Question 1

How large is your team?

Accepted Answer

We operate with a lean and highly experienced core team spanning product engineering, testing, automation, localization, AI, and customer success. Depending on project requirements, we scale delivery through our extended network of specialists and partners.

Question 2

What expertise does your team bring?

Accepted Answer

The team brings an average of 8+ years of experience across software testing, localization, automation, accessibility, project management, and enterprise delivery. We have run complex global programs involving multilingual releases, quality engineering, and large-scale testing operations. The product is the result of those repeated delivery problems - not a generic test-management wrapper.

Question 3

How long have you been in this industry?

Accepted Answer

We have over 8 years of experience delivering software testing and localization services to global enterprises. The product platform is younger; it is built on years of practical delivery, customer constraints, and domain expertise from real testing and globalization programs.

Question 4

Why should we trust a new product?

Accepted Answer

That is a fair question. The product is new. The people behind it are not. The average testing experience on the team is 8+ years at enterprise scale. KaDeep AI exists because the same gaps appeared on every program: disconnected tools, late localization defects, weak release evidence, and automation that costs more to keep alive than it saves. Trust should come from a pilot on your own application, not from a slide. A typical first pass is one web application, one mobile build, or one locale pack with a readiness report at the end.

Question 5

What are your investment and growth plans?

Accepted Answer

KaDeep AI is founder-funded and bootstrapped. Immediate focus is product maturity, customer acquisition, and establishing operations in Singapore to support international growth. We are building sustainably while evaluating strategic investor participation as we scale.

Question 6

What live project experience do you have?

Accepted Answer

Programs Hands-on work across functional testing, non-functional testing, localization, and quality engineering. A localization and quality-engineering program on a large creative-software suite. Further detail is under NDA. A multi-module web and mobile platform: functional, performance, load, stress, and security validation as one readiness pass. A multilingual web and mobile surface pack, including Indic languages, with linguistic accuracy, layout fit, and journey-level experience checks. Confidentiality The team has also supported large-scale enterprise testing and localization programs for global technology organizations. Customer-specific implementation details stay confidential.

Question 7

Can you share the customer name?

Accepted Answer

No. Customers trust us because we keep confidentiality commitments. We can discuss the nature of the work, scale, and outcomes - not customer-sensitive information. Enterprise buyers usually treat this as a credibility signal, not a dodge.

Question 8

What happens if KaDeep AI is acquired or shuts down?

Accepted Answer

We are committed to long-term product development. For enterprise customers we can contract data ownership, exportability (tests, runs, defects, reports, translation memory, terminology), support, and transition assistance so business continuity does not depend on a single vendor remaining independent.

Question 9

What are your offerings?

Accepted Answer

KaDeep AI is both a platform and a delivery partner.

Platform

KaDeep AI is available as both a cloud-based and deployable platform, depending on customer requirements.

Cloud SaaS - hosted and managed by KaDeep AI for rapid deployment and lower operational overhead.
Private cloud / server deployment - for enterprises with security, compliance, or data-residency requirements.
KaDeep AI Studio (desktop edition) - designed for desktop, client-side, or controlled server environments where customers prefer local execution, restricted network access, or on-premise deployments.
Localization Studio - translation, linguistic QA, and multilingual release validation in the same shell.
GTW² - live-site localization and visual QA across locales.
Axco - accessibility assessment, remediation guidance, and compliance reporting.

KaDeep AI supports SaaS, private cloud, on-premise server deployment, and KaDeep AI Studio for desktop or client-controlled environments, depending on security and operational requirements.

Services

Services can run software-only, services-only, or hybrid. Functional testing, automation, performance, localization, accessibility, and quality-engineering expertise can run on the customer’s behalf while the platform provides visibility and governance.

Question 10

What is KaDeep AI versus KaDeep AI Studio versus Localization Studio?

