Data quality and AI validation with human judgment
Automation handles the easy cases. What remains are false positives, edge cases and ambiguous results that somebody has to check: reliably, by fixed rules and traceably. That is exactly the review we take over as a managed process, human in the loop as a service.
Where it gets expensive
Experts checking routine
Data scientists, ML engineers and QA specialists spend hours on checks that do not need their expertise, and are missing where they are needed.
Errors reach your customers
False positives, hallucinations and anomalies slip through automated checks and end up in a quote, an answer or an official letter.
No reliable evidence
Nobody can say what was checked, by which rules and why a decision was made. For audits, customers and regulators that is not enough.
Our services
Data quality sprint
The fixed-scope entry point: one dataset or one AI system. We draw a sample, name the error types, build the validation rules and propose an ongoing process.
Ongoing data quality operations
Recurring validation of databases, catalogues, CRM data and documents. You hand over the workflow, we return validated data and reporting.
Review of AI outputs and edge cases
Human review of false positives, ambiguous results and the cases that need judgment, each checked against your source of truth.
AI evaluation
We define 100 to 500 test scenarios for your chatbot, RAG search or assistant and score correctness, sources, hallucinations and edge-case behaviour.
Regression and quality monitoring
Fixed test scenarios re-run after every change to model, prompt or context. We detect what broke and track the error rate month over month.
Data and RAG readiness
Cleaning, annotation and reference data. Documents become AI-ready: deduplicated, versioned and tagged, so your search finds the right thing.
How the collaboration works
You set the goal and the reference
What is an error, what is correct? You define the source of truth and the goals; we translate them into validation rules.
We build the process
Rules, error taxonomy, escalation paths and quality control, so that the decision on item 1,000 is the same as on item 10.
A dedicated team validates
As a sample in the sprint or continuously as an operation. You do not manage individual people; you receive a result.
You receive a traceable result
Validated data, error classification, an edge-case log and a report you can pass on to business units, customers or auditors.
What you get
- Validated and corrected datasets or scored AI outputs
- Error classification with frequencies, so you fix causes instead of symptoms
- An edge-case log with the reasoning behind each decision
- A summary report with metrics and recommendations
- Validation rules and test scenarios that belong to you and can be reused
Frequently asked questions
What does human in the loop mean?
Automated checks decide the clear-cut cases. Everything that is ambiguous or needs context goes to people who decide according to fixed rules and document their decision. The loop closes when those decisions flow back into rules, training data or test cases.
Which systems and data is this for?
Chatbots and assistants, RAG search, scoring and classification models, product catalogues, CRM data, address and registry data, and document collections that need to become AI-ready. What matters is that there is a reference to check against.
How do we best get started?
With a data quality sprint: one dataset or one AI system, fixed scope, defined result. You see from a real sample which error types occur and what an ongoing process would deliver.
Let's talk about your project.
An initial conversation is free and usually takes 30 minutes. Afterwards you will know what needs doing and what it costs.