Data Quality & Readiness
Assess dataset structure, duplication, missing information, label quality and train–test separation before development or evaluation.
- Data audit
- Sample review
- Prioritised fixes
Benchmarking
Know how your data and models perform.
Task-specific evaluation of datasets, models and processing workflows, with reproducible methods, clear comparisons and practical findings.
Capabilities
What we evaluate
Evaluation for space technology teams, shaped around your task, data and operating conditions.
Model Comparison
Compare candidate models on the same task and agreed test data, with consistent metrics and analysis of failure cases.
- Comparison report
- Results tables
- Reproducible evaluation scripts
Robustness & Generalisation
Examine performance across relevant regions, sensors, conditions and data shifts to identify weaknesses beyond average scores.
- Scenario results
- Failure analysis
- Improvement priorities
Efficiency & Deployment
Measure the trade-offs between task performance, latency, throughput and memory use on an agreed computing platform.
- Runtime profiles
- Configuration comparison
- Deployment findings
Workflow Evaluation
Compare complete data-processing pipelines, including preparation, filtering, compression and inference, against agreed outcomes.
- Pipeline comparison
- Quality and cost analysis
- Technical recommendations
Application Areas
Potential application areas, scoped around your technical question.
Focused evaluation
Private Benchmark Pilot
A focused evaluation designed around one practical technical decision.
Scope, data access conditions and a quotation are confirmed before the pilot begins.
Model training, additional integration and new tests are agreed separately.
Suggested scope
- One task and one agreed-size dataset
- Up to three existing, runnable models
- An agreed evaluation environment and compute budget
- A report, results tables and reproducible evaluation scripts
- One findings review session
Process
How We Work
- Define the question
- Agree data and metrics
- Run and review
- Deliver findings
Evidence you can use
What Makes Results Useful
- Traceable versions and data splits
- Record the dataset versions, train–test splits, model checkpoints and software versions used for the evaluation.
- Defined metrics and environment
- Document the metrics, configurations and computing environment so comparisons can be interpreted and repeated.
- Failure cases and limitations
- Show where systems struggle, gaps in test coverage and the limits of the findings.
- Data and results handling
- Private customer data and results are handled under agreed terms. Public release requires the customer's authorisation.
Methods
Methods and limits
Findings apply to the evaluated data, software and operating conditions. Hardware-specific claims require measurements on the relevant platform.
Measured energy use
Energy use is reported only when it has been measured as part of the agreed evaluation.
Explicit simulation assumptions
Simulation results are labelled as simulated, with assumptions and scenario conditions recorded.
Evaluation scope
This service does not provide flight qualification, radiation testing or formal certification.
Keep the evidence current
Periodic Re-evaluation
When data, models or operating conditions change, arrange a repeat evaluation. We agree the updated scope and document changes so findings can be compared with earlier runs where appropriate.
