✦QK AI Labs
The Applied AI Division of QualityKiosk
QK AI Labs
A portfolio of autonomous agents that sense every signal, reason against your Enterprise Brain, and act before your customers feel it.
5
Agent families, one operating model
Sense
Reason · Act · Learn, continuously
💬
Agents for CX
L1 to L4 support, autonomous resolution
→
⚙
Product & Engineering
Requirement intel, PR risk, release readiness
→
✅
Agents for QE
PRD to RCA, self healing tests
→
📡
Agents for SRE
Alert intelligence, autonomous runbooks
→
📈
Sales & BI
Pipeline risk, forecast, next best action
→
Runs on your choice of platform · your data stays in your boundary
DevRev
Microsoft Copilot
OpenAI Enterprise
Gemini Enterprise
Amazon Bedrock
On-prem · Apple
On-prem · NVIDIA✦Enterprise Brain
The Enterprise Brain
Intelligence that compounds inside your boundary.
Twenty six years of engineering credibility, now shipped as production grade agents. Click a node to trace which agents it powers.
Patented
Enterprise Brain architecture & token optimization
0
Token savings vs. leading frontier models, live
25+
Countries of operation, 150+ enterprise customers
Billions
Of tokens saved every day in production
Delivery hubs
IndiaUSDubaiSingapore
✦
US Patent Granted · AI Foundational Technology
US-12361029-B2 · Scalable Vector Database
The engine under the neural net
✦Who We Work With
Who we work with
From India's largest banks to global digital natives.
Banks
NBFCs & Lending
Digital Natives & Fintech
Beyond BFSI
80%+
Of India's banks trust QualityKiosk
150+
Enterprise customers across 25+ countries
30+
Production agent use cases, demonstrable live
✦Development Lifecycle
✦ Development Lifecycle
AI agents across the SDLC
Dev
Build side
Ops
Run side
Plan
Code
Build
Test
Release
Deploy
Operate
Monitor
✦ DevSecOps
Zero-touch releases with autonomous quality gates & posture management
GitLab
Azure DevOps
Guardian
✦ Agentic AI · Coding
Self-healing agents generating code & tests across every build
Bootlabs
Nimbus AI
Copilot
Nimbus AI
Copilot
✦ AI Observability
Trust reporting across 60 dimensions & LLM fine-tuning
Nimbus AI
Trust Report
Cloud Engineering ✦
IaC · FinOps · SecOps · autonomous migration
AWS
Azure
GCP
Terraform
Ansible
Observe ✦
Synthetic users, real sessions, full-stack signals
Elastic
Grafana
Dynatrace
Datadog
AnaBot
Customer Experience ✦
Self-service agents with RAG-driven resolution
DevRev
AnaBot
✦ Software Testing
Functional (API & UI) · Digital · Automated · Performance · Chaos · Data Integrity
Watermelon
Katalon
Tricentis
UiPath
BrowserStack
LambdaTest
✦ 125+ tools orchestrated · software development lifecycle
GitLabAzure DevOps
GitHubCopilot
JiraNimbus AIGuardianTricentisKatalonUiPath
Selenium
PlaywrightBrowserStackLambdaTestWatermelonAWSAzureGCPTerraformAnsibleDynatraceDatadogGrafanaElasticDevRev✦Revenue Lifecycle
✦ Revenue Lifecycle
AI agents across the Revenue Loop
Sell
Win side
Realise
Cash side
Reach
Qualify
Propose
Close
Book
Invoice
Collect
Reconcile
✦ Marketing · Martech
Intent signals & hyper-personalised campaigns at scale
HubSpot
Marketo
SF Marketing
✦ Outreach & Selling
Automated multi-channel sequences & class selling copilots
Salesforce
ServiceNow
Outreach
✦ Proposal & Closure
AI-generated, priced & pre-approved deal desks
DealHub
PandaDoc
Booking & FinOps ✦
Straight-through booking, invoicing, tax & compliance agents
NetSuite
SAP
Anaplan
Collections ✦
Autonomous AR follow-up, dunning & cash application
HighRadius
Chargebee
Revenue & Growth ✦
Revenue realization, churn prediction & upsell triggers
RevPro
Gainsight
Totango
✦ Enterprise Brain · Revenue Intelligence
Forecasting · pipeline health · reconciliation — every deal compounds into revenue memory you own
DevRev
QK Enterprise Brain
✦ 125+ tools orchestrated · revenue, contract & procurement intelligence
SalesforceServiceNowHubSpotMarketoOutreachDealHubPandaDocNetSuiteSAPAnaplanHighRadiusChargebeeRevProGainsightTotango
SalesloftDevRev✦The Portfolio
The portfolio
Five families. One operating model.