Accepted Answer

KaDeep AI KaDeep AI is the company and the quality operating system: autonomous agents, test management, release intelligence, accessibility, localization, and managed services. KaDeep AI Studio KaDeep AI Studio is the installable execution and management environment. It runs on a Mac, PC, or lab machine, pairs to a project, and lets the agent drive a real local browser or native desktop apps while the customer keeps control of the machine, VPN, and data path. Localization Studio KaDeep AI Studio Localization is not a separate company product. It is the same KaDeep AI Studios workspace in a localization project: chat plus a workbench (Assets, Workbench, Review, Player, Glossary & TM, Deliveries, Dashboard) with live Browser / Mobile / Desktop tabs for in-context validation. One product, two project kinds - QA and localization - so a global release can be tested and localized without switching vendors or shells. Not only a repository It is not only a test-management repository. Test management is one layer. The differentiator is the autonomous QA agent that explores, authors, executes, files evidence-backed defects, and computes release readiness - with Localization Studio, GTW², and Axco in the same system.

Question 11

What is KaDeep AI Studio?

Accepted Answer

What Studio is KaDeep AI Studio is our desktop-based execution and management environment. It installs on individual workstations, test labs, or enterprise servers so teams can perform testing, automation, localization, accessibility, and quality-engineering work while keeping control of data and infrastructure. What it does In practice it does four things enterprises ask for: Local browser - real Chromium on the paired machine (localhost, VPN, internal staging). Native desktop testing - drive installed macOS / Windows / Linux applications through accessibility trees, with screenshots, recordings, and a stop hotkey. Restricted networks - the agent works where a cloud browser cannot reach. Human takeover - watch live, take control for SSO or a one-time challenge, hand back; the session can be reused. Cloud desktop VMs Web, iOS, Android, and desktop run in the cloud. A paired machine remains for private networks and localhost. Credentials stay in the vault. A person makes the ship call.

Question 12

Do you only sell software, or can you also execute testing?

Accepted Answer

Both. Customers choose software-only, services-only, or hybrid. We support functional, automation, performance, localization, accessibility, and broader quality-engineering work. Many customers engage us as a managed testing partner and keep KaDeep AI as the system of record for visibility and governance.

Question 13

Can we start with a pilot?

Accepted Answer

Yes. A typical first pass is a pilot: one web application, one mobile build, or one locale pack, with a readiness report at the end. That is how we validate workflows, integrations, and environments before a wider rollout.

Question 14

We already use Jira, TestRail, Slack, and Microsoft Teams. Why should we move?

Accepted Answer

Not a replacement KaDeep AI is not intended to replace the existing ecosystem overnight. It is an intelligence and orchestration layer across the QA lifecycle. One workspace Today those tools are usually disconnected: Jira for defects, TestRail for cases, Slack/Teams for chatter, Excel and PowerPoint for reporting. KaDeep AI brings planning, execution, AI-assisted authoring, defect intelligence, release readiness, analytics, localization, and accessibility into one workspace, and integrates with the tools you already have. We do not replace Jira and Teams; we make them smarter and remove the manual work around them.

Question 15

We already have a testing partner. There is no space for another vendor.

Accepted Answer

That is fine. Many conversations start as complementary work, not a replacement. We often improve visibility, automation, release readiness, analytics, localization, or accessibility without disrupting the incumbent partner.

Question 16

Why shouldn’t we build this internally?

Accepted Answer

Individual features are buildable. An integrated quality-engineering platform is not a sprint: product, agent runtime, browsers and devices, desktop drivers, localization filters, LQA, reporting, integrations, security, and ongoing model/tool maintenance. KaDeep AI lets teams get value without diverting product engineering into a second QA platform.

Question 17

What is the autonomous QA engineering agent?

Accepted Answer

What the agent is It is a chat-first senior QA engineer that operates your application the way a person would - and leaves durable artifacts a team can reuse. How it works You point it at a URL, a mobile build, or a paired desktop app. It explores, writes natural-language flows, runs them in a real browser, on a virtual iOS/Android device, or on a paired machine, files issues with evidence, keeps notes, and remembers what it learned in governed memory (app / you / team). Credentials stay in a separate vault. It is not a chatbot that writes test-case text into a grid and stops. Execution, evidence, and a readiness verdict are part of the same loop. Specialists The lead agent routes work to specialists: explorer, author, bug-hunter, runner, triager, campaign planner, budget steward, mobile lead, desktop lead, and localization lead. Coordination keeps generation, execution, and triage from collapsing into one hallucinated answer.

Question 18

What AI capabilities are available?

