Industry-grade quality and service
Steady adherence to company terminology, response structure, brand tone, and business boundaries — with fewer repetitive long prompts.
Model fine-tuning
Make the model truly adaptable to your business standards
Fine-tuning is not simply importing files. Model behavior is adjusted with governed, labeled samples. We plan to provide integrated enterprise services covering objective definition, data preparation, training experiments, and evaluation-based delivery.
When to fine-tune
Suitable for stable, repeatable, measurable tasks
Fine-tuning suits behavioral consistency and task-specific performance. For frequently changing factual knowledge, prefer retrieval, tool calls, or business data provisioning.
Classification, extraction & structured output
Classify tickets, extract fields, moderate content, and enforce fixed JSON output — consistent results for high-frequency tasks.
Rule judgment and process assistance
Annotated business samples improve model stability on specific criteria and processes.
Dedicated assistants and multi-task capabilities
Combine task types around internal roles and turn a dedicated version into continuous, evaluable workflows.
Situations where direct fine-tuning is not recommended
Unclear goals, no test sets, insufficient sample size or quality — or just wanting the model to memorize information that keeps changing. Once the service opens, we complete a task and data feasibility check first.
Delivery workflow
Every version has to prove it's better
Training is only the middle step. Full delivery requires a reproducible data version, experiment records, and independent test results.
- 01Goal vs. Baseline
Define the business task, success metrics, and unacceptable outcomes, and establish a reproducible performance baseline with the base model.
- 02Data Preparation
Complete sample cleaning, deduplication, format checks, and sensitive-information handling, then split data into training, validation, and test sets.
- 03Training experiments
Choose candidate training methods based on the model and task, and record data version, specs, cost, and results for each experiment.
- 04Independent Evaluation
Check accuracy, format compliance, robustness, safety, and regression risk. Never substitute training loss for business acceptance.
- 05Version delivery
Lock in the model version, assessment report, and calling method, then hand off to hosting or the API publishing process.
Data & evaluation
Training data is just as important as evaluation criteria
Before you submit a task, we plan to check data ownership, format, quality, and sensitive-information handling, and to require a held-out test set.
- Data availableClear, authorized source with consistent formatting relevant to the target task
- Learnable examplesInputs, ideal outputs, and annotation rules are clear, with no conflicting examples.
- Test independenceAcceptance data never participates in training; it measures real lift and regression risk
Task qualityAccuracy, recall, manual scoring
Output constraintsFormat correctness rate, command compliance rate
StabilityBoundary samples, adversarial samples, regression set
PerformanceLatency and throughput vs. cost per inference
Planned console
A full management path reserved for later self-service access
Once the service opens, fine-tuning tasks will move to your console, connected to your existing account, models, billing, and API management.
ModuleWhat it coversPurpose
DatasetUpload, versioning, and checksum permissions
Make training data sources and every change traceable
TaskBase model, specs, progress & logs
Unified management of experiment status and resource consumption
Evaluation centerBaseline vs. test set and manual acceptance
Compare candidate versions against the same standard
Model revisionRelease, description, status & rollback
Hand the accepted version to the deployment process
Cost recordsTraining resources & task-level billing
Separate training costs from subsequent inference costs
Fine-tuning inquiry
Submit fine-tuning requirements
Tell us about your objectives, data readiness, and expected outcomes. There's no cost to submitting an inquiry before the service opens.
- Contact email
- contact@taidatech.ai
- How it works
- Inquiries reach our operations team and are handled in the order they arrive.
- Helpful to include
- Business goals, current stage, data readiness, and traffic or resource estimates
Prepare now
Validate the base model first, then decide whether fine-tuning is needed.
Test prompts and fundamentals in the Playground first, then collect failure samples and acceptance criteria.