Haptik vs Teneo (2026)

The integration catalog is documented page by page rather than asserted as a round number: Shopify, Magento, WooCommerce and Fynd for commerce; Stripe, PayPal, PayU, Razorpay and SETU for payments; Zendesk, Freshdesk and Salesforce for service; HubSpot, LeadSquared and Microsoft Dynamics 365 for CRM; Twilio, Exotel and Helo for communications; CleverTap for engagement; and CIBIL for credit data. That is breadth across classes. The Indian market integrations are the distinguishing set: SETU, Razorpay, Exotel, CIBIL and Fynd are the local payment, telephony and credit rails a platform selling to Indian banks and retailers needs. Integrations are write paths, not only lookups: agents create tickets, check inventory, issue payment links and schedule appointments, and Code Step Integration covers custom logic where no connector exists.

Teneo does not publish a connector catalog, and the vendor treats that as a feature: connection is through a Public API with no fixed connector list, so any business system can be reached rather than only those on an approved roster. For contact center deployments that matters, because the systems behind a call are often a telco's own BSS or a hospital's EHR, which appear on few connector lists. Breadth is met by the range of systems named, spanning CRM, EHR, global distribution and contact-center-as-a-service, with native integrations into the major CCaaS platforms so agents sit inside the customer's existing telephony estate. Open protocols extend the surface both ways: Model Context Protocol for tool access and an agent-to-agent protocol for interoperability with agents the customer did not build in Teneo. The lack of a connector list cuts both ways: unlimited reach in principle, no verifiable inventory in practice, so how much is prebuilt versus wired by each customer through the API cannot be checked.

Haptik's agents plan and execute tasks by calling APIs and databases, and the vendor explicitly distinguishes this from language models that respond to prompts rather than executing tasks. The documented flow surface is a step graph: Connections represent the path a conversation takes, each mapped to the user inputs, entity presence or entity values that route to the next step, with Static, Code and Output step types and Code Step and Static Step integrations for custom logic. Execution ends in real actions across systems: ticket creation, inventory checks, payment links and appointment scheduling. No multi agent coordination is documented; this is multi step task execution with conditional routing in a conversational frame. The eighty percent automation figure is the vendor's own reported outcome.

Orchestration is what the vendor sells: the product pages are built around unifying LLM orchestration and agent management in one layer, and the vendor's framing is that success in 2026 is about coordinating agents, not deploying more of them. Concretely, there is a native, no-code environment for goal-driven agents that the vendor contrasts with basic intent recognition: an intent router picks a branch, while a goal-driven agent selects a path toward an outcome. Agents are documented selecting workflows autonomously, breaking down complex tasks, taking actions and coordinating with one another, with multi-agent collaboration through an agent-to-agent protocol so specialized agents share context. Scale supports the claim: the vendor cites over seventeen thousand agents in production, with Telefonica Germany handling more than nine hundred thousand calls a month. No branching, looping or conditional constructs are named, and the multi-agent claims rest on product-page descriptions rather than developer documentation.

Haptik documents Proactive Messaging that starts conversations without a user message: campaigns sent to a selected audience immediately or on a scheduled custom date, and event based triggers, with SMS for US users and WhatsApp HSM templates for outbound use. The scheduled wake is an outbound message campaign rather than a scheduled agent task. Channel coverage is wide: Web SDK, WhatsApp, Facebook, Instagram, Sunshine Conversation, LINE, Google Business Messages, Telegram, Microsoft Teams, Bot as an API, and iOS and Android SDKs, plus voice with inbound and outbound telephony through an Exotel integration. More than a hundred languages, including twenty two Indian languages, is why the platform works in tier two and tier three markets, where customers use WhatsApp in a regional language rather than a web widget in English.

Agents deploy across voice, chat, SMS, WhatsApp, web, social and messaging from one platform, with native contact center integrations so an agent sits inside the customer's existing telephony rather than beside it, in more than eighty languages. Voice sets this apart from messaging-first platforms. Handling inbound telephony at scale is a harder engineering problem than answering web chat, and the vendor cites a named telecommunications customer at over nine hundred thousand calls a month with ninety percent containment. Those figures are vendor-reported, but the deployment they describe needs the channel infrastructure to exist. The language breadth lets a European carrier or a global airline run one platform rather than a patchwork, and it reflects the twenty-year natural-language lineage behind the product. Scheduled or event-driven starts independent of a customer contact are not documented, which fits an inbound contact center product: work arrives when a customer calls.

