Orbio vs ZingHR (2026)

Eight systems are named across three classes. Meta4, Workday, BambooHR and Bizneo cover HRIS and core HR, Lever, Teamtailor and Bullhorn cover applicant tracking, and Salesforce covers CRM. Orbio says it plugs into the tools a customer already uses, for richer data and faster action. The agents act through these systems, not just beside them.

They screen, interview, schedule and run onboarding, and they reach candidates and workers around the clock across time zones by calls, WhatsApp, SMS and other channels. Orbio also ships its own applicant tracking system that manages recruitment and candidates and hosts custom career pages with source tracking, so records are written as well as read, and the agents are deployed into the customer's existing systems.

With three named ATS platforms alongside its own, the product is built to sit in a customer's existing stack, not replace it. The integrations list does not state the direction or depth of each connector, and richer data reads as inbound. There is no connector directory, documentation for each system or sync description, so Orbio does not say what it writes back into Workday or Bullhorn as against what it reads.

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ZingHR lists 100+ enterprise integrations, with SAP, Oracle and Dynamics 365 alongside Zoom, Teams and WhatsApp, and prebuilt coverage across ERP, banking, assessment and communication platforms. In production, Xero connects globally through both API routes, SAP SuccessFactors is reached through ZingHR Pull APIs against SAP's own exposed APIs for recruitment, and Oracle PeopleSoft attendance is fed by ZingHR Push APIs.

Host to host banking covers HDFC, HSBC, JPMorgan and ICICI with automated payroll disbursement, so the platform moves money in a third party system on the customer's behalf. External API orchestration in the Workflow Engine connects workflows to data in external systems, so a flow the customer configures can reach a system with no prebuilt connector. In the other direction, customer Push and Pull APIs let outside systems call ZingHR, and requests arrive through WhatsApp, Teams and the ZingHR app. Naukri and LinkedIn are not connected yet and are on the product roadmap.

Sourcezinghr.com/solutions/zing-intelligence-hub with support.zinghr.com integrations articleread 2026-09-12

Three specialized agents cover the whole employment lifecycle, from job post to retention insights, working across the customer's stack. They come as a switchable set covering Recruitment, Onboarding and Employee Insights. María is the recruitment agent and has her own page. Given the need, she works always on from job description to onboarding, with expertise in recruitment and sourcing, detecting talent and managing the hiring process.

The sequence runs as one system, not three separate tools. Signals cross the stages, so onboarding and engagement outcomes inform who is selected next, and live retention insights, which aim to spot who is likely to leave well before they do, feed back from the last stage. Several parties take part.

The HR team states the need, the three agents do the work, candidates and workers are reached over calls, WhatsApp and SMS, and a human validator must sign off on critical HR decisions. No orchestration runtime is named, no handoff protocol between the agents is described, and there is no authoring surface. The flow is Orbio's HR process, configured to the customer's policies by Orbio's own team during implementation. Of the three agents, only María is named.

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The signature ZingBot workflow runs twelve numbered steps: employee initiates query, WhatsApp based access, live database lookup, RAG powered retrieval, employee specific context, policy validation, instant AI response, resolution in under five seconds, transaction requirement detected, Zingo raises request, smart escalation triggered, auto routed support ticket.

It branches, since step 09 decides whether the run ends at an answer or continues into a transaction, and step 11 diverts to a person.

The Workflow Engine is the part customers configure, a no code builder with a drag-and-drop form builder with conditional fields and approval routing, separate flows for operational and strategic approvals, and external API orchestration that connects steps to systems outside ZingHR, so a flow can be set up for any non-standard process without code or an IT ticket.

ZingZeroTAP holds state across a long cycle, with real time, self reconciling attendance and payroll across every pay structure, location and currency and no manual intervention. Self reconciling means the process finds and resolves its own discrepancies between runs, and a payroll cycle spans weeks of attendance events across jurisdictions before it closes.

The Employee Life Cycle Agent works the same way over a longer horizon, from confirmation to separation, with every milestone structured, documented and executed. The participants are the employee raising a request, the manager approving it, the HR administrator configuring the flow, the person receiving an escalated ticket, and the external systems each step reads and writes.

