Two ways to build better AI
Helping organisations ship dependable AI, and equipping engineers to build and evaluate it.
AI engineering & consulting
We partner with your team to audit system architectures, build automated evaluation pipelines, and harden AI agents for real-world reliability.
Explore consulting →Practical AI education
Rigorous, hands-on learning that teaches engineers how to test failure modes, design evaluation benchmarks, and architect production-grade AI systems.
Explore courses →Reliability is a continuous discipline, not a one-time test.
AI systems behave probabilistically. Managing that reality requires deliberate architecture, continuous evaluation, and tight feedback loops throughout the system lifecycle.
Define Failure Boundaries
Establish operational constraints, edge-case failure modes, and quantitative acceptance thresholds before writing prompts or code.
Architect Resilient Systems
Engineer deterministic guardrails, structured tool interfaces, and graceful fallbacks around probabilistic model outputs.
Evaluate Systematically
Execute automated benchmark harnesses, adversarial stress-testing, and regression gates in CI/CD before any deployment.
Learn & Adapt
Capture production telemetry, detect distributional drift, and continuously feed edge-case failures back into benchmark suites.
Engineering judgement over shortcuts.
Applying architectural discipline and empirical evaluation to build AI applications that remain reliable and maintainable in production.
Systems thinking over isolated demos.
Evidence-based evaluation over assumptions.
Engineering judgement over framework dependency.
Continuous improvement over one-time testing.
Make AI more reliable — by design.
From early technical decisions to production operation, RightQA focuses on the engineering practices that help AI systems behave more reliably and remain maintainable.
AI Systems Assessment
Understand the current state of an AI application or agent, identify architecture and quality risks, and establish practical priorities for improvement.
Discuss an assessment →AI Evaluation Engineering
Develop systematic ways to test AI behaviour, measure quality, examine failure modes and build evaluation into your development lifecycle.
Discuss evaluation →Production AI Engineering
Engineer AI applications and agents with attention to architecture, integration, observability, operational concerns and maintainability.
Discuss your AI system →AI Quality & Reliability Advisory
Ongoing technical guidance to help teams strengthen their engineering practices, evaluation strategy and approach to AI quality.
Discuss ongoing support →Design Reviews
Structured review of agent workflows, model trade-offs, and failure boundaries.
Evaluation Reviews
Reviewing regression test coverage and quality thresholds ahead of production releases.
Failure Hardening
Turning production incident traces into hardened schemas, prompts, and guardrails.
Build the skills to engineer reliable AI.
Practical, engineering-led education for people designing, building, and evaluating AI applications and autonomous agents. The focus is on understanding real trade-offs and developing empirical engineering judgement — not just following a framework.
Engineering Reliable LLM Applications
Practical, code-first training for engineers building production LLM features who need systematic evaluation, deterministic fallbacks, and regression safety.
Production AI Agents & Tool Reliability
Advanced architectural training for senior engineers and tech leads deploying multi-step autonomous agents, complex tool suites, and self-healing systems.
Cohorts are strictly limited to 25 engineers to maintain direct code-review feedback, 1-on-1 architecture sparring, and private GitHub lab repositories with lead practitioners who ship production AI.
Ideas for building AI that works in the real world.
Perspectives on AI engineering, deterministic evaluation, and the practical decisions involved in taking AI systems into production.
Why 'Vibe Checking' Fails: Building Deterministic Eval Gates for Production LLMs
Why manual spot-testing leads to silent regressions in production AI systems, and how to build quantitative, automated evaluation harnesses using Pytest and CI/CD gates.
assert groundedness >= 0.92
assert not invalid_citations
assert latency_p95 <= 1800ms
# Gate passed: Regression delta = +0.0%Building an AI application or developing your engineering skills?
Start a conversation about architectural consulting, evaluation engineering, or upcoming training cohorts.
Whether you're scoping an audit, establishing automated CI/CD eval harnesses, or looking to upskill your engineering team, we'd be glad to discuss your requirements.