Moveo.AI vs Teneo (2026)

AI Agents call tools in the form of webhooks and external MCP servers, which the customer adds and tests, and Dialogs connect to external services via webhooks and run task automation such as sending emails and updating databases. The plans page adds custom webhooks connecting to the customer's own APIs, maintained by Moveo, alongside native Zendesk, Intercom and messaging channels, with CRM systems and internal data sources supplying real-time customer context. Documented custom tool support with authenticated action in outside systems is the threshold on this axis, which is a capability test rather than a catalog-class test, and it is met.

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.

Moveo's Dialogs provide the control-flow primitives this axis looks for, and the vendor documents all four: multi-step workflows guiding users through complex processes, conditional logic creating branching paths based on user input, API integrations connecting to external services via webhooks, and task automation executing specific actions, with the vendor's own examples being sending emails and updating databases. Updating a database is back-office action taking. The architecture is worth carrying because it is a considered design rather than a feature list. Knowledge and guidelines handle most conversational needs, and Dialogs are documented as optional, complementing the knowledge-based approach when deterministic structured workflows are needed. So the platform is probabilistic by default and deterministic where it matters, which is the right split for collections and payment conversations where a wrong step has consequences. On top sits the proactive dimension: agents initiate conversations, manage objections and drive toward an outcome such as completing a payment or activating a card, which is goal-directed multi-step execution rather than turn-taking.

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.

The standout cell, on channels, proactive initiation and schedules. Channels are broad: chat, voice through telephony, email, SMS, WhatsApp, Facebook Messenger and Viber, plus Zendesk and Intercom as embedded surfaces, across more than twenty languages. Viber in particular signals a vendor that has done the work for its actual markets, Greece and Brazil, rather than shipping a US channel list. The distinguishing property is proactive outbound. Agents initiate conversations rather than waiting, running outbound campaigns the vendor describes as reaching down to a segment of one, to chase a payment, prompt a card's first use or intervene before a customer churns. Outbound initiation is an unusual event class for a conversational platform, and here it is the commercial point of the product: a collections agent that cannot start the conversation is useless. Campaigns can also be scheduled for a future launch with date, time and timezone, retry with a fallback when delivery fails, and follow up when a contact stays silent, which is an autonomous wake on a schedule.

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.

Persistence is the bar here, not citation-grounded retrieval, and Moveo has a maintained knowledge structure documented first party. The vendor's documentation states AI Agents operate on a knowledge-based system using retrieval augmented generation over a knowledge base holding curated sets of information from various sources, combined with guidelines that shape behavior. That is an explicit RAG architecture rather than context assembled per prompt. The part that settles persistence is the ingestion behavior of Collections, Moveo's knowledge layer: data sources include a website that is re-crawled every 24 hours, a help center, and uploaded CSV, PDF or HTML documents. A corpus that re-crawls its sources on a schedule is a standing structure that tracks what it was built from, which is what separates Full from Partial on this axis. Past chat logs can also be uploaded as grounding material. Worth carrying: grounding here is paired with real-time customer data from CRM and internal systems, so an agent answers from the corpus and personalizes from the customer record. For collections and retention conversations, which is what this product is for, both halves are needed.

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.

Session state, with stronger claims in the marketing than in the documentation. The documentation shows conversation context held as variables and user information through a session, visible per turn in the logs, and simulation criteria can assert on the final session context. The vendor's marketing says memory travels with the customer rather than the ticket, and describes agents as self-learning, evolving from every interaction and driving higher automation rates over time. Both are claims without a documented mechanism: nothing read says what is retained across sessions, for how long, whether a customer profile is a queryable object, or how a customer reviews or deletes it. What is corroborated is cross-channel continuity: live agents receive handoffs with full cross-channel context, which requires state to travel with the customer, so the claim is at least partly load-bearing in the product. A memory or customer-profile page in the documentation would settle it.

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 substantial and it is the right set for the use case. Live agents pick up complex cases through assignment modes with full cross-channel context, so escalation is a designed path rather than a fallback. Compliance officers review and modify conversation flows, which is oversight by a second role over what the agent is permitted to say, and that matters more than usual here because these are debt collection conversations where what is said is regulated. Agents are constrained to negotiate within defined parameters, payment-recovery campaigns run under a required compliance profile, and changes to an agent go to a draft before they are published. What is absent is a gate in the execution path. Nothing documented pauses a conversation for approval before an agent commits to a repayment plan, sends a payment link or writes to a system. Constraining what an agent may offer and reviewing flows beforehand are both design-time controls. So this is oversight before and around, not during, which is Partial. A documentation section on approvals or human-in-the-loop configuration would settle it.

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.