Narrow agents, each with one craft, easier to evaluate, debug and govern than a monolith. Click a family to open its live experience.
01
Agents for CX
- Deflect & Answer grounded replies via retrieval
- Resolve & Act refunds, account actions under policy
- Investigate correlate history, draft escalations
- Specialist L4 one narrative across handoffs
Open the detail →
02
Product & Engineering
- Requirement Intelligence fuzzy PRDs to testable specs
- PR Risk & Review blast radius, regression risk
- Release Readiness go / no-go narrative
- Roadmap Signal backlog ranked by real usage
Open the detail →
03
Agents for QE
- Test Strategy PRD to scoped plan
- Generation & Coverage edge cases vs risk
- Failure & RCA cited, in minutes
- Quality Gate blocks weak cases
Open the detail →
04
Agents for SRE
- Alert Intelligence noise to real incident
- Correlation & RCA graph-native root cause
- Autonomous SOP runbook, human-approved
- Postmortem auto timeline + learnings
Open the detail →
05
Sales & BI
- Pipeline Risk slipping deals, early
- Forecast Integrity committed vs signal
- Account 360 one live account story
- Next Best Action grounded in the graph
Open the detail →
✦Family 01 · Agents for CX
Family 01 · Agents for CX
Support that resolves, every tier.
L1 to L4, autonomous resolution across chat, voice, email and tickets.
- Deflects and resolves L1 to L4: grounded answers, account actions, refunds and escalations under policy.
- Omnichannel: chat, voice, email and in-app, one narrative across every handoff.
- Compounds: every resolution written back so the next case is faster.
-45%
Avg handle time
-38%
Mean time to resolve
+22 pts
CSAT
Reads and acts across 10+ tools
DevRevServiceNowSalesforce ServiceIn-appHubSpotOutreachElasticDatadogGrafanaNimbus AIWhy are refunds up this week, and what should we do?
Querying your systems directly✓
Composing the answer with charts, tables and actions✓
Refund complaints by cause (7d)
Breakdown
| Cause | Share | Trend |
|---|---|---|
| Auth policy | 45% | up |
| Data sync | 28% | flat |
| Duplicate | 16% | up |
Next steps
- Retry queue enabled for auth-policy failures
- Refund SLA set to 24h, customers notified
Raised / linked
TKT-2261TKT-2274INC-88
Same engine, working in the background
CX + Real-User Monitoringauto
App error rate (live)
→
Agent auto-triggers
Correlating the spike with Zendesk tickets✓
Matching to a Dynatrace front-end release✓
Bad build detected. 214 tickets pre-empted, status banner pushed, customers notified.
Notified214 tickets pre-empted
Customer-success reliabilityauto
Account health signals
→
Agent auto-triggers
Scoring health from ServiceNow + product usage✓
Flagging SLA breaches and sentiment drops✓
7 accounts at churn risk, playbooks assigned, CSMs notified.
Playbooks assigned7 accounts, CSMs notified
Autonomous L1-L4 deflectionauto
Signals in
WhatsApp: "Can't add money, card keeps failing."
→
Auto-deflect
Classifying intent, checking KB freshness✓
Routing by skill and current workload✓
Ticket TKT-2290 raised, classified and auto-assigned to L2, SLA set.
Auto-assigned toPPriyaL2 Support
✦Family 02 · Product & Engineering
Family 02 · Product & Engineering
Ship faster, with intent intact.
