# AI / ML: Generative AI and agents, computer vision, and predictive models.

URL: https://extra.dev/services/ai-ml

**AI that earns its place in your business**

AI features and systems with clear uses, measurable quality and practical guardrails.

## A measured approach to AI

AI development applies models and automation to work where they can improve how information is found, processed or acted on. We begin with a defined outcome, the available data and a clear measure of what good performance looks like.
Evaluation, safeguards and monitoring are developed alongside the capability, with human review when needed. This creates AI systems that are useful in practice, transparent in operation and sustainable to run.

What this includes:
- **AI strategy & feasibility**: Assessing suitable uses, technical and data readiness, measurable outcomes and a practical route to implementation.
- **Generative AI & assistants**: Building applications that generate and work with content, from conversational interfaces to search across organisational knowledge.
- **AI agents & automation**: Building systems that carry out tasks and coordinate actions across software, with defined permissions, controls and human oversight.
- **Computer vision & document processing**: Interpreting images, video and documents to recognise content, extract information and support automated processing.
- **Predictive modelling & recommendations**: Developing models that forecast outcomes, identify patterns and anomalies, score options and provide relevant recommendations.
- **AI engineering & operations**: Preparing data, developing or adapting models, integrating them into software and monitoring quality, safety, performance and cost.

How we work:
- **Data assessed for the intended use**: The available data is reviewed for relevance, quality, coverage and permitted use. Gaps and assumptions are documented and addressed in model development, retrieval and evaluation.
- **Evaluation based on real tasks**: Quality measures reflect the work the system needs to perform and the consequences of errors. Models and AI features are evaluated against representative examples and an agreed baseline.
- **Controls matched to the impact**: Access, data handling, permissions and human oversight reflect what the system can influence. Outputs and actions can be reviewed, corrected or stopped according to their level of risk.
- **Approaches compared on quality, speed and cost**: Custom models, existing models and simpler software approaches are compared against the same criteria. The choice reflects the required quality, response time, implementation complexity and operating cost.
- **Quality monitored in operation**: Changes in input data, output quality, response times and costs are monitored against agreed measures. Results from real use inform improvements, model changes and decisions about retraining or replacement.

Stack: Models & serving: Claude, OpenAI, Llama, Mistral, AWS Bedrock, Ollama, MCP · Retrieval: Elasticsearch, pgvector, Qdrant, LlamaIndex, Cohere Rerank · ML & voice: PyTorch, TensorFlow, scikit-learn, Hugging Face, OpenCV, ONNX, Soniox, ElevenLabs · Evaluation & monitoring: promptfoo, Langfuse, Weights & Biases, Grafana.

Questions we get about this discipline:
- **How do we know whether AI is the right approach?** We begin with the task, available data and a measurable result. We compare an AI approach with simpler software or process changes, then test the most promising option against representative examples before recommending wider development.
- **Can you use existing models, or develop a custom one?** Both. The choice depends on the required quality, available data, privacy constraints, response time and operating cost. We can integrate an existing model, adapt one or develop a custom model where the need and evidence justify it.
- **Does our data have to be sent to an external model provider?** No single hosting arrangement suits every use case. We assess what data the system needs, where it can be processed and which providers or deployment options meet your requirements. The data flow and access controls are defined before implementation.
- **How do you check whether the output is reliable?** We define what a useful result looks like for the actual task and evaluate the system against representative examples, including difficult cases. The level of review and control reflects what could happen if the system is wrong.
- **Can people review or override AI decisions?** Yes. Human review, permissions and the ability to correct or stop an action can be built into the workflow. The right level of oversight depends on the system’s purpose and the consequences of an incorrect output or action.
- **What will it cost to run and maintain?** We consider model use, hosting, data processing, monitoring and ongoing evaluation when comparing approaches. Costs and quality are measured during development and monitored after release, so changes in demand or performance can be addressed.

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Published by Extra.dev (https://extra.dev), a software engineering team in Ljubljana, Slovenia.
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