quality, or user experience in measurable ways? A strong MVP provides the foundation for confident scaling into a full production system.

Enterprise AI solutions require more than strong models — they demand reliability, governance, and alignment with existing systems. In large organizations, AI must operate within complex data landscapes, strict security requirements, and regulatory frameworks. Sombra approaches enterprise AI with a systems mindset: integrating AI into core platforms such as ERP, CRM, data warehouses, and internal tools. We focus on explainability, auditability, and operational stability, ensuring models can be monitored, updated, and controlled over time. This includes MLOps, access control, logging, and compliance support. Rather than isolated AI initiatives, we help enterprises embed AI into business processes, enabling consistent adoption across teams while maintaining control, transparency, and long-term maintainability.

AI implementation is the process of turning an AI concept into a working, operational solution inside a real business environment. It includes data preparation, model integration, system architecture design, infrastructure setup, security and compliance alignment, and ongoing monitoring. Unlike experimentation, implementation focuses on reliability, scalability, and user adoption. At Sombra, we treat AI implementation as a cross-functional effort, working closely with engineering, product, and business teams to ensure the solution fits existing workflows and delivers measurable outcomes — not just technical results.