Sourcezinghr.com/solutions/zing-intelligence-hub signature workflow and Workflow Engine, with the homepage agent setread 2026-09-12

Outreach runs around the clock across time zones by calls, WhatsApp, SMS and more. Voice, WhatsApp and SMS are three distinct modes, and they are the ones that reach frontline and deskless workers, who usually have no corporate email address. Orbio offers a live call with Maria, its AI talent acquisition assistant, so voice is a real synchronous channel, not a notification. Reach extends to more than 60 languages, engaging everyone in their native language. Custom career pages with source tracking ship with the ATS, so an application is an event arriving in the pipeline.

The agents run always on instead of waiting for an operator. They work at any time and across time zones, and retention insights run continuously enough to flag churn risk well before it happens. Events drive the later stages. Replies are qualified, a hired candidate passes from recruitment into onboarding, and engagement signals trigger retention flags. Beyond the three named modes Orbio names no further channel, and there is no cadence control to set the interval between touchpoints.

Sourceorbio.work homepage, /security and /contactread 2026-09-12

Work starts from channels, conditions and schedules, all generally available.

ZingBot answers HR queries via WhatsApp, Teams or the ZingHR app, as the Intelligence Hub page states twice. WhatsApp matters here because ZingHR sells into Indian, Middle Eastern and Southeast Asian enterprises with large deskless and field workforces, where WhatsApp is the channel a frontline employee actually has. Going the other way, Workflow Engine approvals "arrive as push notifications and are actioned in 30 seconds," so the platform reaches the manager instead of waiting to be opened.

Conditions start work too. Compliance 24/7 states that "every jurisdiction is monitored without pause," with real time alerts and continuous monitoring, a watcher raising something nobody asked for. In the published ZingBot sequence, step 09 detects a transaction requirement and step 11 triggers smart escalation, so a condition inside a conversation starts a different process. The Engagement Agent's sentiment tracking and the Performance Management Agent's continuous OKR tracking work the same way.

Schedules and the employee lifecycle drive the rest. Payroll runs to a cycle across every pay structure, location and currency, with "every cycle closed on time." The Employee Life Cycle Agent hangs actions off dated milestones "from confirmation to separation," and Zing Learn auto assigns learning paths to every role and career path.

Naukri and LinkedIn integration is on the product roadmap, per an FAQ on the support portal. No full trigger catalog is published, and whether a customer can define its own event conditions outside the Workflow Engine is not stated.

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The agents ground on the customer's own data, and that data persists. Orbio ships a built in applicant tracking system that manages recruitment and candidates and hosts custom career pages with source tracking, and integrates with Meta4, Workday, BambooHR, Bizneo, Lever, Teamtailor, Bullhorn and Salesforce for richer data and faster action. On top sits a longitudinal profile of each worker built across recruiting, onboarding and retention, a store that stands between runs and grows with use.

Every AI generated response comes with transparent reasoning, source references and confidence scoring, so the agent grounds on identifiable material, not a model's general knowledge. The agents decide from that accumulated record. Matching aims at better fit candidates with fewer false positives and higher pass through quality, and retention insights aim to spot who is likely to leave well before they do, both judged over the customer's accumulated record, not a job description alone. Orbio names no index, embedding store or retrieval mechanism, so the structure behind the persistence and grounding is not published.

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ZingBot answers HR queries through WhatsApp, Teams or the ZingHR app, grounded in the customer's actual policies through retrieval-augmented generation, not generic large language model output. ZingHR contrasts generic assistants that answer with generic HR policy with ZingBot answering from the customer's actual policy, the employee's actual data and the organization's actual rules.

Every transaction across every ZingHR module writes to a single unified database with no ETL, no overnight batch and no sync delay, and dashboards load live data the moment they open, so the corpus is kept current, not assembled for each run. Retrieval is its own step. Steps 03 to 06 of the ZingBot workflow are live database lookup, RAG powered retrieval, employee specific context and policy validation, so the answer is checked against policy, not just generated near it.

Unstructured content has a way in, since ZingLens OCR extracts GSTIN, amount, date and claim type from photographed bills and pre-fills the form. Row level security applies at login, so each persona reaches only its authorized data, which bounds what retrieval can reach. ZingHR does not describe citations back to the policy behind an answer, how the policy corpus is loaded or refreshed, or any freshness or confidence signal on a response.

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What looks like memory here is the customer's HR data, and there is no agent memory. A longitudinal profile of each worker is held because Orbio ships a built in applicant tracking system that manages recruitment and candidates and syncs from Workday, BambooHR, Meta4 and Bizneo. That record is what the agents read, grounding data, not memory the agents keep.

