Testing AI produced results
Human evaluation of what your AI systems produce - before users trust it. We score LLM and chatbot answers for accuracy, tone, and safety; review generative images, video, audio, and code; and check RAG or knowledge-base answers against the sources they should use. Includes eval support for AI bots and agents. For physical robots, we support data review and evaluation of robot learning tasks you define. Process discipline, multiplatform checking habits, and clear defect-style reporting applied to non-gaming AI outputs.
Training AI and Datasets
Labeling, annotation, and curated datasets that help your AI improve - plus human-in-the-loop review and prompt / eval harnesses. Ankatmak builds and curates evaluation and training sets for chatbots, AI bots and agents, RAG systems, and carefully scoped robot-learning data tasks. Fine-tuning and GPU compute run through partners when needed.
AI Quality Engineering
One area seeing rising demand is AI Quality and AI agent testing - especially as teams move AI features and agentic workflows toward production.
With Ankatmak AI Engineering and AI Quality, the goal is practical: help organisations move from AI experiments to reliable, production-ready solutions.
AI features fail quietly. A fluent answer can be wrong, ungrounded, biased, or unsafe - with no 500 error to wake traditional QA. Ankatmak helps non-gaming teams define what "good" looks like, then measure it before users trust the product.
What we cover on an agreed brief:
- LLM evaluation: scored checks across prompt and model versions so "better" is evidence, not a demo vibe
- Prompt testing and regression: suites so a wording tweak does not silently break production behaviour
- RAG pipeline checks: retrieval relevance, grounding against sources, and behaviour when retrieval returns nothing useful
- AI agent testing: multi-step task correctness, tool use, and recovery when a step fails
- Hallucination and groundedness: catch confident-but-wrong answers before customers see them
- Bias probes: structured checks across cohorts and topics you care about
- AI security smoke: prompt injection, jailbreak attempts, and basic data-leakage attempts against your system
- Multimodal review: image, audio, video, and voice outputs alongside text when those modalities are in scope
What we measure before production
- AI and LLM accuracy, faithfulness, and groundedness
- Agent behaviour and workflow completion
- Tool and API call accuracy
- Consistency and edge cases
- Failure and recovery scenarios
- AI regression and release readiness
Where we work across the AI lifecycle
- AI-powered applications and LLM-based solutions
- AI agents, copilots, and agentic workflows
- AI-driven business process automation
- AI integrations with products and systems you already run
- AI-assisted test automation
- AI and LLM application testing and evaluation
- AI agent evaluation and workflow validation
- Accuracy, reliability, consistency, behaviour, and edge-case evaluation
Build and run with Ankatmak delivery; measure quality with Testing AI produced results, Training AI and Datasets, and AI Quality Engineering - still pay when satisfied where the live USP applies.
Eval engagement (fits the USP steps below): define correct with golden examples and rubrics, baseline the live feature, pilot where it fits, then gate and expand with regression packs from real failures.
Artefacts you keep: rubric and scenario packs, labeled findings, defect-style reports, and a lightweight eval checklist your team can reuse. CI wiring and custom harness code are scoped per brief.
Enquire via the contact form or Sales@GameCloud-Ltd.com with Ankatmak AI Quality in the subject. Pay when satisfied where the live USP applies.
