Not every problem benefits from AI, and not every AI approach delivers production-quality results. We assess feasibility rigorously so you invest in AI initiatives with eyes wide open.
AI projects have a high failure rate. Research consistently shows that the majority of AI initiatives fail to reach production. The most common reasons are not technical: they are misaligned expectations, insufficient data, unclear success criteria, and costs that exceed the value delivered. AI feasibility assessment addresses these risks before you commit significant resources.
At Arthiq, we have built AI features into our own products. AgentCal uses AI for scheduling intelligence, and Social Whisper uses AI for content optimization. We have experienced both the triumphs and the disappointments of AI development. Our feasibility assessments are informed by this first-hand experience with what works, what does not, and why.
Our assessment answers three fundamental questions: Can AI solve your problem better than non-AI alternatives? What level of quality can you expect with available data and models? And will the cost of building and operating the AI solution deliver positive return on investment?
AI excels at specific types of problems: pattern recognition, classification, generation, recommendation, and prediction from large data sets. It struggles with problems that require precise logical reasoning, operate with very small data sets, or demand explanations that users must fully understand and trust. We evaluate your problem characteristics against the strengths and limitations of current AI technology.
We also evaluate whether AI is better than simpler alternatives. Rules-based systems, statistical methods, and traditional algorithms often outperform AI for well-defined problems with clear logic. AI adds value when the problem space is too complex for explicit rules, when the relationship between inputs and outputs is learned rather than specified, or when the task involves understanding unstructured data like text, images, or audio.
Our evaluation includes identifying the specific AI tasks involved in your use case, assessing the state of the art for each task, and determining whether available technology can achieve the quality level your users require. We are candid about cases where current AI capabilities are insufficient, saving you from investing in a problem that technology cannot yet solve reliably.
AI quality is bounded by data quality. If you do not have the right data in sufficient quantity and quality, no amount of model sophistication will produce good results. We assess your data readiness across several dimensions: data availability, volume, quality, representativeness, labeling, and accessibility.
For applications using foundation models like GPT or Claude, data readiness focuses on the quality of your prompts, the availability of retrieval context, and the representativeness of your evaluation datasets. For applications requiring custom model training, data readiness encompasses training data volume, label quality, distribution coverage, and data pipeline reliability.
When data gaps exist, we evaluate strategies for addressing them: data collection plans, synthetic data generation, data augmentation techniques, transfer learning from related domains, and partnerships that provide access to proprietary datasets. We provide a realistic timeline and cost estimate for achieving data readiness.
For use cases that pass our initial assessment, we design and execute a proof of concept that validates AI feasibility with real data and realistic conditions. The proof of concept is scoped to answer the specific feasibility questions identified during assessment, not to build a production system.
We define clear success criteria before building the proof of concept. These criteria specify the minimum quality level that would justify production development, measured with metrics appropriate to the task: accuracy, precision, recall, latency, and user satisfaction. Without predefined criteria, it is too easy to rationalize mediocre results.
The proof of concept typically takes two to four weeks and produces a functioning prototype, a quality evaluation report, and a recommendation for whether to proceed to production development. If the recommendation is to proceed, we include an architecture proposal and cost projection. If the recommendation is not to proceed, we explain why and suggest alternative approaches.
AI solutions have unique cost structures that must be modeled carefully. Development costs include data preparation, model experimentation, evaluation pipeline creation, and integration engineering. Operating costs include model inference fees, data pipeline maintenance, evaluation monitoring, and periodic model updates.
We model these costs across your projected usage volume and growth rate. For API-based solutions, costs scale with usage and can become significant at high volume. For self-hosted solutions, costs are more predictable but require infrastructure investment. We help you choose the approach that optimizes for your cost structure and scale projections.
ROI modeling compares the cost of the AI solution against the value it creates: time saved, revenue generated, cost avoided, or user experience improved. We also model the ROI of non-AI alternatives to ensure AI is the best investment. Sometimes a well-designed rules engine delivers eighty percent of the value at ten percent of the cost, and that trade-off should be visible in the analysis.
Not every AI initiative delivers value. Our assessment tells you whether AI can solve your problem, what quality to expect, and what it will cost, before you commit resources.