Local & Private LLMs

Overview

Public LLM APIs are fast to adopt but come with data-residency, confidentiality, and cost trade-offs that many regulated organizations cannot accept. We build the alternative: open-weight models selected, fine-tuned, and optimized for your specific use case, running on hardware you own or lease, with full visibility into every input and output.

Common challenges we address

• Contractual or regulatory restrictions on sending data to external AI providers
• Unpredictable per-token costs at production scale
• Latency requirements that a shared public API cannot guarantee
• Need for a model fine-tuned on internal terminology, documents, or processes

What we deliver

1. 01

   Model selection & benchmarking

   We evaluate open-weight model families against your accuracy, latency, and hardware-cost requirements before committing to an architecture.

2. 02

   Fine-tuning & retrieval pipelines

   We adapt base models to your domain using fine-tuning, retrieval-augmented generation, or both, depending on how your data changes over time.

3. 03

   Inference infrastructure

   We size and configure the GPU or accelerator infrastructure needed to serve your expected load, on-premise or in a private cloud.

4. 04

   Monitoring & guardrails

   We put logging, evaluation, and output-filtering in place so the system stays observable and safe after we hand it over.

Outcomes

• Sensitive data never leaves your network boundary
• Predictable infrastructure cost instead of variable per-token billing
• A model tuned to your terminology, not a generic public assistant
• Full audit trail over every prompt and response