Service
Intelligent AI systems, LLM application development and fine-tuning, document intelligence, knowledge retrieval, AI agents, and workflow automation designed around real business problems.
How it works
Trigger → Agent → Tools → Review
LLM engineering
Featured capabilityWe develop applications powered by large language models and adapt suitable pretrained models to specific tasks, domain requirements, and real-world workflows.
Design and develop applications using large language models, including conversational AI, AI assistants, document understanding, and domain-specific language-model applications.
Adapt suitable pretrained language models to specialized tasks using appropriate datasets, fine-tuning techniques, and systematic evaluation.
Fine-tuning is an option, not a default. Many applications are better served by prompt design, retrieval or tool integration, so we recommend fine-tuning only when the use case, the available data, evaluation results and deployment constraints justify it. We build on existing pretrained models, API-based or open-source, rather than training foundation models from scratch.
The problem
Teams lose hours to repeatable work and to knowledge that sits in documents nobody can search properly.
What we do
Map the workflow and find where AI genuinely helps.
Build LLM applications, agents, retrieval and automations around your own documents and data.
Keep people in control with review points, logs and cited sources.
LLM development workflow
05 stagesThe right approach depends on the use case. Each stage decides what the next one needs, and fine-tuning is used only where it is justified.
01
Understand the task, intended users, required outputs, data constraints, and success criteria.
02
Evaluate whether an API-based, open-source, or self-hosted model is appropriate for the requirements.
03
Choose appropriate techniques such as prompt engineering, RAG, tool integration, or fine-tuning where justified.
04
Test output quality, consistency, relevant failure cases, latency, and operational costs.
05
Integrate the selected approach into the intended application or workflow according to the agreed technical requirements.
What it covers
Typical use cases
Examples of fit, not a client list
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