Accepted Answer

Test-case and flow generation from requirements, stories, acceptance criteria, screenshots, spreadsheets, or a live explore. Automation execution (browser, mobile, desktop) without the team writing Playwright first. Defect writing with reproduction steps, expected vs actual, severity, and screenshots. Release-readiness assessment from live runs, open defects, and coverage - not a slide. Campaign planning: costed, gated, approvable plans for “test this release.” Analytics and composed dashboards / PDFs from the same numbers the UI shows. Localization: ingest, estimate, translate, LQA, delivery, in-context validation. Accessibility scans and keyboard journeys (Axco). Specialist skills cover smoke, sanity, regression, form fuzz, UI bugs, performance, load, security basics, network chaos, exploration coverage, and surface-specific leads (web, mobile, desktop, localization).

Question 19

How does AI-generated test case creation work?

Accepted Answer

How cases are designed Users provide requirements, user stories, acceptance criteria, screenshots, specifications, or a live application. The agent designs structured cases using standard techniques: happy path, negative (empty / malformed / wrong type / out of range), boundary, state transition, equivalence, decision tables, permissions, and domain-rule violations. What a saved case includes Each saved case has a name, priority with rationale, technique, type labels, preconditions, test data, numbered steps, and a verifiable expected result. When a BRD or story is present, cases map to requirement IDs and gaps are listed. Drafts Generated tests are editable before they become a shared suite. The agent does not silently promote draft cases into the release gate.

Question 20

Can AI generate automation scripts?

Accepted Answer

Yes. More importantly, it can run the journey itself and keep a reusable flow. Teams that need a script can export recorded runs (including mobile runs to an Appetize/Playwright-style spec). The default path is: author in natural language → execute on a real surface → heal by accessibility id / label / text → keep evidence. That is how we reduce repetitive scripting without creating another brittle selector farm.

Question 21

Can AI automatically write defects?

Accepted Answer

Yes. From a failed run the agent drafts a defect with title, detailed description, reproduction steps, expected versus actual, severity recommendation, environment, and supporting screenshots or recordings. Defects are ordinary issues in the workspace and can sync to Jira or GitHub when those connectors are on. The agent will not hide a critical or paint a green report.

Question 22

Does a human stay in the loop?

Accepted Answer

Autonomy is bounded Yes. Autonomy is bounded. Large or destructive work is proposed as a campaign and waits for a signature. Spending sits inside envelopes (cost, cases/day, minutes, concurrency). Logins, OTPs, CAPTCHAs, and production-like actions can pause for a person (take control / HITL). Localization deliveries require human approval; legal / finance / medical tags can force a human review. Self-approval of gated work is refused. You can pause, resume, halt, or throttle a running campaign. A senior engineer on the team The agent is a senior engineer on the team, not an unsupervised bot in production.

Question 23

How does the agent explore an unknown product?

Accepted Answer

Order of work Order for “test this site” with nothing else: Open it. If login exists and no credentials are in the vault, it asks once. Explore briefly (spawn an explorer for anything beyond a few pages). Write notes. Propose a plan with counts (for example 8 smoke flows, 12 regression, 1 form-fuzz session) and wait when the job is large. Author flows → run them → file issues → summarize with numbers and where things are saved. Bounded exploration Exploration is bounded by design (action caps and path rules). Coverage is reported against the map the agent built, not against an unknown whole.

Question 24

How does it handle logins, SSO, CAPTCHAs, and bot protection?

Accepted Answer

No evasion KaDeep AI never evades protection. When a target refuses an automated browser, the run is marked BLOCKED (access) - not a product defect - and the platform names the mechanism (Cloudflare, Akamai, PerimeterX, DataDome, Imperva, CAPTCHA, WAF, attestation, rate limit, login wall) plus the remedies a security team can grant. Supported lanes Supported lanes: Egress IP allowlists. Customer-granted test-traffic headers (encrypted, host-scoped). Saved logins after a human signs in once. CAPTCHA test keys or a human solve via take control. SSO popup handling: complete the identity-provider window, return, save the session. Nothing in the product solves CAPTCHAs, rotates residential proxies, forges fingerprints, or replays attestation tokens. That is intentional and is the answer InfoSec wants.

Question 25

What can the agent not do?

