Capability
Model and data assessment
Examine the task, available data, quality targets, latency, cost, and deployment constraints before choosing an approach.
AI Engineering / Model Training & Fine-Tuning
Fit the model to the work, not the work to the model.
Find the model approach that meets your domain, quality, latency, cost, and deployment constraints with evidence.
Discuss this serviceThe business problem
What brings this work into focus.
General-purpose models may miss domain language, cost too much at scale, respond too slowly, or perform poorly on specialized inputs. Your team needs to know whether prompting, retrieval, fine-tuning, or a specialized model is the soundest path.
System delivery
Define the boundary, build the capability, and prepare it for real use.
Define the system
Build in working slices
Measure and operate
Capability
Training and adaptation
Fine-tune an existing model or train a specialized model when the evidence supports that investment.
Capability
Performance evaluation
Compare candidates against representative cases and document the tradeoffs that affect production use.
Working outputs
What the engagement produces.
- Model approach and data-readiness brief
- Reproducible trained or fine-tuned model
- Evaluation report and deployment recommendation
Fit guidance
Use the approach that matches the constraint.
This is useful when
Useful when a repeatable, domain-specific task is not meeting its quality, cost, privacy, or latency target with a standard model.
A simpler path may be better when
Start with stronger prompting or retrieval when the main need is access to current knowledge rather than changed model behavior.
Related offerings
The adjacent work that often matters.
Start a conversation
Talk with us about model training & fine-tuning.
We will help you identify the useful first move and say plainly when a simpler option is the better answer.