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. This index has already recorded that vendors in this lane generally do not name the providers behind assistants conducting authenticated banking conversations, and these two are both exceptions in opposite directions. Omilia has no providers to name, which is why it holds A on model supply chain disclosure and why nothing beneath its deployment can be repriced, deprecated or versioned by somebody else. Gradient Labs names the problem rather than the parties, disclosing through a failover design that multiple external suppliers sit in the path, which is more than most manage and 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 centre 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, and it holds B on commercial transparency where Omilia grades C with no rates, packaging or basis of charge published.
- 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. It holds A on model supply chain disclosure, and in a lane where the standing finding is that nobody names their providers, this vendor is the exception because it has none.
- Payments over the phone are in scope. Payment capture is certified to the highest card industry 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, and Omilia holds A on model risk management and transparency, the only such grade on this page, alongside B on deployment residency and security certifications.
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
| 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 |
The short version of each
Gradient Labs
Gradient Labs builds Otto, an autonomous customer operations agent for regulated financial services, which does not deflect to a help centre 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. The AI FinTech Index grades it A on AI centrality, operational and outcome evidence and core systems and integration depth, with B on commercial transparency, institution coverage, AI safety, autonomy and oversight, regulatory status, model risk management, security certifications and model supply chain disclosure, documenting five of the nine regulatory axes the index tracks against an index average of 2.93 across 489 vendors. Its supply chain grade rests on disclosing, through a failover design, that multiple external model providers sit in the path. GLBA posture, governance and bias disclosure, deployment residency and liability and recourse are graded C.
Source: AI FinTech Index, 2026
Omilia
Omilia runs a fully proprietary conversational AI stack for enterprise contact centres, built over two decades and comprising its own speech recognition, voice biometrics, dialogue management and speech synthesis rather than assembled from third party components. Its financial services line ships more than 300 models trained on banking and finance intents, handles payment capture to the highest card industry 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. The AI FinTech Index grades it A on AI centrality, operational and outcome evidence, model risk management and transparency and model supply chain disclosure, with B on institution coverage, GLBA posture, autonomy and oversight, regulatory status, integration depth, deployment residency and security certifications, documenting seven of the nine regulatory axes the index tracks against an index average of 2.93 across 489 vendors. Its supply chain grade rests on owning the stack outright, so no third party model provider sits in the path. Commercial transparency, AI safety, governance and bias disclosure and liability and recourse are graded C.
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 centre conversation itself on a fully proprietary speech, biometrics, dialogue and synthesis stack, with certified payment capture and caller authentication. The AI FinTech Index grades Omilia at seven of the nine regulatory axes and Gradient Labs at five. If the bottleneck is operational work that needs finishing, Gradient Labs. If it is voice containment, authentication and payments over the phone, Omilia.
Which one can tell me what models are behind the agent?
Omilia, and it is the notable exception in this lane. 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. It holds A on model supply chain disclosure. Gradient Labs grades B for the honest version of the alternative: it discloses through a failover design that multiple external cloud and model suppliers are involved, which most vendors here avoid saying at all, without naming which ones or on what terms. Ask Gradient Labs for the list and the retention 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 this index has already recorded as the position across this lane rather than a failing of either vendor. The exposures differ and both are worth raising in contract. Gradient Labs takes actions, so ask what happens for a customer whose card was frozen in error or whose dispute the agent declined, and note that its outcome based pricing aligns incentives without creating a remedy. Omilia authenticates callers biometrically and moves money, so ask what happens when voice authentication wrongly rejects a legitimate customer, whether callers consent to biometric enrolment or can opt out, and how a caller reaches a human when containment is the measure being optimised. 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.
How does the AI FinTech Index grade Gradient Labs and Omilia?
Both are graded on the same fifteen capability axes, with every grade traceable to the public artifact it was read from and the date it was verified, and the index publishes no composite score. Omilia documents seven of the nine regulatory axes at A or B and Gradient Labs five, against an index average of 2.93 across 489 vendors. Gradient Labs holds A on AI centrality, operational evidence and core systems integration, with B on commercial transparency, institution coverage, AI safety, autonomy, regulatory status, model risk, security certifications and supply chain, and C on GLBA posture, governance and bias, deployment residency and liability. Omilia holds A on AI centrality, operational evidence, model risk management and model supply chain disclosure, with B on institution coverage, GLBA posture, autonomy, regulatory status, integration depth, deployment residency and security certifications, and C on commercial transparency, AI safety, governance and bias and liability.
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 both are better than the lane they sit in, in opposite directions. This index has recorded that conversational banking vendors generally 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, disclosing through a failover design that multiple external cloud and model suppliers sit in the path, which is more than most manage, and then not naming 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 grade C on liability and recourse, consistent with the finding already published for this lane, 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 enrolment or can opt out, and how someone reaches a person when containment is the metric being optimised. Both grade C on governance and bias, and Omilia's published word error rate is an average that says nothing about who sits at its tail.