Requirement intelligence, PR risk and release readiness across the SDLC.
- Turns fuzzy PRDs into structured, testable, traceable requirements.
- Scores every PR for blast radius, missing tests and regression risk before merge.
- Go / no-go release readiness from coverage, incidents and change risk.
-35%
Release cycle time
-40%
Escaped defects
+30%
PR review speed
Reads and acts across 10+ tools
JiraGitHubGitLabAzure DevOpsCopilotNimbus AIGuardianDatadogDevRevSeleniumWhich features are we actually maintaining vs using?
Querying your systems directly✓
Composing the answer with charts, tables and actions✓
Usage vs maintenance effort
Breakdown
| Feature | Usage | Effort |
|---|---|---|
| Payments | 81% | med |
| Rewards | 9% | high |
| Legacy import | 1% | high |
Next steps
- Backlog re-ranked by usage-per-effort
- 2 features flagged to sunset next release
Raised / linked
ISS-4102ISS-4118PR-771
Same engine, working in the background
Stitching & Observabilityauto
Error budget burn (release)
→
Agent auto-triggers
Stitching PR risk with Datadog + New Relic✓
Checking blast radius across services✓
Go, with a guard on the payments path. Rollback prepared, owner notified.
Guarded + rollback readypayments path
Engineering reliabilityauto
Errors by service (24h)
→
Agent auto-triggers
Correlating deploys with error budgets in Grafana✓
Tracing the regression to one commit✓
One commit, one service. Fix PR-782 drafted for review.
Trace IDcommit a1f9 -> PR-782
Release-readiness deflectionauto
Signals in
Build pipeline: "Release candidate rc-48 ready for sign-off."
→
Auto-deflect
Assembling coverage, open defects, change risk✓
Writing the go / no-go packet✓
No-go items surfaced, packet auto-routed to release owner. Review time cut.
Auto-assigned toRRahulRelease owner
✦Family 03 · Agents for QE
Family 03 · Agents for QE
Quality, at the speed of signal.
From PRD to RCA with evaluation gates and self healing tests.
- PRD to RCA: strategy, generation, scripting and root-cause in one loop.
- Risk-weighted coverage: edge cases against risk, not just code paths.
- Quality gate: weak cases blocked before they reach scripting.
-40%
Defect escape
3x
Release velocity
-40%
QE cost
Reads and acts across 10+ tools
TricentisKatalonUiPathSeleniumPlaywrightBrowserStackLambdaTestWatermelonJiraNimbus AIWhat is our real coverage on the payments journey?
Querying your systems directly✓
Composing the answer with charts, tables and actions✓
Coverage by module
Breakdown
| Journey | Coverage | Gap |
|---|---|---|
| Payments | 71% | 9 cases |
| Onboarding | 88% | 2 cases |
| Profile | 64% | 6 cases |
Next steps
- 9 cases generated to close the payments gap
- Flaky suite quarantined before the run
Raised / linked
XRAY-556TKT-903DEF-40
Same engine, working in the background
Change-request to testauto
Impact of the new CR
→
Agent auto-triggers
Parsing the CR, building the impact matrix✓
Generating and running cases live✓
9 / 9 acceptance criteria run in-browser, evidence captured, report attached.
Evidence captured9/9 acceptance criteria
Test and quality reliabilityauto
Failure type (last runs)
→
Agent auto-triggers
Detecting flaky tests across recent runs✓
Separating real failures from drift✓
14 flaky tests quarantined, real defects surfaced to engineers.
Quarantined14 flaky tests isolated
Auto-triage deflectionauto
Signals in
Overnight run: "38 failures in the regression batch."
→
Auto-deflect
Classifying: defect, drift, or environment✓
Routing only true defects to owners✓
Manual triage removed. 6 real defects auto-assigned, drift auto-repaired.
Auto-assigned toAAnitaQE engineer
✦Family 04 · Agents for SRE
Family 04 · Agents for SRE
Signal, not noise.
Alert intelligence, graph-native RCA and autonomous runbooks.
