AI customers can trust
Reliability of AI
Evidence that your models and agents behave as intended, before release and after every change.
What is reliability of AI?
Reliability of AI is the engineering work that keeps a model or agent performing as intended after it leaves the demo, once real data and real users start testing it in ways nobody scripted. QKTech organizes this work around GATES, a set of checks built into every release and applied again every time the system changes.
Recent research shows how often internal testing misses what customers find. In a June 2026 VentureBeat Pulse survey of 157 enterprises, half had deployed an agent or LLM feature that passed internal evaluations and then caused a customer-facing failure.
Latest From QKTech
From our AI reliability team and partners
Measured on AI systems
Proof from models and data we have assured
Some proof points from our engagements, covering GenAI chatbots and data programs that fed AI and analytics in regulated industries.
89.2%
Answer correctness for a leading Indian insurer’s GenAI chatbot, evaluated on Nimbus
80%
Response accuracy for an automotive OEM’s fleet-owner GenAI chatbot, verified against portal records
100%
Data coverage across 800+ ETL entities in a UAE bank’s migration
100%
Data accuracy in a GenAI-driven eBaoTech migration for a global life insurer
Checks every model and agent passes
GATES stands for Governance, Assurance, Trust, Evals and Security, the checks a model or agent passes before release and again whenever it changes.
Governance
Named ownership for every model and agent, the policy that applies to it, and a clear route for escalation when the system cannot make the call itself.
Assurance
Proof at every release that the system follows the policy written for it, recorded in documentation your governance and audit teams can review.
Trust
Explainability and bias monitoring that give business users confidence to act on what the system produces, which decides how much work it can take on.
Evals
The tests a model or agent passes before release and the monitoring that keeps grading it in production, with acceptance thresholds agreed with the business.
Security
Guardrails against prompt injection and data leakage through the inputs a system is built to accept, tested before launch and again after each change.
AI Sustenance & Optimization
Drift monitoring, re-evaluation and cost control for systems already live, including model observability that traces a failure back to its cause.
Which platforms power our reliability of AI services?
GATES checks run on techniqk, our platform suite, and connect to the observability tools a client already uses. Evaluation results, drift alerts and audit evidence land in one place for engineering and risk teams alike.
Nimbus AI
Generates test questions from your own knowledge base and scores GenAI assistants across 60+ dimensions, from accuracy to ethics and safety.
qRace
Generates and maintains the regression suites that re-test a model’s surrounding application every time a prompt or model changes.
Partners
Technology Partners
We connect GATES to the AI observability and monitoring platforms our clients already run.
AI observability and evaluation platform behind our joint AI Reliability for Enterprises program with QK AI Labs, used to monitor model and agent behavior in production.
Monitoring and security platform for cloud applications, used to route AI alerts into the ops tooling teams already watch.
Client outcomes
AI held to production standards
Our engagements where AI and the data behind it were tested before customers relied on them.
FAQs
Common questions about reliability of AI
Is GATES a certification?
GATES is QKTech’s internal engineering standard. It produces documentation your governance and audit teams can review.
Can you assess an AI system we built without you?
Yes. We score an existing model or agent against GATES and return a gap report with a remediation plan.
How is this different from AI for Reliability?
AI for Reliability uses AI to make your existing operations more reliable. Reliability of AI makes the AI and agents themselves reliable, and most enterprises need both.
How often should a live model be re-evaluated?
Every time the model, prompt, retrieval data or an upstream API change, and on a fixed schedule in between to detect drift. Nimbus runs those evaluations continuously and flags regressions as they appear.
What is model observability?
Model observability is continuous monitoring of a model’s accuracy and drift in production, with enough detail to trace a failure back to its cause.
Talk to us
Talk to a Reliability of AI expert
Tell us about the model or agent closest to production, or the one already live that nobody has tested since launch. Bring the model or agent you trust least today, and we will come back with a GATES readiness view and the first checks we would run.