Snapshot
Continue to About ↓- What it does
- An AI agent platform that handles customer operations for financial institutions: frontline support across chat, voice, email, SMS and social, proactive outbound outreach such as collections and document collection, and back-office queues like disputes, KYB and claims.
More detail
- It runs on top of an existing support stack (Intercom, Zendesk, Freshdesk) or via API, and is purpose-built with finance-specific guardrails.
- Best for
- Financial institutions — banks, neobanks, lenders, payments firms and insurers — that want end-to-end automation of regulated support and back-office operations, especially those already on Intercom, Zendesk or Freshdesk.
More detail
- Teams willing to define procedures and connect system data to push automation from day-one basics toward 80–90% of handling time.
- Pricing
- Outcomes-based pricing: the company states you pay only for successful query resolutions, with no platform fees and no subscription.
More detail
- The price per resolution, any minimum commitment and volume tiers are not disclosed — the pricing page only invites a discussion, so the effective cost is unknown and
- Main limitation
- Teams wanting a co-pilot that assists human agents — Gradient Labs explicitly does not offer this.
More detail
- Also a poor fit for non-financial-services businesses, and for buyers who need a fully autonomous first line without accepting that the agent handles conversations end to end and reserves humans for approvals on sensitive decisions.
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- Structured profile ↗
About
- An AI agent platform that handles customer operations for financial institutions: frontline support across chat, voice, email, SMS and social, proactive outbound outreach such as collections and document collection, and back-office queues like disputes, KYB and claims.
- It runs on top of an existing support stack (Intercom, Zendesk, Freshdesk) or via API, and is purpose-built with finance-specific guardrails.
What it helps with
- Support cost and queue backlog in regulated finance, where generalist chatbots are seen as too risky and back-office wor
- Manual back-office queues such as suspicious activity, compliance exceptions, disputes and KYB, handled reactively rathe
- Low collections recovery from borrowers who are hard to reach at the right moment
- Slow disputes and chargeback cycles from intake through card-scheme filing
- Customer drop-off during onboarding and activation before first deposit
- One-shot outbound messages for document collection, payment failures and fraud alerts that customers cannot reply to
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| Frontline support agent | Yes | Handles conversations across phone, chat, email, SMS and social, asks follow-up questions to establish intent, and takes actions such as freezing a lost card or tracing a missing payment. |
| Finance-specific guardrails | Yes | Proprietary safety rules, policy checks and language controls stated to run on every turn of a conversation. |
| Procedural agent configuration | Yes | Agent behaviour defined in plain-language documents with no coding required. |
| Proactive outbound conversations | Yes | Two-way outreach triggered manually, by system alerts or by team workflows; use cases include document collection, payment failure, fraud alerts and activation nudges. |
| Back-office case handling | Yes | Investigates flagged issues, gathers evidence, applies the institution's rules and escalates with a complete case file for human review. |
| Human approval and audit trail | Yes | Approval requirements can be set for sensitive decisions, with review and control of agent actions. |
| Pre-deployment scenario testing and monitoring | Yes | Scenarios tested in a web app before going live; resolution rates and CSAT monitored in real time. |
| Collaborate version control and evals | Yes | Drafts, batch testing, AI-assisted procedure drafting and continual learning with proposed fixes for review; announced as a new product surface. |
| Multi-model backend | Yes | Not tied to a single LLM; company says it uses Anthropic, Google and OpenAI models with failover between providers. |
| Human agent co-pilot | No | Explicitly not offered; the company positions the agent as end-to-end automation rather than assistance for human agents. |
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Evidence
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- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- gradient-labs.ai
Technical profile details
Record ID
b4399dff-6544-450c-b6fc-cb1d0f79e90eCanonical URLhttps://profiles.scribble.network/listing/gradient-labs-b4399dff