Requirements and constraints
Clarify privacy, latency, context size, languages, GPU budget and hosting preferences.
Open-Source LLMs Services.
Private or self-hosted language-model solutions with more deployment control.
We assess licensing, hardware requirements, model quality and infrastructure costs before selecting an open-weights model.
AOZAL / EXPERTISE 0208 STAGES TO DELIVERYSCROLL TO EXPLORE ↓
01 / OUR APPROACH
Built with
intention.
A practical path from first conversation to launch, built around your goals—not a one-size-fits-all package.
WHAT WE FOCUS ON
We assess licensing, hardware requirements, model quality and infrastructure costs before selecting an open-weights model.
WHAT COMES NEXTClear milestones.
Clear milestones.
Working solutions.
Room to grow.
Every engagement is scoped according to the actual product, business and technical requirements.
02 / END-TO-END DELIVERY
How it works.
The complete workflow for open-source llms services—from discovery and planning through implementation, launch and ongoing care.
Model/license review
Evaluate candidate models, their usage terms, performance and hardware footprint.
Infrastructure planning
Provision GPU or CPU inference environments with secure networking and storage.
Model serving
Configure runtime, quantization, batching and authenticated inference endpoints.
Application integration
Add retrieval, tools, logging and business workflows as needed.
Benchmark & safety testing
Evaluate accuracy, throughput, hallucinations, privacy and adversarial inputs.
Go-live
Deploy with scaling, monitoring, rollback and versioning.
Ongoing optimization
Tune inference costs, update models and re-run benchmarks.
03 / TECHNOLOGY & TOOLS
The right stack.
Specific tools are chosen based on platform requirements, existing systems, budget and long-term ownership.
Languages & skills
PythonBashTypeScript / JavaScript (application layer)
Frameworks & methods
PyTorch / TransformersvLLM / Ollama / llama.cppRAG / embedding pipelinesREST inference endpoints
Platforms & tools
GPU server / cloud inferenceDockerQuantization and evaluation toolingObservability / access controls
TECHNOLOGY NOTE — These are representative options, not a claim that every project uses every tool. E-commerce marketplace workflows often need no programming language.
04 / TANGIBLE OUTCOMES
What you receive.
Typical project outputs. The exact scope, documentation and responsibilities are agreed before work begins.
Model shortlist / architecture recommendation
Configured inference service
Benchmark report
Deployment and maintenance runbook
HAVE A PROJECT IN MIND?