Haptik grounds agents on a maintained knowledge base built from the customer's own material: PDFs, DOCX, PPTX, CSVs and the customer's website, with a product catalog as a separate structured source for recommendations. The vendor states it improves accuracy by combining multiple RAG systems, a considered retrieval architecture rather than a single vector lookup, and that answers stay based solely on provided information. Knowledge Base Integration connects agents to third party knowledge management systems, with documented integrations for Zendesk Guide, Freshdesk KMS, Salesforce KMS and USU Unymira, so the corpus tracks the customer's existing system of record rather than requiring a parallel copy. Per answer source attribution is not documented, a gap regulated buyers may notice.

The platform page says Teneo connects to proprietary or public databases, document stores and data lakes to "ingest, index and query" the knowledge base. A knowledge base that is ingested and indexed is a standing structure, not context assembled per prompt. Two further properties stand out. Retrieval runs on infrastructure the customer already owns, with Teneo RAG documented against AWS Bedrock, Azure OpenAI, OpenSearch and AI Search plus any custom model endpoint, so the retrieval stack is not locked to one cloud. And the vendor documents end-to-end visibility, monitoring and control over every retrieval and augmentation step, with PII security enforced through the pipeline, which matters for teams handling customer records in regulated contact centers. Adaptive Answers capture sentiment, history, age and location during a conversation and adapt output, which is personalization layered on the grounding rather than grounding itself.

Haptik's agents retain context for personalized experiences and handle multi turn conversations, which is within conversation state, and the Archives section preserves transcripts for review. Neither carries what an agent learned in one conversation into the next. The vendor's argument that language models respond to prompts rather than actively executing tasks or learning from interactions implies its own agents learn from interactions, but that is marketing prose about something else, not documentation. The knowledge base persists and counts under knowledge grounding. The documentation is extensive, and a user state or context persistence page could exist.

Unified Customer Memory retains preferences, past interactions and historical data across channels, so a customer who web-chatted on Monday and calls on Thursday is recognized with context intact. Cross-channel continuity is harder than it sounds in a contact center, where channels are usually separate systems. The memory is about the customer, not the agent. Nothing documented describes an agent retaining what it learned from handling a case, carrying a conclusion into an unrelated interaction, or updating its own behavior from a correction. Adaptive Answers adapt output within a conversation from retrieved state rather than accumulated learning. This rests on product-page descriptions rather than documentation of what is stored, for how long, or how it is governed, which for a platform holding customer interaction history under GDPR is a question to ask early.

What is documented is a handoff rather than a gate. Agents escalate high touch scenarios to human counterparts through live chat, with a centralized agent inbox so no interaction is missed and people can jump into a conversation; the Agent Inbox, Live Traffic and Teams sections confirm the surfaces exist. That suits customer experience work, where the failure mode is a frustrated customer stuck with a bot rather than an agent taking a destructive action. But escalation transfers the conversation once the agent cannot proceed; it does not pause the agent for approval before it acts, and nothing gates ticket creation, a payment link or a booking on a person's sign off. Guardrails are named and SOP compliance is tracked, but neither is described as a mechanism. Grounding does real work: an agent stated to answer solely from provided information is constrained at the output layer.

Teneo's oversight is automated rather than human-gated. Guardrails at scale cover accuracy, relevance and safety, with protection against prompt injection and validation of every response before it reaches a customer. For critical processes, a deterministic fallback written in Teneo's TLML language takes over when generative output cannot be trusted. Escalation to human agents is documented for cases needing judgment. What is missing is an approval step in the run path: nothing documented has a person review and approve an agent's action before it executes.

The grade rests on a published trust center rather than a claim. Attestations are deep: ISO 27001 and ISO 27701, GDPR, CCPA, CPRA, HIPAA, FIPS 140-2, NIST SP 800-53 and CyberGRX, with independent penetration testing by a CREST certified third party and audits by BDO India. ISO 27701 is the privacy information management extension, so privacy governance is certified separately rather than asserted, and FIPS 140-2 with NIST SP 800-53 is United States federal cryptographic and control baseline territory, a strong signal for a company centered on Indian enterprise. The control surface is thinner than the certifications: single sign-on, role based access and retention configuration are not documented.

