Gradient Labs vs Kore.ai (2026)
The decision is whether you want one agent that finishes a narrow class of work or a platform on which you build an estate. Gradient Labs sells the first. Otto reasons through a query and takes the action, such as freezing a card, filing a dispute or running a due diligence check. It works under more than twenty compliance guardrails per turn and is deliberately exposed to only the one or two tools each procedure needs, which is constraint used as a safety mechanism. Kore.ai sells the second, a banking application on an enterprise agent platform reaching more than forty channels through more than three hundred integrations. Each has done something to earn trust that the other has not. Gradient Labs prices on outcomes, so unresolved or reopened cases cost the vendor revenue, which puts its own money against its performance. Kore.ai has submitted to a government operated security assessment with a defined baseline and a sponsoring agency, and documents its security certifications and where it runs in detail. Neither says what happens when its agent is wrong, a gap this category rarely closes, and Kore.ai's marketplace leaves a further question open about who answers when a partner supplied agent is wrong.
- You want the query resolved, not deflected. Otto reasons through a query and takes the action, freezing a card, filing a dispute or running a customer due diligence check, rather than routing to a help center. It works across voice, text and email frontline support as well as back office investigations.
- Constraint is the safety mechanism you trust. More than twenty compliance guardrails are applied on every turn, procedures are written by the institution in plain English, and the agent is deliberately exposed to only the one or two tools a given procedure needs, which limits what a wrong reasoning step can reach.
- You want the vendor's revenue tied to being right. Gradient Labs prices on outcomes, so unresolved or reopened cases cost it money, and it says more about pricing than Kore.ai does. This is alignment, not a liability commitment.
- You are buying a platform to build on, not a single agent. Kore.ai ships AI for Banking as a pre built application on its agent platform, with banking specific intents, workflows and compliance controls covering retail, commercial and wealth management across account support, card servicing, fraud resolution and loan servicing.
- Channel and system reach is the requirement. Kore.ai reaches more than forty voice and digital channels, including interactive voice response, messaging and live agent handover. More than three hundred integrations connect it to core banking, payment platforms, CRM, risk engines and data warehouses.
- Your security and residency review is the gate. A FedRAMP Moderate authorization is a government operated assessment with a defined baseline and a sponsoring agency, and Kore.ai pairs it with PCI certification. It documents its security certifications and where it runs in detail, where Gradient Labs documents its certifications in part and says little about where it runs.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Gradient Labs and Kore.ai are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Gradient Labs | Kore.ai | |
|---|---|---|
| Primary category | Customer & Banking Agents | Customer & Banking Agents |
| Founded | 2023 | Not published |
| Headquarters | London, England, United Kingdom | Not published |
| Website | gradient-labs.ai | www.kore.ai |
Side by Side
Select any grade to read the note behind it.
| Axis | G Gradient Labs |
K Kore.ai |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
Product updates
The latest logged changes from each vendor, newest first, each linked to its source.
Gradient Labs
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Product / capability
Gradient Labs launched Collaborate, which lets operators and engineers edit and improve AI agents using software development practice.
Source
Kore.ai
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Safety / governance
Kore.ai Agent Platform 1.8.0 gives Autoloop, its evaluation driven loop that proposes improvements to agents, three approval modes.
Source -
Product / capability
Version 1.7.3 of Kore.ai's Agent Platform changes how agents handle several requests in one message: two new settings decide when a request counts as finished and have the supervisor capture every request at once, and the automatic strategy now handles requests one after another and asks the customer when they are ambiguous.
Source -
Product / capability
Version 1.7.2 of Kore.ai's Agent Platform lets builders choose how much of each tool result the model keeps in conversation history (the full result, selected fields or a template summary) and treats workflow and MCP tools as having side effects by default, so retries do not repeat operations and confirmation policies cover them.
Source
The short version of each
Gradient Labs
Gradient Labs builds Otto, an autonomous customer operations agent for regulated financial services. Otto does not deflect to a help center. It reasons through a query and takes the action, freezing a card, filing a dispute or running a customer due diligence check, with more than twenty compliance guardrails applied on every turn. It follows procedures the institution writes in plain English and deliberately exposes only the one or two tools a given procedure needs. It works across voice, text and email frontline support as well as back office investigations, sitting over existing helpdesks. According to the AI FinTech Index, it publishes the most on how central AI is to the product, its customer results and its integrations. It prices on outcomes, so unresolved or reopened cases cost it revenue. It says little about privacy terms, bias and fairness, where it runs and where data sits, or what happens when it is wrong.
