Enterprise AI doesn't need to know everything. It needs to understand your business.
Far adapts open-weight models to each company's key tasks, deploys them privately and measures them against real outcomes.
More than a model behind an API.
| Aspect | A closed-weight model through an API | Far |
|---|---|---|
| Knows your business | Only what fits in the prompt | Through structured context from Far Foundation |
| The model | Closed weights; the provider sets versions | Open-weight, adapted to the task; you control versions |
| Your data | Processed by the provider | Processed in private, controlled environments |
| Cost at scale | Per request | The cheapest model that performs best on each task |
When a general model with retrieval is enough, Far uses one, always open-weight. Closed-weight models are never used: your data would have to leave your control.
How specialization works.
A smaller model adapted to one well-defined task can match or beat a much larger one on that task.
Base model
Open-weight, chosen for the task
Task data
Real examples, validated
Adaptation
Only where it helps
Evaluation
Against simpler and larger options
Optimization
Sized to the real workload
Deployment
Private, versioned, monitored
Methods, chosen per task: fine-tuning, LoRA, reinforcement learning, distillation, quantization and domain embeddings.
Fine-tune only when it wins.
Rules, forecasting, classical ML or a language model: each task gets the simplest system that meets the goal.
Quality
Accuracy, and how serious each error is
Speed
Latency at your real volumes
Cost
Per successful task, all in
Operations
Monitored, retrained, reversible
Build once. Reuse. Adapt privately.
The next customer with a similar need starts from a working, evaluated model, not from scratch.
1
General capability
Language, vision, reasoning
2
Reusable task specialization
Quote requests, technical documents, media cataloguing, decision workflows
3
Your company's adaptation
Your terms, rules and catalogues, kept in your environment
Eight layers, one architecture.
Far Foundation
- 1
Connectors
ERP, databases, SharePoint, files
- 2
Data processing
Schemas, mappings, entities
- 3
Semantic context
Metadata, relationships, embeddings
Far Decisions
- 4
Model development
Open-weight models, fine-tuning, ML
- 5
Inference
Private serving and routing
- 6
Decisions
Forecasts, recommendations, workflows
Across both
- 7
Evaluation
Accuracy, cost, outcomes, drift
- 8
Governance
Access, isolation, audit
Start with one decision that matters.
In a first conversation we look at the problem, the information you already have and whether there is a good first use case to measure.
or write to hello@farsystems.eu