- Noise to signal: dedupes thousands of raw alerts to the few that are real.
- Graph-native RCA across telemetry and human channels in minutes.
- Autonomous SOP: runbook failover and retries, human-approved where it matters.
-82%
Mean time to resolve
100%
Customers auto-notified
Reads and acts across 10+ tools
DynatraceDatadogGrafanaElasticServiceNowDevRevNimbus AIAWSAzureGuardianWhat happened during last night's incident?
Querying your systems directly✓
Composing the answer with charts, tables and actions✓
Alert volume vs real incidents
Breakdown
| Layer | Count | Note |
|---|---|---|
| Raw alerts | 2,400 | ~90% noise |
| Correlated | 18 | grouped |
| Incident | 1 | payments |
Next steps
- Runbook proposed and human-approved
- On-call paged via PagerDuty, customers notified
Raised / linked
INC-4471CR-231TKT-990
Same engine, working in the background
Stitching & Observabilityauto
Latency spike (payments)
→
Agent auto-triggers
Stitching the ticket with APM + gateway logs✓
Naming what evidence can and cannot prove✓
Cause pinned to auth service. Missing trace ID flagged as the gap to fix.
RCAauth service, missing trace ID flagged
Payment reliabilityauto
Rail health: card / UPI / netbanking
→
Agent auto-triggers
Running synthetic journeys across all rails✓
Opening a change request, drafting the fix PR✓
Degradation caught in under 4 min. Fix PR-88 raised for approval.
Fix PR raisedPR-88, awaiting approval
Incident auto-deflectionauto
Signals in
Monitor: "HTTP 200 but business op failed x50."
→
Auto-deflect
Classifying severity from evidence + history✓
Auto-resolving the known pattern✓
Known pattern auto-handled. Only a true Sev-1 escalates to a human.
Auto-assigned toKKarthikSRE on-call
✦Family 05 · Sales & BI
Family 05 · Sales & BI
Every signal, in one story.
Pipeline risk, forecast integrity and next best action across the revenue stack.
- Pipeline risk early: slipping deals flagged before the forecast call.
- Forecast integrity: rep commit reconciled against behavioural signal.
- Account 360 and next best action, grounded in the graph.
-60%
QBR prep time
+18%
Forecast accuracy
+25%
Win rate
Reads and acts across 10+ tools
SalesforceServiceNow CLMHubSpotDealHubPandaDocGainsightTotangoHighRadiusAnaplanOutreachGive me the pipeline slipping this quarter, by region.
Querying your systems directly✓
Composing the answer with charts, tables and actions✓
Slip by region ($M)
Breakdown
| Region | At risk | Owner |
|---|---|---|
| West | $7.1M | 3 deals |
| East | $3.8M | 2 deals |
| North | $1.9M | 1 deal |
Next steps
- At-risk deals listed, owners notified
- Forecast re-scored on behavioural signal
Raised / linked
OPP-2201OPP-2214CASE-77
Same engine, working in the background
Enterprise Intelligence stitchauto
Usage vs invoiced ($)
→
Agent auto-triggers
Stitching CRM, ERP and product usage✓
Explaining the divergence with sources✓
Usage up, invoicing lagged. Billing gap surfaced to finance.
Billing gapsurfaced to finance
Forecast and pipeline reliabilityauto
Commit vs signal
→
Agent auto-triggers
Comparing committed vs signal across pipeline✓
Flagging deals with weak evidence✓
Forecast integrity scored. 5 optimistic deals flagged for review.
Flagged5 optimistic deals
Deal-desk deflectionauto
Signals in
Email: "Need the QBR deck and account 360 by 4pm."
→
Auto-deflect
Assembling account 360 from CRM + support✓
Drafting the QBR and next best actions✓
QBR auto-drafted from the graph. Prep time cut, routed to the AE.
Auto-assigned toMMeeraAccount executive
✦Our Recommendation
Our recommendation
Two ways we can do this. We recommend Buy & Configure.
Both are QK, both are done with your team. The question is only where we start from.
