Solution · 13
AI Safety & Model Testing
Test AI before launch and keep watching it after launch.
The problem
Why this exists
AI features go live without agreed rules or a real test set, so quality problems appear in front of customers.
- There is no agreed definition of a good answer.
- Nobody notices when quality drops.
- Sensitive data handling is unclear.
- Changes are made without a retest.
What it does
- Agreed AI rules and acceptance criteria
- Test set built from real and difficult cases
- Evaluation report before launch
- Live monitoring, alerts and audit records
How it works
The steps we run
- 01
Agree rules, risks and success measures
- 02
Build the test set with experts
- 03
Test and fix before launch
- 04
Monitor quality, cost and response time
- 05
Retest after every change
Services combined
Built from our core capabilities
Measures of value
How we measure success
Lead industries
Where it fits best
See what ai safety & model testing would look like for you
Tell us the problem, the systems and the target. We come back within one business day with scope, first result and timing.
Other solutions
01
Web & Market Data Monitoring
Know when important prices, products, suppliers, sellers or rules change.
02
Model-Ready AI Training Data
Give your ML and LLM team clean, labeled and reliable data.
03
AI-Ready Business Data
Create one trusted data base for reporting, automation and AI.
04
Document Processing Automation
Process invoices, orders, contracts and forms with less typing.
FAQ
AI Safety & Model Testing questions, answered
How this solution scopes, integrates, and proves value.
It means agreeing what a good AI answer looks like, building a test set from real and difficult cases, scoring the system against that test set before launch, and monitoring it afterward. We agree the rules, risks and success measures with your team, build the test set with domain experts, then test and fix issues before launch and set up ongoing monitoring. Most clients complete an initial evaluation within three to five weeks.
Yes, we can review an existing AI system, agree acceptance criteria with your team, build a test set from real usage, and set up monitoring even though it is already live. This is common when quality problems have already appeared in front of customers and a team needs an independent check. The review gives you a clear evaluation report and a monitoring setup going forward.
We need access to the AI system, examples of good and bad past answers, and a domain expert who can confirm what counts as correct. Cost depends on the complexity of the system and the size of the test set required, quoted after a short scoping session. Most engagements start with a fixed-price evaluation project, then move to a monthly monitoring fee once the system is live.
Retesting should happen after every change to instructions, data or model, and on a regular schedule once the system is in production, since small changes can shift answer quality without warning. Live monitoring tracks answer quality, response time, cost and escalations continuously, with alerts sent to the owner when results drift. This keeps testing an ongoing practice rather than a one-time checkbox.
Vendor claims are usually based on general benchmarks, not your specific use case, data or risk tolerance. AI safety and model testing builds a test set from your real and difficult cases, sets acceptance criteria your domain experts agree with, and monitors your live system continuously rather than a one-time vendor benchmark. This means quality is measured against what actually matters to your business and customers.
Proof
Where this work paid off
Healthcare & Pharma Distribution
Control complex contracts, rebates and claims without losing oversight
Contract, rebate and claim rules were complex and varied by product and customer. Controlled documents needed careful handling, and any AI use needed evidence and audit records. DataplexLabs built a contract and rebate data model, automated document review and set up model testing and approval workflows.
Read the case study →Software & AI Companies
Ship model-ready datasets and safer AI products faster
Engineers were spending most of their time cleaning and labeling data. There was no test set that reflected real customer cases, and quality problems were found by users. DataplexLabs built a repeatable data pipeline, created labeled evaluation sets and introduced regression tests before release.
Read the case study →Next step
Discuss your use case
Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.