MODEL EVALUATION & SPECIALIZED TRAINING

Select models, train specialized capabilities and improve outcomes

Start from actual business tasks to compare models, diagnose data, knowledge and retrieval issues, and train and deploy specialized capabilities across text, multimodal, vision, actions, time series, traditional ML and hybrid systems.

Open model console Discuss a model task

MODEL CONSOLE

Start using model capabilities here

VECTOKEN MODEL CONSOLE

Multiple models. One console.

The Vectoken model console brings multiple large models into one entry point for model use and application exploration. Task evaluation, optimization and specialized training are available as professional services.

Go to model console

MODEL CONSOLEvectoken.com/console

Unified access · Multiple large models

MODEL SERVICES

Four services with defined deliverables

Available independently or as part of product delivery and ongoing upgrades.

Task evaluation & model selection

Generic leaderboards do not establish which candidate fits your task.

Work: Define task success; prepare an independent evaluation set; reproduce candidate results; analyze errors and cost per valid outcome.
Deliverables: Task brief, evaluation-set documentation, comparison report, primary and fallback choices, operating boundaries and reproducible records.

Service details

Model & RAG optimization

A running system lacks grounded answers, misses evidence, requires excessive review or costs too much.

Work: Collect problem examples → Locate bottlenecks → Improve retrieval and models → Run regression evaluation → Release under control.
Deliverables: Diagnosis, knowledge or retrieval configuration, optimization plan, before-and-after evaluation, deployment and rollback instructions.

Service details

Specialized model training

General capabilities do not meet domain accuracy, field conditions, latency or edge deployment requirements.

Work: Define labels and data standards; separate training, development and test data; choose vision, time-series, traditional ML or fine-tuning methods; train, independently evaluate and deploy.
Deliverables: Data specifications, annotation or training assets, weights, adapters or an inference service, model cards, configurations, evaluation and deployment documentation.

Service details

Ongoing model operations

Data, business needs and models change after launch, requiring defined monitoring, updates and rollback.

Work: Establish quality and cost baselines; monitor drift and errors; collect feedback and difficult cases; evaluate, approve and release through quality gates.
Deliverables: Operating reports, cost and drift monitoring, update plans, regression evaluation, version records and rollback plans.

Service details

QUALITY & COST

Measure task outcomes and track improvements by version

Quality, stability, latency, cost per valid result and deployment fit determine the solution. Evaluation and training data are managed separately; expert review and regression gates control changes.

Prepare a model-service enquiry