Signals from later stages feeding back into decisions are model learning, not a store. Orbio says its agents continuously learn and improve based on feedback and changing HR requirements. Learning built into a model improves the system, but a customer cannot point at it, inspect it or clear it.

There is no named memory layer, retained preference set the agents reapply, decision trace carried between runs as an object to inspect, stated scope or lifetime, or customer control to inspect, edit, export or purge agent state apart from the worker records. The AI governance controls run down to prompt versioning, input and output logging, confidence scoring and an immutable audit trail, and none of them is a memory component. Those logs are a record of conduct, not memory.

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ZingHR describes a system of record, not agent memory. Its central claim is one employee record, one source of truth, one database and real time data flow, with every transaction across every module writing to the single unified database. That persistent store of employee records is a data model, and the agent queries it for retrieval.

The homepage says the platform learns from itself and that intelligence compounds at every stage, but neither phrase describes state the agent keeps, scopes and can retrieve, or anything a customer could inspect. The closest thing is step 05 of the ZingBot sequence, employee specific context, which assembles the requesting employee's record to answer the question in front of it and carries nothing from a previous conversation.

Nothing says a second query knows what the first one asked, that a correction is reapplied or that a preference survives the session, and the sequence runs from query to resolution in under five seconds and ends. There is no named memory component, ZingBot conversation history or thread persistence, retained user preference, decision trace reused between runs, scope or lifetime for agent state, or way to inspect, edit, export or purge it apart from employee records. Row level security governs what the assistant may read.

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Human validation is required, not optional. Under its human in the loop controls, Orbio says AI management follows responsible AI principles and critical HR decisions require mandatory human validation and oversight. The agent cannot complete a critical HR decision without a person validating it. The validator has what is needed to judge. Every AI generated response arrives with transparent reasoning, source references and confidence scoring, so the person sees the basis and the model's own certainty before deciding.

Protections also run before anything executes. Defenses against prompt injection and prompt tampering, with output filtering, prevent model misuse or leakage of sensitive information, and environment isolation bounds what the agents can reach. Orbio does not define which decisions count as critical, and there is no configurable threshold, approval queue or escalation path, so a customer cannot see where the line falls or move it.

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The Workflow Engine is ZingHR's own approval mechanism, and the customer shapes it. It is no code workflow automation with separate flows for operational and strategic approvals, a drag-and-drop form builder with conditional fields and approval routing, and custom approval flows for any non-standard process without code or an IT ticket. Zingo, the conversational automation layer, raises leave applications, submits claims and regularizes attendance through conversation.

Those transactions enter the approval flow instead of completing themselves, and approvals arrive as push notifications to be actioned in 30 seconds, so an action the agent raises waits on a named person before it takes effect. Escalation is the second mechanism. The last steps of the ZingBot workflow are transaction requirement detected, smart escalation triggered and auto-routed support ticket, so the agent hands off to a person when a case exceeds it.

ZingHR argues against approvals that live in email and get lost in someone's inbox with no tracking, no audit trail and no accountability, and the same Workflow Engine carries the audit trail and the multistep conditional sequencing. There is no confidence threshold, autonomy dial, override log or reviewer queue object.

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Four compliance claims are held, not in progress. Orbio says it is ISO 27001 certified, SOC 2 Type II audited, GDPR aligned and ready for the EU AI Act, and shows four compliance marks on its homepage and security page with no in progress caption on any of them. CREST approved vendors carry out independent security validation with comprehensive infrastructure and application testing, which names an accreditation body for the testers, not just a pentest claim. A trust center is at trust.orbio.work. Access controls include role based access, behavioral analysis and an immutable audit trail.

Infrastructure security covers environment isolation, encryption at rest and in transit, continuous vulnerability monitoring and proactive threat detection. Two further commitments matter on an HR product. A zero training guarantee, contractually enforced and technically protected, keeps customer data out of AI model training, and privacy by design covers data minimization and user consent management. The marks appear without certificates or stated scopes, so the attestations rest on Orbio's own statement.

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ZingHR's operational center is certified to ISO 27001:2013, and elsewhere ZingHR says only that it meets standards like ISO 27001. ZingHR runs periodic network and application penetration tests of the cloud service infrastructure, and a named security team runs risk assessment and treatment across strategic, financial, regulatory, compliance, management, operational and technical domains.