What Teneo documents directly is narrower than its marketing: enforced enterprise-grade PII security across the retrieval and augmentation pipeline, protection against prompt injection, and immutable audit trails. Certifications and an access control model are not documented on any security, trust or compliance page; those paths on teneo.ai return 404 and the homepage names none. A security or trust page, or the SOC 2 report itself, would settle this.

Agent Analytics, integrated with an Insights Agent, provides custom KPI dashboards, sentiment measured from the start to the end of a conversation, and tracking of agents' compliance with standard operating procedures, and the Analytics, Live Traffic, Archives and MyChats sections confirm the surfaces. Conversation level visibility is strong, and SOP compliance tracking is a substantive quality signal. It holds at Partial because it observes conversations, not agent execution: no trace shows which integrations an agent called in what order, with what arguments, or why it chose one path. When an agent creates a ticket, checks inventory and issues a payment link, the analytics show the outcome and the customer's sentiment, not the sequence.

The vendor documents step-level visibility directly: "end-to-end visibility, monitoring and control over every retrieval and augmentation step". Control alongside visibility means an operator can intervene, not only observe. The agent-level half is visual real-time insight into which models and goals each agent uses and how flows are triggered. For a platform that routes each step to a different model, seeing which model served a given interaction is what an operator needs: what happened, by which model, under which goal. Immutable audit trails supply the compliance record, and every interaction is tracked. These are product-page descriptions rather than documentation of what a trace contains or how long it is kept. Nothing documents exporting this data to the customer's own monitoring stack.

Multi region data residency at the Enterprise tier is reported only in third party pricing material, described there as critical for finance, healthcare and government and as a negotiated enterprise term rather than a documented product feature. Haptik never states it on its own pages, so it supports Partial and no more. The circumstantial case is strong: as a Reliance Jio subsidiary serving Indian banks, insurers and telecoms under RBI and IRDAI rules, where local data residency is frequently required, Haptik would struggle to sell to most of its named customers without Indian data centers. But circumstance is not evidence, and the trust center is where a first party statement would sit.

The vendor's CTO describes a self-scaling, containerized platform in the Microsoft Azure Cloud that scales without manual intervention during peak call volumes. That is the vendor's own hosting, and containerization there is an elasticity property, not a customer deployment option. Nothing documented offers on-premises installation, deployment into the customer's own subscription, a virtual private cloud option, customer-managed keys, or a selectable region. An open question: Teneo's predecessor product from Artificial Solutions was sold as an on-premises platform for many years, and a vendor of this vintage with Telefonica and government customers may still offer it under contract. It is not documented anywhere reached; a deployment or architecture page would settle it.

Haptik ships three named prebuilt agents: an AI Support Agent resolving queries with multi turn context, an AI Sales Agent giving catalog aware recommendations, and an AI Booking Agent for reservations and scheduling. Those are ready made agents a customer adopts and configures, not components to assemble, and Agent Assist adds prebuilt integrations into Zendesk, Freshdesk and Salesforce agent desktops. Three agent types read partly as a product taxonomy rather than a library, and no browsable catalog exists. The product is sold sales led, so how a customer adopts these agents without a full build is not documented.

What Teneo ships is a composition system: reusable modular skills, tools and goals that an agent is assembled from, which can be updated centrally and deployed instantly across every running agent. Central updates that propagate to all agents are valuable at the seventeen thousand agents Teneo says it has in production. But these are building blocks the customer defines and reuses, not finished agents the vendor supplies ready to adopt. Templates are referenced, but no template library, agent gallery or named set of prebuilt industry agents was found. A vendor with two decades of contact center deployments across telecommunications, airlines and banking likely has industry accelerators that are not marketed on public pages. The centrally updatable design is worth noting regardless.

Haptik states that teams choose between top models including GPT, Llama and Claude based on their needs for performance, security and scale, and invites them to experiment across those models to find the best fit: the customer chooses the model, which is what this axis measures. Haptik names security as a selection criterion alongside performance and scale. For an Indian enterprise weighing which provider may process customer conversations under RBI or IRDAI rules, which model runs where is a compliance decision, and letting a bank choose Llama over a US hosted API solves a real problem. Third party pricing material indicates premium model access is charged as pass through, so choice has a direct cost.

