Fine-tuned models for accuracy,
cost control, and reliability
When prompting isn't enough, we fine-tune SLMs/LLMs or build custom models—backed by benchmarks, deployment packaging, and monitoring plans.
When to use what
Use Prompting
When rules are simple and risk is low
- Simple classification
- Low stakes
- Fast iteration needed
- No training data
Use RAG
When answers depend on changing knowledge
- Dynamic knowledge base
- Need citations
- Frequent content updates
- Multiple sources
Use Fine-tuning
When you need consistent formats + high precision + domain behavior
- Consistent output format
- Domain-specific language
- High precision required
- Cost optimization
What you get
- Strategy: SLM vs LLM, fine-tune vs RAG vs hybrid
- Dataset plan (labeling/governance)
- Tool integrations (APIs/actions)
- Benchmark report (precision/recall, latency, cost)
- Deployment package (API wrapper + rollback plan)
- Monitoring hooks + documentation
How it works
Baseline
Assess current
Data Prep
Label + clean
Train
Fine-tune model
Evaluate
Benchmark
Deploy
Production
Monitor
Track drift
ISO-backed delivery
Certified processes & security
Security-first
Enterprise-grade controls
Full documentation
Runbooks + handover
Frequently asked questions
What data do you need?
Labeled examples of inputs and desired outputs. We help with labeling strategy and data augmentation
Can it call our APIs safely?
Yes—with action approvals, rate limits, logging, and rollback capabilities for sensitive operations
What's the timeline?
Typically 4-8 weeks from data prep to deployed model, depending on data readiness and complexity.
How do you measure accuracy?
We build eval suites with precision/recall, latency, cost metrics, and human evaluation for quality
How do you reduce cost?
Smaller fine-tuned models often outperform larger prompted models at lower inference cost.
Ready to get started?
Let's discuss your use-case and map the fastest path to production.