Row level security applies at login and scopes data by persona, so the CHRO sees the full organization, the HRBP their business unit, the CFO costs and the manager their team, and each persona sees only its authorized data with no manual configuration. For an agent answering from one employee database, that row boundary decides what the assistant can reach.

Microsoft Azure holds ISO 27001, HIPAA, FedRAMP, SOC 1, SOC 2, IRAP, G-Cloud and MTCS for the underlying infrastructure, and ZingHR's own attestation is the ISO 27001 certificate. That certificate names ISO 27001:2013, a version since superseded by ISO 27001:2022 with the transition window closed, and there is no trust center, certificate registry or renewal date.

There is no single sign on, SCIM, MFA or role model beyond row level security, customer audit is restricted in the multitenant environment, and security controls and audit reports can be reviewed on request.

Sourcesupport.zinghr.com audit-and-compliance article, with Row-Level Security from zinghr.com/solutions/zing-intelligence-hubread 2026-09-12

An immutable audit trail captures all user actions and AI interactions, covering both people and agents. AI monitoring is a named product area, with full input and output logging, continuous response evaluations and prompt versioning. Full input and output logging gives a record that can be reconstructed, and prompt versioning tells a reader which version produced a given output.

Each response also carries its own explanation, with transparent reasoning, source references and confidence scoring, so a reviewer sees not only what the agent did but what it read and how sure it was. The same controls include role based access, and the continuous evaluations and prompt versioning double as a loop for testing changes.

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Every workflow transaction, ZingHR says, is time-stamped and attributed with a permanent, immutable audit trail, which records when it happened and who or what acted, and cannot be edited after the fact. Zingo raises leave applications, submits claims and regularizes attendance through the Workflow Engine, so actions the agent starts land in the trail with attribution, not as anonymous system events.

A customer can reconstruct what the agent did, when and on whose behalf in the transactional layer, which is where these agents operate. ZingHR says email approvals have no tracking, no audit trail and no accountability, and claims an 80% reduction in audit prep time.

ZingHelp SLA compliance is tracked as a KPI, and the nineteen ZingIntel dashboards report headcount, attrition, payroll, performance, recruitment and succession, which measure the workforce, not the agent. The trail covers workflow transactions only.

There is no trace of ZingBot's retrieval or reasoning for each run, no record of which policy documents a RAG answer drew on, no confidence or citation trail on the conversational side, and no export or retention policy for agent activity or the trail itself.

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Orbio commits to residency at the inference layer. LLM inference runs within the EU for EU clients and follows data sovereignty compliance for clients based in the Americas. That names the EU as a place, at the layer that is hardest to localize. Around it sit environment isolation and encryption at rest and in transit. Only one geography is named as a location.

The Americas half is a compliance posture, not a place, since it says a rule is honored but not where the data sits. There is no region list, named customer environment or selection surface, and placement follows the client's domicile, not the customer's choice, so an EU customer has nothing to decide. Orbio also runs a trust center at trust.orbio.work and an application host at admin.eu.orbio.work.

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ZingHR names its hosting, and it leaves the customer no control. The application runs on Microsoft Azure, and in what ZingHR calls the united hosting environment it is multi-tenant, with customer access for audit restricted. No other environment is named.

ZingHR runs three regional site variants (IND, MEA and SEA), serves 30+ countries and localizes payroll compliance for each jurisdiction, but those are markets and statutory rules, not places a customer chooses to keep its data. A customer with an Indian, UAE or Singaporean data localization obligation cannot choose where its tenant is hosted or run ZingHR in its own environment. There is no region list, region picker, single tenant tier, private cloud, on premises option or self hosting.

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Orbio ships three complete agents, each doing a different job. A recruitment agent takes a role from job description through screening and interviewing, an onboarding agent onboards new hires, and an employee insights agent produces retention signals. They are presented as a set, the customer's new HR team, and María has her own page listing her expertise and strengths. Each works on its own. Without the insights agent, recruitment still fills roles, and without recruitment, onboarding still onboards people hired elsewhere. They are not three modes of one engine or three tiers of a price list.

They arrive prebuilt, not authored. There is no builder, studio or template gallery, and a customer receives working agents on day one. Tailoring is a managed step, not self serve. Orbio's team configures the agents to the customer's HR processes, policies and company culture and deploys them into the customer's existing systems. A customer chooses among three prebuilt agents but does not configure them alone, and Orbio does not say whether the three are licensed separately or arrive as one bundle.

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Three catalogs can be browsed.

