Custom objective and workflow
Language and reinforcement objectives, custom schemas, prompt encoders, and multi-stage workflows, plus other objectives after a capability review.
Custom model training
A model program built around your company, not a preset.
For teams whose model, data, infrastructure, or deployment requirements sit outside the self-service lane, Farka scopes a custom path from feasibility through verified delivery.
Built for the exception
Bring the requirement the catalog cannot express.
The custom lane extends the same evidence-first workflow without forcing a complex company deployment into self-service defaults.
Model and compute fit
Larger dense models, sparse or MoE architectures, multi-accelerator plans, and non-catalog targets receive a feasibility and capacity review.
Your infrastructure boundary
Managed compute, private networking, regional constraints, or customer-cloud deployment are designed into the plan before training begins.
Evaluation and handoff
Define task-specific holdouts, promotion thresholds, artifact ownership, retention, and the delivery package your operators can use.
Engagement path
One accountable path from question to working artifact.
Define
Align on the task, available data, success criteria, delivery boundary, and constraints.
Design
Select the model, training objective, evaluation plan, infrastructure, and commercial scope.
Train
Run the reviewed workflow with observable spend, durable evidence, and holdout evaluation.
Deliver
Transfer verified weights or deployment artifacts with the agreed operational handoff.
A useful first conversation
Tell us the outcome, not the training recipe.
Share what the model should do, what data exists, where it must run, and any timeline or security constraints. We will identify the open questions and whether Farka is a fit before proposing paid compute.
Helpful, not required
- Company and use case
- Representative data type and volume
- Candidate model or parameter range
- Deployment, privacy, and region needs
- Target quality measure and timeline
Every custom request receives data-rights, model-license, safety, infrastructure, and feasibility review before a commercial proposal or compute spend.