Eval harnesses
Golden sets and regression suites for RAG, agents, and fine-tuned models.
Evals · Red-teaming · Release gates · Monitoring
Ship AI you can trust
Make AI systems trustworthy — evaluation harnesses, red-teaming, prompt-injection defenses, policy filters, and continuous monitoring for production models and agents. Catch regressions before users do.
CIRelease gates
RedTeam ready
LiveMonitoring
AuditTrail
Quality & safety
Production AI fails quietly without measurement
We install the testing, safety, and observability layers that keep agents and models accurate, policy-compliant, and auditable as they evolve — from first deploy through every prompt and model change.
Red-teaming
Adversarial testing for jailbreaks, prompt injection, and data leakage.
Runtime guardrails
Input/output filters, tool allowlists, and approval gates for high-risk actions.
Continuous monitoring
Drift, cost, latency, and quality alerts in production with incident response.
Maturity model
Three levels of AI quality
We scope clearly so you know when to graduate from baseline checks to production gates and enterprise red-teaming.
Baseline
Establish golden sets and manual spot-checks before your first production deploy.
- Golden query/answer sets
- Faithfulness & relevance baselines
- Manual review workflows
Production
Automated regression suites and CI release gates on every prompt/model change.
- RAGAS / DeepEval regression
- CI release gates
- Runtime guardrails & PII filters
Enterprise
Continuous red-teaming, production sampling, and full observability with audit trails.
- Scheduled adversarial testing
- Production quality sampling
- Incident response & dashboards
What we test
Threats we red-team
Adversarial scenarios mapped to your system architecture — not generic checklists.
Prompt injection
Direct and indirect injection via documents, tool outputs, and multi-turn context poisoning.
Data exfiltration
Attempts to extract PII, credentials, or proprietary data through crafted queries.
Tool abuse
Unauthorized API calls, privilege escalation, and out-of-scope tool invocations by agents.
Hallucination drift
Silent accuracy regressions after model swaps, prompt changes, or corpus updates.
Decision guide
Ship & pray vs eval-gated
Most production AI incidents are silent regressions — eval gates catch them before users do.
Silent failures
- Manual spot-checks before launch, then nothing
- Regressions discovered by users in production
- No audit trail for compliance reviews
- Prompt changes ship without validation
Measured releases
- Golden sets run on every model/prompt change
- CI blocks deploys that fail quality thresholds
- Red-team findings tracked and remediated
- Production sampling catches drift post-launch
What we deliver
Eval & guardrail capabilities
From offline regression through runtime safety and production observability — every layer of trustworthy AI.
Offline Eval Harnesses
Accuracy, faithfulness, toxicity, and task success metrics on golden sets — with RAGAS, DeepEval, and controlled LLM-as-judge scoring tuned for your domain.
- Golden query/answer regression suites
- Faithfulness & citation grounding checks
- Automated CI integration
Online Monitoring
Tracing, feedback loops, production sampling, and incident response workflows for live systems.
Security Testing
Prompt injection, tool abuse, jailbreak, and exfiltration scenarios with prioritized remediation.
Policy Engines
PII detection, topic blocks, and brand/policy constraints at input and output.
Human Review
Queues and scoring UX for high-risk outputs requiring human approval.
Release Gates
CI checks that block bad model/prompt versions from reaching production — no silent regressions.
Our process
Audit to production gates
Structured delivery from system audit through eval harness build, red-teaming, and continuous monitoring.
Architecture audit
Map data flows, tool access, failure modes, and compliance requirements for your AI system.
Week 1–2Golden sets & baselines
Build representative eval sets and establish faithfulness, relevance, and task success baselines.
Week 2–4Red-team & guardrails
Adversarial testing, runtime filters, tool allowlists, and prioritized fix recommendations.
Week 3–6Gates & monitoring
CI release gates, production dashboards, drift alerts, and incident response runbooks.
Week 6–8
Technology
Eval stack
Evaluation frameworks, safety services, and observability — integrated with your existing AI infrastructure.
Layer 01
Evals
Layer 02
Safety
Layer 03
Observability
Proof of work
Quality-driven case studies
Production systems with rigorous eval frameworks and measurable accuracy outcomes.
RAG Compliance Review System
Hybrid RAG with citation-grounded answers, faithfulness tracking, and 91% gap detection accuracy on 2,800+ regulatory documents.
91%Gap detection
$900KAnnual savings
LLM Contract Analysis Platform
Fine-tuned LLM + RAG with eval harnesses driving clause extraction from 88% to 96% accuracy with release gates on every model update.
88→96%Accuracy
87.5%Time saved
CIRelease gates
RedTeam ready
15+Years experience
200+Projects shipped
01Can you evaluate an existing AI system we already built?
Yes. We audit architecture, build eval sets, red-team the system, and recommend prioritized fixes. Many engagements start with an audit of a system already in production — we identify silent regressions, security gaps, and missing observability without requiring a rebuild.
02Do guardrails hurt UX?
Good guardrails are scoped — they block high-risk behavior while keeping legitimate workflows fast. We design guardrails around your actual failure modes, not blanket refusals. Most users never notice well-scoped guardrails; they notice when the system hallucinates or leaks data.
03How often should evals run?
On every prompt/model/data change, plus scheduled production sampling. Release gates prevent silent regressions — if faithfulness drops below threshold, the deploy is blocked automatically. Production sampling catches drift that offline evals miss.
Next step
Ready to make AI trustworthy?
Tell us about your system — we'll audit your eval gaps and recommend a quality & safety roadmap within 48 hours.