Both halves first party. The documentation states SOC 2 Type II covering security, availability and confidentiality, ISO 27001, and HIPAA compliance with a BAA available, with a Trust Center carrying certifications, reports, security practices and penetration test summaries. Single Sign-On and role-based access control through Permissions are documented, and only Admins and Owners can create API keys. The control that matters most for a bank running collections conversations is private model hosting: with Moveo's own models hosted privately and private or on-premises deployment available, conversation data need not pass to a third-party model provider. Compliance officers can also review and adjust conversation flows. Sovereign delivery properties count on deployment and are not counted twice here.

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.

A per-turn record of the agent's own decisions. Logs replay production conversations in the administrator view, showing for each turn the predicted intents with their confidence scores and the conversation context, including variables and user information, with an AI Agent log view for debugging individual agents and filters by channel, coverage, containment, tags and ratings. The Moveo MCP server can read session transcripts, and the API reference lists session and log-content endpoints. Analytics dashboards and a live-chat agent desk with a thread view and customer information panel sit around it. Whether webhook or tool calls appear in the production log is not stated.

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.

Private and on-premises deployment inside the customer's own infrastructure is documented, alongside EU data residency and three named hosting regions, Europe, the United States and Brazil, and the vendor's proprietary models are hosted privately, which is what makes on-premises meaningful here. That last point deserves emphasis. An on-premises platform that still calls out to a third-party model API lets the sensitive content, the conversation, leave the perimeter even when the application does not. Moveo can run its own models, so a private deployment can be self-contained. For a bank negotiating repayment terms with a customer, that is the difference between an on-premises checkbox and an on-premises fact. EU residency plus European engineering is the second half of the story and is the reason the named customers, Allianz, Alpha Bank, National Bank of Greece, are reachable at all. No detail is documented on what an on-premises installation requires. A vendor selling to banks would normally publish that, and its absence is worth noting.

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.

The bar asks whether the customer receives packaged assets ready to adopt, and a set of prebuilt agents qualifies; a browsable marketplace is evidence of the bar, not its definition. The vendor's documentation states "AI agents come in different types, each trained and optimized for a specific use case", and that some carry predefined guidelines a customer completes to get the best out of the agent. That is a named set of ready-made agents arriving pre-configured for a purpose. The campaigns documentation also ships two campaign types, Outreach and Payment recovery. The use cases these are built around are specific to the product's market, onboarding, collections, card activation, churn prevention and support, and the Auto Builder converts a plain-text description into a ready conversational flow for anything unlisted. The agent-type list itself was not read, so how many exist and how much they arrive pre-configured with rests on the documentation's description.

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.

Customer choice across several makers. Customers connect large language models from external providers, OpenAI, Azure OpenAI, Anthropic, Gemini and DeepSeek, by adding a provider under Deploy, Developer Tools, LLMs with their own API key and choosing the model from a dropdown. Connected models are listed with provider, model, creator and last use, and the model strategy guide configures which models power an AI Agent on each channel. Moveo's own proprietary models, fine-tuned for business conversations and hosted privately, remain one option among the connected providers, and they are what make a self-contained on-premises deployment possible when the conversation should not leave the perimeter.

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.

Moveo publishes its own MCP server with regional endpoints for Europe, the United States and Brazil, signed in through the browser or with a Manage-type API key sent as documented headers, and about 100 tools covering the platform's own objects: creating, cloning, training, publishing, comparing and rolling back AI Agents, changing guidelines and workflows, managing knowledge bases, environments and routing rules, and running simulations. The documentation site also carries a REST and analytics API reference, with endpoints for brains, dialogs, collections, campaigns and API keys. Both let an outside caller drive the platform. Webhooks from Dialogs run the other way, Moveo calling other systems, and count on integrations.

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.

Simulations are regression tests for an AI Agent. The customer writes a scenario and success criteria, a simulated customer plays it through the agent's real production pipeline over chat, email or voice, and an evaluator grades the finished conversation pass or fail with written reasoning. Criteria can assert on tool calls, webhooks fired, handover, fallback responses and final context variables. Runs can be launched singly, in bulk or on a weekday, weekly or monthly schedule, and the Runs tab reports success rate, failures and duration per run. For a product negotiating repayment terms under financial-services rules, testing behavior against scenarios before deployment is exactly what a buyer needs, and it is documented. Simulations require a paid plan.

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.

Every documented action path is programmatic. Agents answer from a knowledge base using retrieval, and where deterministic behavior is needed Dialogs connect to external services via webhooks and execute task automation such as sending emails and updating databases. Webhooks and database writes are programmatic interfaces by definition. Channels are conversational surfaces the agent talks on, not software it operates. There is no near miss on this record: Moveo never claims interface-level operation anywhere. The absence is structural. A conversational platform whose agents live inside WhatsApp, Viber and telephony has no surface on which browser control would operate, and the integration model, webhooks into systems of record, is precisely the alternative to driving an interface.

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.