Accepted Answer

State these in the meeting so they are not discovered in a pilot: CAPTCHAs and bot managers are classified, not defeated. Screen readers (VoiceOver / TalkBack) are not driven; accessibility is axe + keyboard + predicted announcements, plus a recommended manual pass. Visual judgement depends on the model; sub-pixel rendering issues are not reliable. Race conditions that need sub-millisecond timing may not reproduce. Appetize mobile: no pinch; iOS has no scriptable offline / battery / push. Real-device farms (BrowserStack / Sauce / USB) are a connector tier, not bundled. Web, iOS, Android, and desktop run in the cloud. A paired machine remains for private networks and localhost. Campaign estimates are wide until the project has its own run history.

Question 26

How does KaDeep AI help us ship faster with confidence?

Accepted Answer

The bottleneck The bottleneck is rarely “we cannot write another test case.” It is late evidence: coverage unknown, localization unchecked, accessibility last-minute, defects in five tools, and a go/no-go meeting that argues from slides. How the loop shortens KaDeep AI shortens that loop: Agent authors and executes against the candidate. Defects land with evidence the same day. Localization and accessibility run as part of the same campaign when in scope. Readiness is a live score with reasons, not a green spreadsheet. Stale greens are labelled stale - an old pass is not “ready.” Most teams can be operational in days; a first useful readiness signal often appears in the pilot week.

Question 27

How do you measure release readiness?

Accepted Answer

How the score is computed Readiness is computed from the last 14 days of completed runs, open defects, and requirement coverage. It is a drillable state, not a vibe. Input Role in the score Pass rate Half of the score Open critical defects Heavily weighted; any open critical → not ready Requirement coverage Remainder of the score Freshness Last run older than the freshness window (default 72 hours) → stale, never shown as ready Thin sample Fewer than 5 runs is labelled so a lucky green is visible States States: no_data · stale · not_ready · at_risk · ready. When a GitHub repo is connected, readiness can note how many commits landed since the tested build so you do not ship untested HEAD. Predictions Predictions (leakage, schedule risk) are rule formulas, not a secret ML model. The agent is instructed to say so.

Question 28

What does a go/no-go look like for an executive?

Accepted Answer

A plain sentence: “Release readiness is 82% on Payments staging; two open majors; last run 4 hours ago; 6 commits landed since the tested build.” Drill into gates, execution, coverage heatmap, defects, and leakage. Super-admins see the org picture; testers see the project. The agent can produce the same payload as a spoken answer, a live infographic, or a PDF. It will refuse to hide criticals. The project dashboard behind that sentence includes gates, execution, coverage heatmap, defects, leakage, predictions, and productivity. Manager / admin altitude adds the enterprise shell, org readiness, localization floor, and spend envelopes. Reports ship as markdown, HTML, or PDF.

Question 29

How long does implementation take?

Accepted Answer

A first project, environments, users, a smoke suite, and a first readiness view can be standing in a few days. Complex enterprise deployments with workflows, integrations, SSO, and custom configuration typically take 2–3 weeks. Typical onboarding: discovery → platform configuration → workflow setup → user onboarding → integrations → training → pilot.

Question 30

What if we do not want to migrate existing TestRail / Excel / Jira history?

Accepted Answer

Migration is optional. Many customers start new projects in KaDeep AI and keep history where it is. We can integrate and coexist during transition. Cases can also be authored from a spreadsheet when a team wants a one-time import of active suites only.

Question 31

What ROI should we typically expect?

Accepted Answer

Faster test creation and higher coverage, less reporting effort, clearer release visibility, better defect quality, higher automation productivity, and faster decisions from evidence-backed readiness. Localization programs additionally see TM leverage (exact matches are applied without a model call) and fewer post-launch locale defects. We prove ROI on the pilot’s own numbers, not a generic multiplier.

Question 32

Can readiness and testing sit in CI/CD?

Accepted Answer

Yes. KaDeep AI connects to modern DevOps workflows so testing, automation, localization validate, and reporting become pipeline steps rather than a weekly meeting. CLI and MCP expose flows, suites, runs, issues, and localization push / pull / status / validate. GitHub can be used to file defects, read the PR behind a candidate, and check CI before a campaign. A failing localization or QA gate can block a release the same way a failing unit-test job does.

