Controlled company knowledge
Ground answers in approved documents, technical records and procedures with source-aware retrieval and access boundaries.
01 THE DEPLOYMENT
Engineer the whole operating system around the model.
Sosteco designs and deploys private AI systems for organizations that need control over data location, model behavior, access and operating cost. We evaluate the workload first, then select the smallest architecture that meets the quality, latency and privacy requirements.
02 OUTCOMES
What the engagement is built to change.
A measured model choice
Compare hosted, open-weight and specialized models against representative tasks instead of selecting from benchmark headlines.
Production inference
Size GPU or CPU infrastructure, optimize latency and throughput, and build observable services that your team can operate.
03 ENGINEERING SCOPE
From constraints to an operable deployment.
- Use-case discovery, data classification and deployment-boundary design
- Model benchmarking, quantization and inference optimization
- Retrieval-augmented generation, vector search and document pipelines
- Identity, permissions, citations, guardrails and human review
- Evaluation suites, monitoring, cost controls and operational handover
04 DELIVERY PATH
Evidence before scale.
Benchmark
Build a representative task set and establish quality, latency, privacy and cost targets.
Architect
Choose the model, retrieval approach and infrastructure around the actual constraints.
Integrate
Connect trusted sources, identities and workflows with explicit permissions and review points.
Operate
Deploy with evaluation, observability, incident paths and documentation for your team.
05 PRACTICAL QUESTIONS
Before the first technical decision.
Does a local LLM mean the system must be fully offline?
No. Local can mean on a workstation, an on-premises server, a private cloud or an edge gateway. We choose the boundary according to data sensitivity, connectivity, latency and operating requirements.
Can an open model answer from our internal documents?
Yes, when retrieval, permissions and document quality are engineered together. We test whether RAG, fine-tuning or a simpler search workflow is the right approach for the task.
How do you select inference hardware?
We benchmark the target models with realistic context lengths, concurrency and latency goals before sizing GPU memory, compute, storage and redundancy.
07 START WITH THE CONSTRAINT
Bring us the real workflow.
We'll define the smallest deployment that can prove technical feasibility and operational value.