Advancing AI Reliability through Research & Engineering

Applying 26 years of reliability discipline to the hardest problems in enterprise AI.

QK AI Labs evaluation dial: correctness, safety, groundedness, tone, latency and workflow scored around a GA, production-ready verdict

Ecosystem

We build on the platforms enterprises already trust.

We work across every major AI model, cloud environment and agent platform, and connect into the systems your teams already use.

Models

Models & foundation

  • Anthropic
  • OpenAI
  • Gemini
  • Amazon Bedrock
  • Nvidia
  • ElevenLabs
  • Kore.ai
  • Cognition (Devin)
  • Cursor
  • DevRev

Integrations

100+

Connectors

Environments

Cloud & deploy

  • AWS
  • Google Cloud
  • Azure
  • Oracle
  • Kubernetes
  • Private VPC
  • On-premise

Connectors

Connectors

Observability & on-call

  • Datadog
  • Dynatrace
  • New Relic
  • Splunk
  • Grafana
  • Prometheus
  • AppDynamics
  • Elastic
  • PagerDuty
  • Opsgenie

Connectors

ITSM

  • ServiceNow
  • Jira
  • Freshservice

Connectors

Data & channels

  • Salesforce
  • Postgres
  • Kafka
  • Slack

Trust in production

The hard part of enterprise AI isn’t starting,
it is earning trust in production.

57%

of organizations now run AI agents in production.

32%

say quality is the #1 barrier to deploying more.

Source: LangChain State of Agent Engineering, 2025

Anyone can prototype an AI system. Very few can put one in front of a regulated enterprise’s customers and stand behind every decision it makes. QK AI Labs exists to solve one problem: trust in production. It follows the path any real system travels: you Build it, you Prove it works, and you keep it Running once it’s live.

Build

Applied AI & Agent Engineering

We build AI systems and agents as evaluated, governed, reusable IP, not one-off scripts, and our engineers embed with enterprise teams to build them on any stack.

20+

AI-led projects

100+

Applied AI engineers

Prove

AI Evaluation & Assurance

An AI system has no native unit test, so we built the measurement system. We generate hundreds of realistic test cases from a plain-English description, score every response across a multi-dimension framework (correctness, safety, groundedness, tone, latency, workflow reliability), and turn each release into a pass/fail deployment verdict. Governance with identity, data controls, human-in-the-loop, audit, is applied at every step, not just as a final gate.

60+

scoring dimensions

200+

Test cases auto-generated per system

Run

AI Reliability & Operations (AI Ops)

We turn millions of production signals into real-time answers, correlating events, finding root cause, and driving incidents toward resolution before customers feel them, connected into the observability, on-call and ITSM stack the enterprise already runs.

~90%

Alert-noise reduction

30–58%

MTTR improvement

<15 min

Root cause

80%+

CSAT in production

100+

Enterprise connectors

How we measure

Vibe-checking doesn’t scale.
Systematic evaluation does.

AI systems fail differently from normal software. The same input can produce different outputs, the failure surface is unbounded, and behavior drifts silently when the model updates.

So we treat evaluation as a measurement problem, not a review. Describe a system in plain English, detect the domain, generate hundreds of realistic test cases (core, edge, adversarial, and probe), run them against the live system, score every response across the framework, and produce an Alpha to GA verdict with per-dimension guidance on what to fix. Then we re-run continuously to catch drift.

Describe

a system in plain English

Detect

the domain

Generate

core · edge · adversarial · probe

Run

against the live system

Score

every response

Verdict

Alpha → GA, per-dimension guidance

Re-run continuously to catch drift

We score for

Correctness

  • Task execution
  • Semantic precision
  • Answer quality
  • Consistency across turns and sessions

Safety

  • Compliance
  • Adversarial robustness
  • Misinformation control
  • Privacy
  • Escalation accuracy

Production behaviour

  • Groundedness & trust
  • Tone
  • Tool reliability
  • Latency within SLA
  • Workflow reliability
  • Resilience

Platform · Custom · Pre-built

From lab prototype to production system.

QK AI Labs develops the methods, frameworks and engineering practices that power QKTech’s AI offerings. As enterprise AI shifts from pilots to production systems, QK AI Labs offers these capabilities to enterprises through three paths: Techniqk, Agentiqk and Marqk. And this is how our decades of research become your most reliable running system.

Platform

Techniqk™

the platforms

26 years of expertise in quality engineering, reliability, performance and enterprise operations is infused into QKTech’s home-grown platforms. These AI-powered platforms help businesses build, run, secure, scale and assure reliable digital experiences end to end.

Custom

Agentiqk™

custom agents

Agentiqk is QKTech’s custom agent engineering platform that helps enterprises design, build, evaluate, and deploy AI agents tailored to their workflows, data, and governance requirements. It enables organizations to move from AI use cases to production-ready agents through a structured, enterprise-grade delivery lifecycle.

Pre-built

pre-built agents

Marqk is a marketplace of industry-specific and function-specific pre-built AI agents that enable organizations to accelerate AI adoption through ready-to-use solutions that can be evaluated, customized, and deployed with minimal effort.

The research behind reliable AI

Measured outcomes

The evidence, in production.

Every result here comes from real enterprise work.

~90%

alert-noise reduction in enterprise operational intelligence deployments.

60+

scored dimensions that improve customer experience, build trust, and maximize ROI.

80%+

customer satisfaction measured through AI-led customer experience monitoring.

24/7

single-pane visibility achieved through unified monitoring of multiple data sources.

40–50%

more defect detection achieved through AI-led digital assurance and reliability practices.

30–58%

MTTR improvement observed across production AIOps implementations.

Forward-Deployed Engineering

The lab, and the expertise behind it.

A Forward-Deployed Engineering core of architects, consultants and agent engineers, backed by solutioning, delivery and governance, and 26 years of QKTech reliability expertise.

26 years

Engineering digital reliability

40+

Certified practitioners

300+

Clients

25+

Countries

12K

Applications assured

Certified on

  • AWS
  • GCP
  • Azure
  • Oracle
  • DevRev

Talk to the AI Lab Expert

Book a consultation with our AI Expert and bring us a use case. We will help you with a measurable path to production.