Generic models are expensive, slow, and wrong in the ways that matter to you. We fine-tune and deploy LLMs built on your domain, your docs, and your use cases — measurably better on accuracy, latency, and cost per query.
Key performance indicators
Model 'Hallucination' rate reduction
Inference latency (ms per token)
Domain-specific accuracy score (MMLU benchmark)
Operational cost per 1k tokens
Delivery plan
Custom LLM Engineering is delivered in discrete milestones, with weekly checkpoints and transparent reporting.
Milestone-based delivery
Progress you can verify, sprint by sprint
Phase 1
Analyze constraints and define scope
Phase 2
Execute delivery in phased iterations
Phase 3
Validate system scale and security
Phase 4
Optimize and govern live operations
Deliverables
Concrete, verifiable artifacts produced during delivery — quality you can audit, not promises.
Domain-specific fine-tuned model
Evaluation report (Accuracy vs. Base model)
Secure inference API and monitoring dashboard
Model retraining and versioning pipeline
What we measure
Every engagement is tracked against results you can put in front of your board — not effort, outcomes.
Production solutions deployed with high confidence
Transparent stakeholder visibility on progress
Minimized delivery risks and operational overhead
How we integrate
How our teams plug into yours — from day one.
Custom LLM engineering with measurable accuracy gains, disciplined delivery, and costs you can actually forecast.
2000+ vetted engineers · 3 global hubs · 98% client retention
FAQs
Questions about our process, pricing, or technology? Clear answers to the most common ones.
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