Source: AI FinTech Index, 2026
Kore.ai
Kore.ai builds an enterprise agent platform on which organizations design, deploy, govern and optimize AI agents. On top it ships AI for Banking, a pre built application for banks and credit unions carrying banking specific intents, workflows and compliance controls across retail, commercial and wealth management. It covers account support, card servicing, fraud resolution and loan servicing. It reaches customers through more than forty voice and digital channels and connects agents to core banking, payment platforms, CRM, risk engines and data warehouses through more than three hundred integrations. According to the AI FinTech Index, it publishes the most on how central AI is to the product, who it serves, its integrations, where it runs and its security certifications. It has a FedRAMP Moderate authorization and PCI certification. It says little about pricing, bias and fairness, the models it is built on, or what happens when it is wrong.
Source: AI FinTech Index, 2026
Common questions
Is Gradient Labs better than Kore.ai?
They are different purchases. Gradient Labs sells one agent that completes a defined class of customer operations work end to end, tightly constrained by guardrails and by only exposing the tools each procedure needs. Kore.ai sells an enterprise agent platform with a pre built banking application on top, reaching more than forty channels through more than three hundred integrations. If you want a narrow set of tasks finished, Gradient Labs. If you are standardizing an agent estate across retail, commercial and wealth, Kore.ai. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
If the agent gets it wrong, who is responsible?
Neither says, which is usual in this category rather than a failing of either. What is worth knowing is which comes closest. Gradient Labs prices on outcomes, so it earns on resolutions and unresolved or reopened cases cost it revenue, which puts the vendor's money against its own performance without ever becoming a liability term. Kore.ai publishes no error rate, remediation commitment or correction path, and its marketplace adds an unanswered allocation question between a partner agent's author, the platform and the bank whose name is on the conversation. Who pays when the agent is wrong is left to the contract rather than the website. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Which one will clear a government or bank security review?
Kore.ai, and it is the widest gap on this page. It has a FedRAMP Moderate authorization, a government operated assessment against a defined baseline with a sponsoring agency, plus PCI certification. It also documents where it runs in detail, with on premises among the options. Gradient Labs documents its security certifications in part and says little about where it runs. If your constraint is a public sector or heavily supervised procurement, that difference will decide the evaluation before anything about agent quality is discussed. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Where does each publish the most, and the least?
Gradient Labs publishes most on how central AI is to Otto, its customer results and its integrations, and says little about privacy terms, bias and fairness, where it runs or what happens when it is wrong. Kore.ai publishes most on its AI, who it serves, its integrations, where it runs and its security certifications, and says little about pricing, bias and fairness, the models it is built on or what happens when it is wrong. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Customer & Banking Agents page.
Well documented vendors in this category rarely publish what happens when an agent errs, and neither of these does. What distinguishes them is how close each gets without arriving. Gradient Labs comes nearer by pricing on outcomes, so unresolved or reopened cases cost it revenue. That is a real incentive and still not a commitment to bear the consequences of being wrong.
It publishes no accuracy guarantee, no remediation term and no route for someone whose card was frozen in error or whose dispute the agent declined. That matters more than in advisory products because the agent takes the action rather than recommending it. Kore.ai adds a question its architecture creates.
When a pre built or partner supplied agent from the marketplace gives a bank's customer wrong information, nothing states where responsibility sits between the component's author, the platform distributing it and the institution whose name appears on the conversation. Both say little about bias and fairness. Kore.ai's exposure is concrete because of where the agents operate.
Loan servicing, fraud resolution and card blocking are consumer facing decisions, and a fraud resolution agent treating some customers' disputes differently from others produces exactly the uneven outcome bias disclosure exists to detect. Gradient Labs runs across voice, text and email on two continents with nothing published breaking resolution or escalation rates down by language, accent or customer segment. On the models underneath, the smaller vendor discloses more. Gradient Labs shows through a failover design that multiple external model providers sit in the path, while Kore.ai names no provider, family or version for the agents it supplies itself.