AI Development
AI Evaluation & Testing
We build evaluation and testing systems for LLM products: golden datasets, automated scoring, human review loops, and regression gates. This turns “it feels worse” into measurable signals your team can ship against.
Overview
What this service is
AI evaluation is how you measure quality and prevent regressions across prompts, models, retrieval settings, and tool behaviors.
We define realistic test sets and scoring criteria, then automate evaluation so changes can be reviewed and shipped safely.
Delivery includes dashboards and workflows so teams can iterate quickly without losing trust in production behavior.
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Catch regressions before they hit users
Measure quality improvements with real metrics
Reduce subjective debates with shared eval criteria
Improve safety with red-team test coverage
Ship model/prompt changes confidently
Prioritize fixes using labeled failure modes
Features
What we deliver
Golden datasets
Representative test queries and expected behaviors based on real user intents and business rules.
Automated scoring
Scoring for relevance, correctness, format, citation quality, and policy compliance with repeatable runs.
Human review loops
Sampling-based review workflows to label failures and improve datasets over time.
Regression gates
CI-style checks that fail builds when quality drops below thresholds for key intents.
Safety and red-team tests
Prompt injection, policy-violating requests, and adversarial scenarios to validate guardrails.
Dashboards + reporting
Visibility into quality trends, failure clusters, and “what changed” between releases.
Process
How we work
Define criteria
Success metrics, intents, and failure taxonomy.
Build datasets
Golden queries + expected behaviors.
Automate scoring
Repeatable evaluation runs and reporting.
Add gates
CI checks and thresholds for release control.
Review loop
Human labeling workflow and iteration plan.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
RAG accuracy validation
Evaluate retrieval precision and citation quality on real queries and content changes.
Chatbot regression protection
Prevent prompt or model changes from degrading user-facing answers and escalation behavior.
Tool-action correctness testing
Validate that agents call the right tools with the right parameters under edge cases.
Safety validation
Test prompt-injection defenses and refusal behavior against adversarial user inputs.
Enterprise rollout reporting
Produce quality reports and release notes that stakeholders can trust.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
Done right, they speed it up. Teams ship changes faster when they can see impact and avoid regressions early.
Usually both. We score relevance, correctness, formatting, citation quality, and safety depending on the product.
Yes. We include adversarial test sets and safety checks aligned to your policy and guardrails.
For most real products, yes. Sampling-based human review helps validate edge cases and keeps datasets honest.
Yes. We design evaluation runs and budgets so you can run meaningful checks during CI without excessive cost.
Datasets, scoring scripts, dashboards, and runbooks for expanding coverage over time.
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