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. Delivery aligned to United States teams (USD).
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
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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Regional
Delivery considerations for your region
Compliance & Data (US)
For US teams, we build with auditability in mind: clear access boundaries, least-privilege roles, and reviewable operational controls.
We can align delivery with SOC 2 / ISO-friendly practices (without claiming certification): evidence-ready logs, secure-by-default config, and clear ownership.
- SOC 2 / ISO-friendly implementation patterns (no certification claims)
- Least-privilege access and permission boundaries
- Security review checklists for auth, payments, and data flows
- PII-safe logging + incident response playbooks (on request)
- Retention and deletion flows where required
- NDA + vendor onboarding docs on request
Timezone & Collaboration (Americas)
We support teams across the Americas with meeting windows that work for EST/CST/MST/PST.
We keep delivery predictable with weekly milestones, concise async updates, and written decisions to reduce calendar load.
- Americas overlap with EST/PST-friendly windows
- Async-first updates with written decisions
- Weekly milestone demos + change control
- Fast turnaround on blockers and clarifications
- Clear owner per workstream and escalation path
Engagement & Procurement (US)
US-friendly engagement structure: clear SOWs, milestone billing, and invoice cadence that fits typical procurement workflows.
If you need vendor onboarding artefacts, we can provide security posture summaries and delivery process documentation.
- USD invoicing and milestone-based payment schedules
- SOW + scope lock options for fixed-scope work
- Time-and-materials for evolving requirements
- Procurement-ready documentation on request
- Optional paid discovery to de-risk delivery
Security & Quality (US)
We ship with a security-first checklist and performance budgets—so releases stay stable under real traffic.
Expect clean PRs, reviewable changes, and production-ready testing from day one.
- Threat-aware checks for auth, roles, and sensitive data flows
- CI-friendly testing: unit + integration + critical path smoke tests
- Performance budgets (Core Web Vitals-minded) and bundle checks
- Structured logging + error tracking hooks (Sentry-ready)
- Rollback-safe releases and clear release notes
Want help with AI evaluation and testing?
Share your requirements for United States delivery. USD-based engagements.
Reply within 2 hours. No-pressure consultation.