The homepage lists eight named agents under their own heading, each with its own description and job. The AI Recruitment Agent runs cognitive assessments, personality profiling and AI ranked shortlists. The ZingZeroTAP Agent handles real time, self reconciling payroll. The Engagement Agent covers pulse surveys, grievance resolution and sentiment tracking. Compliance 24/7 monitors every jurisdiction with real time alerts.

The Performance Management Agent does continuous OKR aligned tracking, the Learning AI Agent delivers microlearning in 32 languages, the Employee Life Cycle AI Agent covers confirmation to separation, and the Travel and Expense AI Agent standardizes multi country travel policy. Each works on its own, not as a stage of one pipeline.

Twelve vertical solutions are the second catalog, "twelve purpose-built vertical solutions, each engineered around the workflows, compliance requirements and outcomes your domain actually runs on." They cover BFSI and microfinance, staffing, logistics, manufacturing, pharma, energy, retail, hospitals, conglomerates, IT services, education and NGOs, each with its own page in the primary navigation and its own rule set per market.

The third is content. Nineteen ZingIntel Power BI dashboards live across the modules, and "over 100 standard reports auto-generate from live module data with zero manual compilation." ZingESG adds packaged governance, risk, compliance and ESG reporting frameworks.

The 35+ modules on their own are a product line. The eight agents are homepage cards, not pages of their own, so each gets a paragraph, and no page states which agents are licensed separately from the modules they sit on.

Sourcezinghr.com homepage agent set, industries navigation and zinghr.com/solutions/zing-intelligence-hubread 2026-09-12

Which model runs is never stated, and the customer chooses none. Orbio's pages cover LLM inference, prompt security, prompt versioning and model misuse, and describe what the agents do, but name no model or provider for screening, the voice calls, matching or the retention signals. There is no model picker, admin setting, per agent assignment or bring your own key path.

What Orbio discloses about models is governance, not choice. LLM inference runs within the EU for EU clients, a zero training guarantee, contractually enforced and technically protected, keeps customer data out of model training, and prompts are versioned. The engineering blog at blog.orbio.work carries posts on the inference pipeline.

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No one outside ZingHR chooses the model. There is no model picker, admin setting, model assignment per agent, routing policy per module or bring your own key path, including on the Zing Intelligence Hub page, ZingHR's own AI product page.

ZingHR names no model provider at all, whether OpenAI, Anthropic, Azure OpenAI or an open weight model. The closest statement is a contrast about grounding, "Generic AI assistants answer HR questions with generic HR policy. ZingBot answers with your actual policy," which says nothing about which model generates the answer. So there is clearly no way to pick a model, and the stack underneath is undisclosed.

ZingHR runs on Microsoft Azure per its compliance article, but a cloud host is not a model. No MCP server is documented either, and neither routing across more than one model on ZingHR's side nor a customer or admin control is described.

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There is no developer API, SDK or MCP server. Orbio runs three subdomains. trust.orbio.work is a trust center, blog.orbio.work is an engineering blog with posts on the streaming pipeline, speculative prefetching and caching behind sub second replies, and admin.eu.orbio.work is an application host. None is a developer or documentation site, and there is no docs host, API reference, endpoint list, authentication guide, SDK, webhook or MCP server. The integrations point the other way.

Meta4, Workday, BambooHR, Bizneo, Lever, Teamtailor, Bullhorn and Salesforce are systems Orbio reaches into, and no outside system or outside agent calls Orbio. Implementation runs Analyze, Configure, Deploy, Optimize, with Orbio's own team configuring and deploying the agents into the customer's systems, so there is less reason to publish a build surface, and the blog is written about the platform, not for people building on it.

Sourceorbio.work homepage, /security and subdomain proberead 2026-09-12

An API reference sits on ZingHR's support site. The SyncSwipeData documentation describes an "API for pushing swipe details from client to ZingHR system," with a production endpoint at portal.zinghr.com/2015/route/Integration/SyncSwipeData, a separate UAT endpoint at clientuat.zinghr.com on the same path, POST as the method, token based authentication where each request carries a token that identifies it, and a typed request schema with mandatory fields, data types and lengths. Data flows in and writes, since the client posts into ZingHR and attendance changes as a result.

A second article covers the wider surface in both directions. The integrations FAQ defines customer Push APIs as "when a customer wants to push their data into the ZingHR platform" and Pull APIs as "when a customer wants to pull data from the ZingHR platform for use by their ERP systems," and states that "currently Employee-Basic, Master, Leaves, Attendance and Exit pull APIs are available."