Question 33

Is the solution cloud-based or desktop-based?

Accepted Answer

Primarily cloud, with first-class desktop and web access. KaDeep AI Studio adds local execution. The same workspace is used in the browser and inside the desktop app. A companion mobile app is view-only for live watch (browser, virtual device, or paired desktop). Choose the lane per environment: cloud browser for public staging, Studio for VPN / localhost / native apps.

Question 34

Which cloud providers do you support?

Accepted Answer

KaDeep AI can be deployed on AWS, Azure, Google Cloud, private cloud, or customer-managed infrastructure depending on security and compliance requirements.

Question 35

Can KaDeep AI be deployed on-premise?

Accepted Answer

Yes. On-premise is supported through KaDeep AI Studio and enterprise server deployments when customers require complete control of infrastructure and data. Private-cloud and customer-VPC patterns are the usual regulated-industry path.

Question 36

How does pairing a machine work?

Accepted Answer

Install Studio on the Mac or PC that has the app (or the browser profile) you need to test. On macOS, grant Accessibility and Screen Recording. Open Desktop → Pair this computer. Leave Studio running. Heartbeats keep the worker online. A global stop (Ctrl/Cmd+Shift+Esc) and an on-screen HUD let anyone in the room halt the agent. Headless lab machines use KaDeep AI Studios in worker mode.

Question 37

Can Studio reach localhost, VPN, and internal staging?

Accepted Answer

Yes. That is the main reason Studio exists. A cloud browser cannot see localhost or a private VPC. A paired machine sits on the same network as the build. Saved logins stay on that browser profile’s terms; vault secrets stay encrypted.

Question 38

Does KaDeep AI offer APIs?

Accepted Answer

Yes. The platform is integration-first: REST and Socket.IO for the workspace, MCP for Cursor / Claude and internal tools (flows, suites, runs, issues, plus loc_* on localization projects), and CI routes for pipeline jobs. Enterprise reporting and third-party automation can sit on the same APIs.

Question 39

Which platforms and browsers do you support?

Accepted Answer

Applications Applications under test: web, mobile (Android and iOS), desktop (Windows, macOS, Linux), APIs and microservices, enterprise apps. Browsers Browsers: Chrome, Edge, Firefox, Safari / WebKit. Chromium is the default cloud lane. Firefox and WebKit run where a local engine is installed; a missing engine fails its matrix cell truthfully instead of faking a pass. Matrix Matrix: engine × viewport / device. Divergent cells lead the report. Customer-specific browsers can be arranged.

Question 40

How does mobile testing work?

Accepted Answer

Virtual devices Drop an Android APK or a zipped iOS simulator .app. The agent installs it on a virtual device (Appetize), reads the native hierarchy, taps / types / swipes, and records screenshots, logs, and network. Take control from web or Studio; watch live from the phone app. Environment controls Environment controls include appearance, language, location, biometrics, permissions, and (on Android) network, notifications, battery, and font scale. Device builds Device builds (.ipa / .aab) do not run on the virtual lane - the product says so and asks for the right artifact. One readiness view The same release can also run web smoke, an iOS + Android matrix, and a desktop smoke on a paired lab machine, then roll into one readiness view. Localization validation reuses those live tabs.

Question 41

How does native desktop testing work?

Accepted Answer

How the driver works A paired Studio worker drives installed apps through the OS accessibility tree (AX / UIA / AT-SPI) with a pixel fallback for canvases. Every action records method (ax / px / key / api) and whether the driver confirmed the effect. Runs get per-step screenshots and an MP4 trajectory. Bounds Bounds keep the agent inside an app allowlist. Blocked OS surfaces (terminals, system settings) stay off unless permitted. Secure fields are masked. File writes stay in the project workspace. Preflight Preflight a protected environment before spending a campaign. The platform reports egress addresses, saved logins, and bypass headers. Security grants a lane; the agent records it on the run’s Changes tab.

Question 42

What localization capabilities do you offer?

