Applied AI Engineering
Applied AI Engineer
An Applied AI Engineer doesn't research new AI models from scratch — they apply them. We take the best models and APIs already available (OpenAI, Anthropic, open-weight models) and turn them into real production solutions for your business: rapid prototypes, objective evaluation of which model fits each case, and productization of the chosen solution.
Why Choose This Solution
Rapid prototyping
From idea to working prototype in days, not months, to validate whether an AI use case makes sense before investing in a full solution.
Objective model evaluation
We compare models and providers using benchmarks on your own data, not marketing claims.
Real productization
We take the prototype to a robust production system: error handling, monitoring, controlled costs, and scalability.
Pragmatism over cutting-edge tech
We choose the simplest solution that solves the problem — not the most technically sophisticated one if it doesn't add real value.
Focus on measurable ROI
Every AI application is justified with a clear business metric, not just the novelty of using AI.
Frequently Asked Questions
What is an Applied AI Engineer?
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It's an engineering role focused on applying already-existing AI models (not training new ones from scratch) to concrete business use cases, taking them from prototype to production.
How is this different from a Data Scientist or ML Researcher?
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An ML researcher focuses on developing or improving models; an Applied AI Engineer focuses on integrating already-proven models (in-house or third-party) into production systems that solve a specific business problem.
What use cases are good candidates for Applied AI?
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Document data classification and extraction, internal or external conversational assistants, semantic search, AI-assisted content generation, and automation of simple decisions based on unstructured data.
How long does it take to see a working prototype?
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Generally between 1 and 3 weeks for a validatable prototype with real data, depending on the complexity of the use case and data access.
How do you decide which AI model to use?
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We evaluate using your own data: accuracy, cost per use, latency, and ease of maintenance. Sometimes the best model isn't the newest one, but the most cost-effective for your actual volume.
What happens if the prototype doesn't work as expected?
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It's a normal part of the process: we iterate on the approach, try another model, or adjust scope before investing in productization. Rapid prototyping exists precisely to catch this early and cheaply.
Does the solution end up locked into a specific AI provider?
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We design integrations with an abstraction layer that allows switching model providers without rewriting the entire system, reducing the risk of vendor lock-in.
How are the costs of using AI APIs in production controlled?
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We implement response caching, usage limits, dynamic model selection based on query complexity, and per-transaction cost monitoring from the first deployment.
Can you work alongside my existing development team?
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Yes, it's the most common working mode: the Applied AI Engineer integrates into your team's sprint or works in parallel on a specific module, whichever you prefer.
What's the difference from the Forward Deployed AI Engineers service?
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The Applied AI Engineer solves a specific AI use case with a focused scope. The FDE is a broader, more integrated engagement, embedded in your organization to solve complex problems specific to your infrastructure. See Forward Deployed AI Engineers.
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