Test of AI: Making Sure Smart Systems Stay Smart
We make your AI prove itself in production, not just look smart in the lab.
Making Sure Your AI Thinks and Behaves the Way the Business Demands
Building trust in how AI thinks and responds
AI assurance validates how our models think, respond, and adapt under real-world conditions. Accuracy, resilience, edge-case handling, and decision consistency are pushed hard so the system behaves reliably when the pressure hits. Every layer of intelligence is tested to confirm the model stays stable, predictable, and aligned with what the business actually expects.
The approach blends structured evaluation, domain-driven checks, adversarial validation, and tight governance to deliver enterprise-ready outcomes. Risky behaviors are flagged early, compliance gaps are exposed, and outputs are verified against real business intent. The result is AI we can trust to perform correctly, safely, and consistently in production.
Owning the Entire AI Lifecycle, End to End
We govern every stage from data → prompts → agents → deployment → monitoring.
Agentic AI Validation
Testing how AI agents' reason, act, and coordinate under real conditions.
Application Assurance
Making sure AI-powered products behave predictably and don’t break the user experience.
LLM Evaluation
Measuring consistency, reliability, safety, and drift instead of chasing vanity benchmarks.
AI’s Blind Spots: Why Testing Is Critical
Hallucinations and Hidden Failure Modes
AI models fail confidently, generating false information, misinterpreting context, reacting unpredictably. Adversarial prompts, red teaming, and scenario-based evaluations expose these blind spots before production.
Performance Must Hit Enterprise SLAs
Latency targets, grounding accuracy, multi-lingual consistency, and high-load stability don't happen by default. Real-world performance validation against strict SLA commitments is non-negotiable.
Compliance Keeps Tightening
EU AI Act, NIST AI RMF, ISO 42001, regulatory requirements are evolving fast. Demonstrating adherence through audits and fairness certifications is now mandatory for enterprise deployment.
Bias Has Direct Business Impact
Unfair AI triggers legal exposure and kills user trust. Fairness audits and demographic stress-tests catch bias before it becomes a liability.
Inconsistency Kills Adoption
Users abandon AI that contradicts itself. Consistency testing, tracking logical alignment across multi-turn conversations, prevents instability from eroding confidence.
Indium’s Approach to AI Testing
- Agentic AI Validation
- Testing of AI-Infused Applications
- LLM Model Evaluation
Autonomous agents are validated on:
- Multi-step planning & reasoning accuracy
- API/tool-use correctness
- Memory retention & context carryover
- Workflow stability without loops or unsafe actions
With adversarial and business-critical scenarios, we test:
- Recovery strategies
- Goal-completion rates
- Decision-making under constraints
- Synthetic edge-case handling
Agents that behave predictably and responsibly — even in complex business workflows.
We test AI features such as chatbots, RAG search, summarization, recommendations, and NLP/CV workflows for:
- Correctness across varied prompts
- Human-like UX and coherent dialogues
- Consistency across languages & contexts
- Performance under volume, load & concurrency
Post-deployment, we ensure ongoing reliability through:
- Drift detection (data, model & behavior)
- Latency & grounding regressions
- Hallucination spikes
- SLA deviation alerts
- Continuous red teaming
- Risk & compliance notifications
AI that stays reliable, relevant, and safe, every day in production.
LLMs are evaluated using curated prompts, golden datasets, and hallucination-scoring frameworks to ensure:
- High precision & factual consistency
- Strong retrieval grounding in RAG setups
- Stable reasoning across multi-turn conversations
- Efficient token usage & runtime performance
Our responsible AI layer includes:
- AI Red Teaming for jailbreaks & unsafe actions
- Toxicity & content-safety tests
- Bias evaluations across demographic attributes
- Privacy & leakage checks
An LLM that is safe, aligned, and enterprise-ready.
Where Indium Breaks Away From the Pack
AI-QE Accelerators that Speed Up Validation
Our IP-backed frameworks, LLM Evaluator, Prompt Variance Engine, Drift Monitoring, cut validation time by 40–60% while improving coverage.
Deep Expertise Across the AI Stack
From GPT to Claude, Llama, Mistral and enterprise RAG/Agent ecosystems (LangChain, LlamaIndex, AutoGen, CrewAI), we bring real engineering depth.
Enterprise-Grade Quality Governance
We embed ISO-aligned AI governance with traceability from requirements → risks → prompts → evaluations → monitoring.
Domain-Aligned AI Testing
Pre-built validation assets across BFSI, Healthcare, Retail, Manufacturing, EdTech, Media, and Travel allow rapid, context-aware testing.
Specialized AI Quality Engineering Teams
AI test architects, Responsible AI specialists, prompt engineers, and MLOps experts collaborate to assure technical, behavioral, and business quality.