Accepted Answer

Linguistic validation, UI localization testing, globalization testing, cultural / locale-convention review, multilingual release readiness, and localization quality reporting - plus an actual translation workbench, not only a bug tracker for “string truncated.” Ingest of real localization formats (not “paste into Excel”). Translation memory and terminology (DNT, forbidden terms, brand). Machine translation with glossary and style constraints. Deterministic QA rules + MQM review by a different model than the translator. Human review and delivery gates. In-context validation in a real browser (truncation, overlap, untranslated, RTL, lang, layout drift). Image text: OCR → translate → render → vision read-back. Export native format, XLIFF 2.1, or TMX with a checksum. CLI / MCP / CI status and validate.

Question 43

Walk Localization Studio end to end.

Accepted Answer

Walk-through Create a Localization project. Set source and target locales (BCP-47, including hi-IN, ta-IN, pt-BR vs pt-PT, RTL tags, CLDR plurals). Ingest. Drop SCORM, websites, JSON/ARB, XLIFF, Office, iOS/Android strings, PO, RESX, YAML, properties, CSV/XLSX, SRT/VTT, images, or a URL. Detection is by file shape. Each asset becomes documents → segments with protected inline codes. Estimate. Word counts, TM leverage, locales, cost. Exact and 101% in-context TM matches are applied without a model call. Approve a campaign. Pilot locale first if the customer wants to see quality before the rest of the pack. Translate as a durable job (visible progress in chat and on every workbench tab). Terminology and style guide are constraints, not suggestions. QA. Rules on every write (placeholders, plurals, numbers, dates, currency, DNT, forbidden terms, length, wrong script). Then MQM review on another model: accuracy, fluency, terminology, style, locale convention, layout fit. Human review in the Workbench. Back-translation for reviewers who do not read the target. Consistency check for the same source translated two ways. Delivery per asset × locale: translating → in-review → needs-rework → approved → delivered. Critical flags block export. Tagged legal / finance / medical content can require a human. Export native, XLIFF, or TMX with sha256. Validate the localized build or live site: screenshots, DOM analysis, vision review, defects with a loc facet. That is the multilingual release gate. Jobs Jobs checkpoint, pause, and resume. “Translated” means draft. Only accepted counts toward a delivery. Roles Roles. Tester altitude: workbench tabs. Manager: Localization floor, mode filter (All · Testing · Localization). Admin: org localization governance (allowed models, ZDR / data-policy filter, task policy). Role presets include Linguist, Reviewer, and Localization manager. Deliveries approve from Mission control and the phone. QA-only organizations see no localization chrome. Kind is chosen at project create and is not silently mixed.

Question 44

Which file formats do you ingest?

Accepted Answer

Shipped filters include: HTML, JSON / string catalogs, SCORM packages, SRT/VTT, plain text, XLIFF, Android XML, iOS strings / stringsdict / xcstrings, PO/POT, RESX, YAML, .properties, CSV and XLSX bilingual tables, DOCX / PPTX / XLSX, and generic zip bundles (inner files become documents). Unknown formats are not silently forced: the user chooses HTML, plain text, or asset-only. Re-ingest of the same name versions the asset and carries translations for unchanged sources. Identical bytes are refused.

Question 45

Which languages do you support?

Accepted Answer

Major global languages plus extensive Indian-language coverage (Hindi and multiple South Indian languages). Additional languages can be arranged through our localization network. The workbench is locale-agnostic (BCP-47). Low-resource pairs (Indic, SEA, African) default to a stronger model tier. Regional variants keep separate translation memories.

Question 46

How do translation memory, terminology, and style work?

Accepted Answer

Memory, terms, and style TM: exact and in-context (101%) matches apply automatically; fuzzy matches are hints to the model. Customers own the TM and can export TMX. Terminology: termbase with approved targets, DNT, and forbidden terms. Empty termbases can get generated proposals; humans activate them. Style guide is injected into the translate prompt. Re-using approved memory is how cost per word falls over time. Ask for leverage on the estimate card. Who owns the TM Who owns the TM and termbase? The customer. KaDeep AI is the platform. Export TMX / TBX / XLIFF is the portability answer.

Question 47

How is linguistic quality measured?

Accepted Answer

Two layers Two layers: Rules - deterministic, every write. MQM review - a second model, never the translator, with category and severity. Severity and triage Critical blocks acceptance and export. Major must be fixed or dismissed with a reason. Minor is polish. Triage order: legal/financial meaning → untranslated visible text → terminology → cosmetic. This matches how enterprise LQA programs already score vendors (MQM / DQF-style scorecards), so scores can sit next to an incumbent LSP.

