AI Lab — Decisions That Pay Off
AI Lab helps enterprises define AI initiatives that suit their business, data, and operating model best, and then move them into execution with clear ownership and scope. Decide what to build before execution begins.
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What Is an AI Lab?
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As a Center of Excellence, it consolidates knowledge, best practices, and delivery patterns across generative and agentic AI.
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As an R&D function, it continuously explores modern AI technologies, evaluates emerging approaches, and validates where they create real value — particularly in complex, high-risk, or high-scale scenarios.
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With AI Lab, we can identify where AI creates real economic impact, validate those opportunities through deep analysis, and translate them into measurable initiatives and potential ROI.
AI Technologies and Tools We Use
Why Choose Sombra's AI Lab?
According to S&P Global survey, 42% of companies abandon most of their AI pilot projects in a span of the year. Without clear understanding of why and how, AI initiatives stall, fragment, or fail to reach production.
Across client projects, we saw similar patterns: teams moved quickly to implementation without fully understanding the business problem, market context, or economic upside. This often led to misaligned solutions, unclear ROI, and avoidable cost.
We address this gap by putting structured research, domain understanding, and financial validation at the center of AI decision-making.
What Value Does AI Lab Provide?
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Up to 80% reduction in manual effort in operational review processes
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1,5x increase in throughput without expanding delivery or operations teams
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30% less time spent searching or validating information in knowledge-heavy environments
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Identification of seven-figure cost-saving opportunities, up to $2M annually
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Delivery timelines shortened to as little as 2-3 weeks for well-scoped initiatives
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The result? A portfolio of AI initiatives that are feasible, aligned, and execution ready.
Discover Proven Approach to Delivering Impactful AI Solutions
Separate product decision-making from engineering execution, so each stage can be done properly
Internal Workshops
Each engagement starts with a deep analysis of the client’s business problem. We start with deep-dive workshops involving teams on the client’s side, and we also conduct our internal research to collect as much information as possible. These sessions focus on real delivery challenges, current workflows, and understand which parts of the process rely on manual judgment, interpretation of complex information, or repetitive human effort.
Use Cases Research
Each potential use case is assessed through a structured Level of Effort (LoE) lens, evaluating scope, complexity, data readiness, and organizational dependencies. These insights are adapted to Sombra’s technical stack and delivery model, resulting in research-backed, execution-ready use cases that can be confidently presented to stakeholders without requiring prior client involvement.
Impact Analysis
We evaluate data availability and quality, operational constraints, and governance considerations, and models the expected business impact of different solution paths. This includes indicative estimates of cost reduction, productivity gains, or revenue impact, as well as the effort required to achieve them. In several client engagements, this analysis has uncovered opportunities to save millions of dollars by prioritizing the right AI initiatives and avoiding unnecessary development.
Custom Solution Architecture
Only after deep analysis does our team formulate solution proposals. These are clearly defined initiatives with a rationale, expected ROI, and a delivery path. As an output, you get a business-backed recommendation that can move directly into execution, with clear assumptions and measurable success criteria.
Future Initiatives Planning
Promising initiatives are structured and scoped for next-stage validation. They are prepared as pitch-ready concepts for discussion with stakeholders, sponsors, or clients.
Implementation & Delivery
At the delivery and implementation stage, we transition validated concepts into execution, either through custom-built solutions or by leveraging Sombra’s AI Accelerator. The Accelerator provides a production-ready foundation for many AI initiatives, allowing teams to move faster without rebuilding core components such as orchestration, evaluation, monitoring, and knowledge pipelines.
Who Can Benefit From Our AI Solutions
AI Lab supports enterprise and upper mid-market organizations that recognize AI as a priority but lack clarity on where to start, or which use cases to prioritize.
Get In TouchAI Services We Proudly Deliver
The AI Accelerator provides a production-ready foundation for building, testing, and operating AI assistants and agents at scale. It enables teams to move from validated ideas to working systems faster by offering reusable components for orchestration, knowledge retrieval, evaluation, and monitoring. Designed to support both generative and agentic AI use cases, the Accelerator reduces delivery risk, shortens implementation timelines, and ensures AI solutions are built for long-term reliability.
AI Lab works across both generative and agentic AI as a single, integrated capability. Rather than treating these as separate domains, the Lab designs systems where generative models, decision logic, tools, and workflows operate together. This approach enables the development of AI solutions that not only generate insights or content, but also act within defined processes, automate decisions, and support complex, multi-step workflows in production environments.
The real AI challenge isn’t just getting the tech right. It’s all about finding a valid use case. Thus, instead of forcing AI into places it doesn’t fit, we dig deeper to identify where it can drive value and deliver the most substantial ROI for you. Let’s make sure it’s a tool that works for your business and not just ride on its hype.
Free Call with Our Director of Enterprise AI
Do you have a project in mind? Let's discuss how our generative AI services can drive growth and efficiency for your business.
Get in TouchStart Your AI Journey
Do you have a project in mind? Let's discuss how our generative AI services can drive growth and efficiency for your business.
Get in TouchFrequently Asked Questions
How long does it take to develop an AI solution?
Timelines depend on the scope and maturity of the initiative. Decision and discovery phases typically take a few weeks and focus on defining the right use cases, data readiness, and success criteria. Delivery timelines vary based on complexity, but validated use cases often move from pilot to production in a predictable, staged manner rather than open-ended experimentation.
What is the cost of implementing an AI solution?
Costs are driven by factors such as data availability, integration complexity, governance requirements, and scale. By clarifying use cases and expected outcomes upfront, we help clients avoid unnecessary investment and focus on initiatives with a clear value case. This approach reduces wasted spend on low-impact experiments and enables more accurate budget planning.
Can AI be integrated with my existing business systems?
Yes. AI solutions are designed to work within existing enterprise environments, including legacy platforms, data warehouses, and operational systems. Integration is planned as part of the solution design to ensure AI outputs can be embedded into real workflows rather than operating as isolated tools.
What is the difference between an AI POC and an MVP?
A proof of concept (POC) is used to validate feasibility or technical assumptions in a limited, controlled scope. An MVP goes further by delivering a usable solution with defined business value, production constraints, and success metrics. Many AI initiatives fail by stopping at the POC stage; our approach is designed to bridge the gap to real-world adoption.
How do you ensure compliance with data regulations in AI projects?
Compliance is addressed from the start through data governance, access controls, auditability, and human-in-the-loop processes where required. AI solutions are designed to align with applicable regulations and internal policies, ensuring transparency, traceability, and responsible use throughout their lifecycle.