Option A
Build with you
We co-engineer the agents from the ground up. Full control, but it starts at requirement capture and takes months to mature.
Option B · we recommend
Buy & configure with you
Start from a proven agentic engine, then configure and tune it with your team. A working system in days, then made yours.
| What matters to you | Build with you | Buy & configure · our pick |
|---|---|---|
| Time to first working system | Months, starts at requirement capture | Days. Working system next week |
| Orchestrated agents & memory | Built ground-up, heavy R&D | Ready engine, configured to you |
| Auditability & traceability | You design and certify it | Built in, bank-grade from day one |
| Future-proof & self-improving | You own the upgrade loop | Model-agnostic, learns every run |
| Customised to you | Fully bespoke | Configured and tuned with your team |
| Risk & cost profile | High upfront, uncertain timeline | Low risk, prove value before scale |
| Who runs it | Your team, once we hand over | We run and improve it with you |
✦Productionizing Options
Productionizing options
Your infrastructure, your choice.
Same agents, same outcomes. You decide where they run, matched to your security and infra comfort.
Recommended
Agents on QK's platform
Hosted · consumed as a service
- Outcome-based pricing, pay for value not tokens
- Fastest to value, nothing to set up
- We run and maintain the engine
SaaS · no prerequisites
Agents on your infrastructure
Your cloud · shared infra
- Runs on infrastructure we share with you
- Inside your controls, network & governance
- Your environment, our agents
Prereq: cloud account + network access + IAM roles
On-prem, if available
Your on-prem LLM · nothing leaves
- Integrates with your on-prem model
- We build the harness, skills & agents on it
- Fully air-gapped, bank-grade control
Prereq: on-prem LLM, GPU capacity, data plane
Vertical agents, QK-provided infra
Dedicated appliance from us
- Dedicated edge AI appliances
- Purpose-built vertical agents
- Full stack, delivered end to end
Prereq: rack space + connectivity, we supply the rest
✦The Journey
The agent engineering journey
Autonomy is earned, not assumed.
Every agent starts under full human review and graduates only as evaluation data, graph depth and memory accumulate. Level 1 to Level 4, distinct from the CX support tiers.
Level 1
Human in loop
Agent recommends, human decides. Starting state for every new domain.
DAY TO DAY DEFAULT
Level 2
Supervised
Outputs sampled and spot checked. Day-to-day default after a few sprints.
EARN NEXT
Level 3
Co-pilot
Acts within scope, humans handle exceptions and low-confidence flags.
Level 4
Autonomous
Acts within a confidence band, alerts only on anomaly. Strongest evals required.
Pick the trail, stand up the brain, agents live at Level 1, then earn autonomy with evals and memory until production runs at Level 2 and Level 3, with Level 4 where the evidence is strongest.
✦Commercials
How the commercials work
Built to adapt as fast as AI moves. Commercially too.
Models, tools and skills change monthly. Your solution absorbs them, and the commercial model stays simple, transparent and tied to value.
01
Platform Subscription
The engine, one predictable line
- Agentic engine, orchestration, memory & governance
- Annual subscription, easy to budget
- Scales with autonomy, no surprises
02
Build & Enablement
Your agents, at actuals
- Configuring and co-building your agents
- Enabling your experts as model engineers
- Billed transparently at actuals, phase by phase
✦ Superior
03
Outcome-based Pricing
Pay for value, not tokens
- Priced on success criteria and outcomes
- On token-efficient runtimes, every rupee maps to a result
- 80 to 85% more efficient than generic token machines
✦Next Step
Agent engineering · QK AI Labs
See our agents on your own workflows.
Pick one family, CX, engineering, QE, SRE or sales. In a working session we run the loop live on your data, in your tools, and map a production path across all five families, in weeks not quarters.
Book a working session →
Shakthi Guru · General Manager, Sales · QK AI Labs
shakthi.guru@qualitykiosk.com · +91 99427 68848
shakthi.guru@qualitykiosk.com · +91 99427 68848
QualityKiosk Technologies · QK AI LabsMaking Software Reliable ✦