Question 48

Can KaDeep AI validate multilingual releases in the UI, not only in a CAT grid?

Accepted Answer

Yes. That is a core reason Localization Studio lives in the same product as the QA agent. Validation opens the locale overlay or the live site in a real browser, captures source and target, and looks for truncation, overlap, off-canvas text, untranslated copy and alt, RTL direction, lang, and layout drift. Findings become ordinary issues with a localization facet. GTW² is the “give us the production URL and the locale list” version of this idea for marketing and content sites.

Question 49

What is GTW² versus Localization Studio?

Accepted Answer

Localization Studio Localization Studio is the system of work: files, TM, linguists, approvals, exports, CI. Use it when you produce or accept translations. GTW² GTW² is the system of proof on the live site: crawl every locale, screenshot, catch what escaped the TMS (missing strings, RTL, visual regression) without requiring a TMS integration. Most global programs need both. Translation quality in the file is not the same as quality in the shipped page.

Question 50

Can AI translate images? What about video and audio?

Accepted Answer

Images Images - shipped. OCR extracts text boxes as segments (buttons/labels tagged UI). Those segments translate and review like any string. A render step redraws the picture per locale and a vision model reads it back; missing strings are flagged and coverage is recorded. Video and audio Video / audio (transcription, subtitle burn-in, TTS dubbing, muxing) are not in the current Localization Studio build. They can be scoped as a services engagement or a later platform phase.

Question 51

Can localization run in CI and via MCP?

Accepted Answer

Yes. loc push | pull | status | validate over the CI API, and MCP tools for assets, search, status, translate, and deliveries. A release job can fail on open critical loc flags or a delivery that is not approved.

Question 52

Can you support localization and multilingual testing as a service?

Accepted Answer

Yes. Localization and linguistic validation are core strengths, including Hindi and South Indian language programs at 500-screen scale. Hybrid is common: KaDeep AI runs the workbench and gates; our linguists or the customer’s in-market reviewers accept deliveries.

Question 53

Do you support accessibility testing?

Accepted Answer

Yes. Through Axco, our accessibility and compliance solution, we support assessments, remediation guidance, and compliance reporting. Axco is built into the KaDeep AI platform - it is not a bolt-on scanner you remember the week before launch. On each key page the agent runs an axe scan, files one issue per rule with impact mapped to severity, then walks the main journey keyboard-only (visible focus, Enter/Space, Escape, no traps, labels, heading order).

Question 54

Which accessibility standards do you support?

Accepted Answer

Industry-recognized standards such as WCAG. We help organizations improve accessibility readiness across web and (with the same caveats as agent limits) mobile. Honest limit: VoiceOver and TalkBack are not driven. Findings come from the accessibility tree and computed properties; a manual assistive-technology pass is still recommended for a formal VPAT / legal claim.

Question 55

Can KaDeep AI generate accessibility reports?

Accepted Answer

Yes. Findings, compliance status, and remediation recommendations consolidate into actionable reports and can ride along with release-readiness PDFs so accessibility is a gate, not a side email.

Question 56

How is customer data protected?

Accepted Answer

Encryption in transit and at rest, role-based access, audit logging, a secrets vault separate from product memory, org isolation, and deployment options that align with the customer’s policy (SaaS, private cloud, on-prem, Studio). Auth sessions (cookies after a human login) are encrypted, expiry-bounded, and never printed in the UI. Bypass headers are encrypted and host-scoped.

Question 57

Who owns the data and the IP?

Accepted Answer

The customer always owns their data. KaDeep AI is the platform provider. Customers retain ownership and control of project, testing, reporting, translation memory, terminology, and project-specific deliverables unless a contract says otherwise. The same rule applies to IP created during a services engagement.

Question 58

What happens to our data if we stop using KaDeep AI?

Accepted Answer

Customers can export in agreed formats before contract closure: cases and flows, run evidence, defects, reports, localization assets, XLIFF / TMX / native files. We can provide transition support.

Question 59

How is customer data protected when AI is used?

