Gradient Labs vs Omilia (2026)
The decision is whether the risk you are managing is the breadth of what an agent can do or the dependency chain underneath it. Gradient Labs takes actions, reasoning through a query and freezing a card, filing a dispute or running a due diligence check, across frontline support and back office investigations. Omilia runs the conversation itself on a stack it built over two decades, its own speech recognition, voice biometrics, dialogue management and synthesis, with certified payment capture and caller authentication. Conversational banking vendors usually do not name the providers behind assistants conducting authenticated banking conversations, and these two are exceptions in opposite directions. Omilia has no providers to name, so nothing beneath its deployment can be repriced, deprecated or versioned by somebody else. Gradient Labs names the problem rather than the parties. It discloses through a failover design that multiple external suppliers sit in the path, which still leaves a bank unable to identify who is reading its customer conversations.
- The work is operational rather than conversational. Otto reasons through a query and takes the action, freezing a card, filing a dispute or running a customer due diligence check, and works back office investigations as well as frontline support, which is a wider set of outcomes than a contact center stack is built to produce.
- You want procedures your own team writes and controls. Institutions author procedures in plain English, more than twenty compliance guardrails apply on every turn, and the agent is exposed to only the one or two tools a given procedure needs.
- You want the vendor paid on resolutions. Outcome based pricing means unresolved or reopened cases cost Gradient Labs revenue. Omilia publishes no rates, packaging or basis of charge.
- You do not want a model provider in the path at all. Omilia runs a fully proprietary stack built over two decades, naming its own speech recognition, voice biometrics, dialogue management and speech synthesis engines individually, so there is no third party whose pricing, availability, versioning or data handling can change beneath your deployment. Where conversational vendors usually leave their providers unnamed, Omilia has none to name.
- Payments over the phone are in scope. Payment capture is certified to the highest PCI DSS compliance tier through both speech and keypad with real time redaction so card data is never stored in clear text, which is an audited certification rather than an alignment claim.
- Caller authentication is the problem. Passive and active voice biometrics combine with real time detection of deepfakes, synthetic callers, spoofed numbers and replay attacks. Omilia documents its accuracy and validation in detail and where data sits in part. Gradient Labs documents the first in part and says little about the second.
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 Omilia 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 | Omilia | |
|---|---|---|
| Primary category | Customer & Banking Agents | Customer & Banking Agents |
| Founded | 2023 | 2002 |
| Headquarters | London, England, United Kingdom | Larnaca, Cyprus |
| Website | gradient-labs.ai | omilia.com |
Side by Side
Select any grade to read the note behind it.
| Axis | G Gradient Labs |
O Omilia |
|---|---|---|
| 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
The short version of each
Gradient Labs
Gradient Labs builds Otto, an autonomous customer operations agent for regulated financial services. It does not deflect to a help center but 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, deliberately exposes only the one or two tools a given procedure needs, and works across voice, text and email frontline support as well as back office investigations. According to the AI FinTech Index, it documents in detail how central AI is to the product, its customer results and its integrations. It discloses through a failover design that multiple external model providers sit in the path. It says little about privacy terms, bias and fairness, where data sits or what happens when it is wrong.
Source: AI FinTech Index, 2026
Omilia
Omilia runs a fully proprietary conversational AI stack for enterprise contact centers, built over two decades. It comprises its own speech recognition, voice biometrics, dialogue management and speech synthesis rather than being assembled from third party components. Its financial services line ships more than 300 models trained on banking and finance intents. It handles payment capture to the highest PCI DSS compliance tier through both speech and keypad with real time redaction, and combines passive and active voice biometrics with real time detection of deepfakes, synthetic callers, spoofed numbers and replay attacks. According to the AI FinTech Index, it documents in detail how central AI is to the product, its customer results, its accuracy and validation, and the models it is built on. It owns the stack outright, so no third party model provider sits in the path. It says little about pricing, AI safety, bias and fairness or what happens when it is wrong.
Source: AI FinTech Index, 2026
Common questions
Is Gradient Labs better than Omilia?
They are built for different work. Gradient Labs runs customer operations, reasoning through a query and completing the action across frontline support and back office investigations. Omilia runs the contact center conversation itself on a fully proprietary speech, biometrics, dialogue and synthesis stack, with certified payment capture and caller authentication. If the bottleneck is operational work that needs finishing, Gradient Labs. If it is voice containment, authentication and payments over the phone, Omilia. 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 can tell me what models are behind the agent?
Omilia, and the reason is ownership. Its stack is fully proprietary and disclosed component by component, covering its own speech recognition, voice biometrics, dialogue management and speech synthesis, so no third party model provider sits in the path whose pricing, availability, versioning or data handling could change beneath the deployment. Gradient Labs takes the other route. It discloses through a failover design that multiple external cloud and model suppliers are involved, which conversational vendors often leave unsaid, without naming which ones or on what terms. 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.
What happens when either one gets a customer wrong?
Neither publishes it, which is usual among conversational vendors rather than a failing of either one. The exposures differ and both are worth raising in contract. Gradient Labs takes actions, and nothing published covers a customer whose card was frozen in error or whose dispute the agent declined. Its outcome based pricing aligns incentives without creating a remedy. Omilia authenticates callers biometrically and moves money. Nothing published covers what happens when voice authentication wrongly rejects a legitimate customer, whether callers consent to biometric enrollment or can opt out, or how a caller reaches a human when containment is the measure being optimized. 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 the product, its customer results and its integrations. It says little about privacy terms, bias and fairness, where data sits or what happens when it is wrong. Omilia publishes most on how central AI is to the product, its customer results, its accuracy and validation, and the models it is built on. It says little about pricing, AI safety, bias and fairness 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.
The model supply chain is where these two are most interesting, and they differ in opposite directions. Conversational banking vendors usually do not name the generative and speech providers behind assistants conducting authenticated banking conversations. Omilia escapes that by owning the stack outright, naming its own recognition, biometrics, dialogue and synthesis engines component by component, so the dependency does not exist. Gradient Labs escapes it by admitting the dependency.
It discloses through a failover design that multiple external cloud and model suppliers sit in the path, and then does not name them, so a bank knows its customer conversations reach several third parties without being able to identify them. One removed the risk, the other disclosed it, and only the second still owes a buyer an answer. Both say little about what happens when they are wrong, and the affected parties differ.
Gradient Labs takes actions on accounts, so the unaddressed person is one whose card was frozen in error or whose dispute was declined. Omilia authenticates callers biometrically and moves their money, so the unaddressed questions are what happens when voice authentication wrongly rejects a legitimate customer, whether callers consent to biometric enrollment or can opt out, and how someone reaches a person when containment is the metric being optimized. Both say little about bias and fairness, and Omilia's published word error rate is an average that says nothing about who is at its tail.