Put AI to work on the outcomes that matter to your business: lower costs, better quality and more efficient operations. Xfaang selects, trains or adapts models for your industry's tasks, deploys them in your environment and connects them to your systems.
Discuss your AI use caseControl over your data. Process prompts, documents and model outputs within your infrastructure. We design the deployment around your access rules and data boundaries, including fully local processing where required.
Models adapted to your work. We select the training and adaptation approach for your business objective and available data. Language models can be adapted through fine tuning using relevant examples from your industry. Predictive and vision models can be trained for specific tasks. We evaluate their performance on cases that reflect your team's work.
Less dependence on external AI APIs. Choose when to update your models and how to run them. A fully local deployment can operate without an external model API and, where the complete workflow supports it, without an internet connection.
An operating model that fits your workload. We compare infrastructure, maintenance and usage costs against hosted alternatives. For suitable workloads, local deployment can reduce recurring API spend; the business case depends on volume, hardware and operational requirements.
From model selection to a working system
Optimize production. Strengthen your margins.
Measure the business impact
Applications across industries
Define the task. Identify the workflow, expected outputs and measurable success criteria.
Select the model. Compare candidates against your quality, licensing, hardware and deployment requirements.
Prepare the data. Curate training examples and separate evaluation data with appropriate access controls.
Train, adapt and evaluate. Train predictive or vision models, adapt language models through fine tuning, or configure optimization algorithms for planning problems. Test against representative cases and the current process.
Deploy and integrate. Run the system in your environment and connect it to your applications and workflows.
Maintain and improve. Monitor quality, manage model versions and evaluate updates before rollout.
Use your production data to identify where time, materials and energy are lost. We build and deploy models that help your team anticipate disruptions, detect defects and improve planning, with results measured against your current operations.
Reduce waste and rework. Detect defects and identify process conditions associated with poor quality.
Reduce unplanned downtime. Use equipment data to identify warning signs and support maintenance planning.
Improve production planning. Optimize schedules around capacity, material availability, changeovers and delivery requirements.
Use resources more efficiently. Identify opportunities to reduce material and energy consumption per saleable unit.
Understand production economics. Combine production and cost data to reveal costly deviations and support decisions about products and orders.
We start with a defined bottleneck and a measurable target. The pilot compares the proposed approach with your existing process and accounts for deployment and operating costs before scaling.
Depending on the use case, we track cost per saleable unit, scrap and rework rates, unplanned downtime, throughput, delivery performance and energy consumption per unit. Where reliable commercial and cost data are available, we also assess the effect on the agreed margin measure for products or orders.
Improved throughput becomes a financial benefit when additional output can be sold or resources can be redeployed. Margin impact is assessed after accounting for implementation, infrastructure and operating costs.
Illustrative use cases to explore together:
Manufacturing: optimize production planning, support predictive maintenance, detect defects and analyze production costs; assist teams with technical documentation.
Financial services and insurance: organize documents, extract defined fields and assist staff with internal procedures.
Legal and professional services: search internal knowledge, classify documents and assist with drafting for expert review.
Retail and online commerce: classify product information, draft catalog content and assist customer support teams.
The model, training approach and evaluation criteria are selected for the specific task and available data.
Local AI & Model Training: common questions
It is Xfaang's service for selecting, training or adapting AI models and deploying them on a company's own infrastructure. The work can include production optimization, data preparation, model training and fine tuning, integration with internal knowledge and systems, evaluation and maintenance.
We select the approach for the use case. In many projects, adapting a pretrained model is more practical than training a new foundation model. Fine tuning changes how a model performs selected tasks; connecting internal knowledge sources helps it use relevant information at runtime. These approaches can be combined.
No. Depending on the task, we can use predictive models for equipment and process data, vision models for quality inspection, language models for documents and knowledge, and optimization algorithms for planning. The approach is selected for the business objective, available data and deployment requirements.
Yes, when the complete workflow is deployed locally. We assess model inference, document processing, search, integrations and monitoring together so the required data boundary applies to the whole system.
Costs depend on usage volume, model size, hardware and maintenance. We compare total operating costs with hosted alternatives before recommending a deployment approach.
Local AI & Model Training focuses on the models, their adaptation and where they run. Agentic Services focuses on agents that carry out tasks and workflows. Agents can use locally deployed models when the use case calls for them.