Accepted Answer

How AI follows policy AI follows the customer’s security policy: SaaS - model calls go through a single key-resolution path (customer BYOK on OpenRouter, or an optional shared platform key). Private / ZDR - org allowlists and zero-data-retention filters can restrict providers. Studio / on-prem - sensitive execution and artifacts can stay on customer-controlled machines; model routing is still an explicit customer choice. Every generated segment, screen, flag, and file can record model, provider, prompt version, and cost. What we say in diligence Enterprise conversations should name which subprocessors see prompts. We do not say “data never leaves” if a cloud model is in use. We say where it goes and what the switch is.

Question 60

Do you train foundation models on our prompts, tests, or recordings?

Accepted Answer

KaDeep AI does not take customer content to train a shared public model. Customer data is used to run their project (memory, TM, reports). Shared-model training is off unless a future contract explicitly says otherwise. Ask us to put “no training on customer data” in the DPA. We expect that clause.

Question 61

Can KaDeep AI support regulated industries?

Accepted Answer

Yes. Deployment flexibility, auditability, RBAC, exportability, and a human in the loop are how the platform is built for regulated environments. Suitability is architectural and contractual, not a claim about a named industry installed base. We do not lead with a certificate we have not shown you. Suitability is architectural (isolation, encryption, audit, private deploy, human gates) and contractual (NDA, DPA, BAA discussion where applicable). Evidence packs are provided during diligence.

Question 62

Does KaDeep AI support Single Sign-On?

Accepted Answer

Enterprise deployments can support SSO and centralized authentication based on customer requirements (typical ask: Entra ID / Okta / Google via SAML or OIDC). Group-to-role mapping is an enterprise configuration item. Separately, the agent can test the customer’s SSO - including identity-provider popups - which is a different question and a common demo win.

Question 63

How do roles, audit, and secrets work?

Accepted Answer

Roles Built-in altitudes: Tester, Manager, Super Admin, plus localization presets. Permissions are capabilities, not hard-coded pages, so an auditor or security-reviewer role can be added without a rewrite. Testers operate in assigned projects. Managers assign work and see team dashboards for their projects. Super Admins see org readiness, spend, and policy. A user can be a tester in one project and a manager in another. Role changes and denied actions can be audited. Secrets and credentials are not stored in the three memory layers. Destructive tools, production targets, and high-cost models ask for a signature. Industries The platform is industry-agnostic. Proof comes from a pilot on your product — journeys, findings, and a ship decision — not from a vertical SKU or a named installed base.

Question 64

Which integrations ship today?

Accepted Answer

Connector Typical use Jira Import requirements / epics, file and update defects, link runs GitHub Defects, PR/commit vs tested build, CI status before a campaign Slack / Teams Campaign summaries, approval links, channel notices Railway (and similar deploy logs) Confirm the preview is up; read deploy logs on environment errors MongoDB Verify what a UI action actually wrote MCP Use KaDeep AI from Cursor / Claude as tools Adobe (Firefly, Analytics, CJA, AEM) Entitlement / connector tier - wired, live use needs customer IMS CI/CD is first-class. CMS / LMS / Figma / Phrase pushes from Localization Studio are connector work, not a silent promise in the current build.

Industry	They will ask	Lead with
Regulated data	Data residency, SSO, audit, production-like staging, PCI-adjacent data	Private deploy / Studio, vault, no evasion of WAF, HITL on destructive, readiness tied to commit
Patient or health data	PHI, BAA, environment isolation, accessibility	Customer-controlled deploy, no training on customer data, Axco + keyboard journeys, module-level readiness
Digital media / creative / enterprise software	Desktop apps, NDA, localization at volume	Studio native desktop, paired lab, Localization Studio + GTW², confidentiality posture
Public sector / regulated	On-prem, export, continuity, SLA	Studio + server deploy, contractual export, continuity commitments, written security pack
Consumer mobile	iOS/Android, biometrics, store locales	Virtual devices, language/locale on device, honest iOS limits, real-device connector when needed
Global product / i18n teams	TM ownership, MQM, Indic languages, in-context	TM export, Hindi and South Indian coverage, delivery gates, CI validate

Enterprise FAQ

What are your offerings?

Platform

Services

Which industries ask what

What to bring to a first technical demo

On your product. Not a deck.