That is five named read APIs plus documented write. The same article records the APIs in production use, with Xero globally through both routes, SAP SuccessFactors through ZingHR Pull APIs against SAP's exposed APIs for recruitment, and Oracle PeopleSoft attendance through ZingHR Push APIs.

No SDK, OpenAPI or Postman specification, developer portal or self serve key issuance is documented. The reference lives in a support knowledge base, not on a developer site. Coverage is five Pull APIs plus specific Push attributes, not the whole data model, and further Push APIs "can be made available based on customer needs." What is documented is complete enough for a customer's engineer to call.

No MCP server is documented anywhere. WhatsApp and Teams are channels, and the prebuilt connectors are ZingHR calling out, not outside systems calling in.

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Orbio evaluates its agents' output continuously and versions its prompts. AI monitoring includes full input and output logging, continuous response evaluations and prompt versioning. A change to the agent's instructions is tracked, and its output is assessed after it ships, a quality check over the agent's own output with change management attached. Confidence scoring on each response gives the evaluation a signal per item. There is no test harness a customer can read.

No score, pass rate, judge verdict, test set or before and after comparison is published, and nothing says a new prompt version runs against a known set before it reaches candidates instead of being evaluated afterward. No bias or fairness audit of the screening criteria is published. The Optimize step of implementation, where the agents continuously learn and improve based on feedback, is model learning, not testing.

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Nothing puts a change to the agent under test. There is no sandbox for agent behavior, dry run, holdout, A/B comparison, versioning of agent or workflow configuration, evaluator, scored verdict on what ZingBot answered, or regression surface. A customer can reconfigure a Workflow Engine flow or a rubric and it governs the next transaction, but nothing tries that change against known cases first, and no readable result compares before with after.

The Swipe Push API reference has a UAT endpoint at clientuat.zinghr.com alongside production. That is a test venue for the customer's own integration payload. Nothing promotes, blocks or verifies anything between UAT and production, and it does not check whether ZingBot's answers were right.

ZingHelp SLA compliance is tracked as a KPI. It is a live operational metric for the helpdesk's ticket performance, not for the quality of the agent's output, so a ticket a person closes inside SLA looks the same in that metric as one ZingBot resolves.

ZingHR claims 60% fewer routine HR queries, an 80% reduction in audit prep time, resolution in under five seconds and a 70% reduction in hiring effort. Those are outcomes the vendor asserts, not tools a customer runs.

Sourcezinghr.com/solutions/zing-intelligence-hub with support.zinghr.com swipe-push-apiread 2026-09-12

Nothing here drives a browser or a computer. The agents reach people by calls, WhatsApp, SMS and more in more than sixty languages, and they reach systems through named connectors into Meta4, Workday, BambooHR, Bizneo, Lever, Teamtailor, Bullhorn and Salesforce, plus Orbio's own built in ATS. Talking to a candidate on WhatsApp and writing a record through a connector both happen without a screen, and neither navigates an interface.

A live call with Maria is telephony, an agent conducting a phone interview over a voice channel, not operating a computer. There is no hosted or local browser, desktop session, remote or local computer control, RPA or extension. There is not even a sourcing browser extension, because sourcing runs against Orbio's own pipeline, not on pages a recruiter has open.

Sourceorbio.work homepage, /security and /contactread 2026-09-12

Nothing in or out of ZingHR runs through a screen. Everything moves through protocols or conversation. Outbound work goes through prebuilt connectors to SAP, Oracle, Dynamics 365, Zoom, Teams and host to host banking, inbound work comes through documented Push and Pull APIs, and the Workflow Engine's "external API orchestration" reaches other systems by API instead of driving them.

No hosted or local browser, desktop session, remote or local computer control, RPA, recorder, virtual machine or screen scraping fallback is described.

ZingLens OCR "extracts GSTIN, amount, date, and claim type from photographed bills and pre-fills the form," which is document ingestion. Attendance capture runs on swipe data the customer's own hardware posts to ZingHR through the SyncSwipeData API, so even physical input arrives as a JSON payload, not through anything the agent operates.

ZingHR sells a unified database and native connectors, positioning itself against suites that need middleware for integrations.

Sourcezinghr.com/solutions/zing-intelligence-hub, the homepage and support.zinghr.com integration articlesread 2026-09-12