Private AI · Regulated industries
Enterprise AI where data never leaves.
A fine-tuned SQL model on the client's own servers, with field-level masking and every query logged.
Advanced AI — AI Trust, Evals & LLMOps
Ship AI you can trust and measure.
Evaluation, guardrails, monitoring and cost control for AI in production — so quality never drifts silently.
- Quality you can measure
- Risks caught before users
- Costs under control
The difference
What changes with Xendex.
Before
With Xendex
Before“Looks good to me” testing
With XendexAutomated evaluation on every release
BeforePrompt injection and data leaks
With XendexGuardrails and red-team testing
BeforeSilent quality drift
With XendexLive monitoring with alerts
BeforeSurprise AI bills
With XendexRouting and caching that cut cost
What's delivered
AI Trust, Evals & LLMOps, end to end.
LLM Evaluation
Test sets and metrics for accuracy, grounding and tone — run on every change.
- Golden sets
- LLM-as-judge
- Regression
Guardrails & Red-teaming
Defend against prompt injection, jailbreaks and data leakage.
- Injection
- PII
- Jailbreaks
Monitoring & Observability
Traces, quality and latency dashboards with alerts.
- Tracing
- Drift
- Alerts
Cost Optimisation
Model routing, caching and right-sizing.
- Routing
- Caching
- Right-sizing
AI Governance
Policies, documentation and risk registers to prepare for the DPDP Act and global frameworks.
- DPDP Act
- Risk register
- Model cards
How it's delivered
A clear path, with something to show at every step.
Each stage ends with a tangible output — so progress is visible, not promised.
Technology used
- Ragas
- DeepEval
- promptfoo
- Langfuse
- LangSmith
- OpenTelemetry
- NeMo Guardrails
- Guardrails AI
- Model cards
- Risk registers
- DPDP
Baseline
Measure today's quality, risk and cost.
- Baseline report
Instrument
Add tracing, evaluation and test sets.
- Eval suite
Protect
Deploy guardrails and run red-team tests.
- Red-team report
Monitor
Dashboards and alerts for quality, drift and cost.
- Live dashboards
Optimise
Route, cache and right-size — then re-measure.
- Savings report
Proof
Already in production.
FAQ
Questions, answered.
AI is already in production — can it be improved?
Yes. What exists is baselined before anything is changed.
How do you measure quality?
Task-specific test sets and automated judges, calibrated against human review.
Won't this slow releases down?
No — evaluations run automatically in your pipeline, so releases get safer and faster.
Can AI costs be reduced?
Usually, through routing, caching and right-sized models — measured before and after.
Is your AI quietly drifting?
Let's baseline it. You'll know exactly how it performs — and what it costs.