The vendor's product launch names the switchable set: GPT-4o, Claude 3, LLaMA 3.1, Falcon 180B, PaLM 2, Stable LM 2, Gemini Ultra 1.0 and Mixtral, plus any custom model endpoint, with the CTO saying the platform can "seamlessly switch between models while maintaining optimal performance and cost efficiency". That is customer choice across providers, and the platform page confirms Teneo RAG runs against AWS Bedrock, Azure OpenAI and custom endpoints too. Orchestration is the product's central claim rather than a setting. Model selection operates per step of an interaction, routing routine turns to lean models and hard ones to reasoning models. For a contact center running hundreds of thousands of calls a month, per-step routing is the difference between viable and not. The claimed cost reduction of up to 98 percent and the FrugalGPT lineage are vendor marketing figures.

Haptik's documentation publishes three SDKs, a Web SDK, an iOS SDK and an Android SDK, alongside a channel documented as Bot as an API, so the platform is callable from outside through documented interfaces. The SDK set says something about the product: customers embed Haptik agents inside their own applications, a more demanding form of extensibility than an admin calling a REST endpoint, and Bot as an API makes an agent addressable as a service. Code Step Integration and Static Step Integration let custom logic be inserted into an agent's flow, and the integration catalog is documented page by page rather than asserted as a number. No MCP server is documented.

A Public API is documented as the way any business system connects to Teneo, so the platform is addressable programmatically rather than only through its own interface, and MCP support means an assistant the customer chose can reach it. The pro-code half is the distinctive part. TLML, the Teneo Linguistic Modeling Language, is a proprietary language for building deterministic conversational logic, and it sits alongside the low-code builder rather than replacing it. Business users compose agents visually while engineers drop into a real language for the hard parts; the deterministic fallback for critical processes is written in it. An agent-to-agent protocol on open standards means Teneo agents can interoperate with agents built elsewhere, a bet on coexistence rather than lock-in. The developer documentation sits behind a sign-in, so the API's shape, authentication and coverage rest on product-page description.

Haptik documents a Bot QA Tool for regression testing: a Generate API turns mock conversations covering the test scenarios into a test case CSV based on step traversal, and a Run API executes those cases and returns a result CSV with success or failure for every case, run each time a bot goes live. A functional testing guide covers test case preparation across flows, APIs, error handling and multilingual behavior, and the Test bot shows per message debug logs. That is a harness that scores the bot's behavior against recorded cases, documented for step based bots through staging API endpoints.

What is documented is a real development lifecycle: built-in real-time testing, a build, test, deploy, analyze and optimize loop with version control, and the ability to validate model choices without code changes or downtime. That last capability sits at the boundary. Validating a model choice implies comparing outcomes between models, and for a platform built on per-step model routing, customers would expect to measure whether a routing change helped. But nothing documented names a test suite, a score, a pass rate, a judge verdict or a measured report the customer reads, and validating without downtime could describe deployment safety as easily as measurement. A testing or evaluation documentation page would settle it.

Everything a Haptik agent does runs through a documented programmatic interface. Channels are messaging and voice surfaces the agent converses on: WhatsApp, Instagram, LINE, Telegram, Microsoft Teams, web and mobile SDKs. Actions run through named integrations: creating a Zendesk ticket, checking inventory, issuing a Razorpay payment link, scheduling through a calendar. Code Step Integration executes custom logic. Real time web data integration, mentioned in third party descriptions of the platform, is retrieval over fetched content, not computer use. The absence is structural: agents live inside a customer's messaging channels, with no surface on which browser control would operate.

Everything a Teneo agent does runs through a documented programmatic interface. Channels are conversational surfaces the agent talks on: voice, chat, SMS, WhatsApp, web and social. Action on business systems goes through the Public API, with MCP for tool access and an agent-to-agent protocol for reaching other agents. Retrieval goes through Knowledge AI against databases, document stores and data lakes. Teneo claims nothing in browser or desktop control. The absence fits the product. A contact center platform whose agents live inside telephony and messaging channels has no surface on which browser or desktop control would operate, and its integration model, an open API into the customer's systems of record, is the direct alternative to driving an interface. At the nine hundred thousand calls a month Teneo cites for one customer, API integration is also the approach that scales.