InfiniteWatch raises fresh capital to police AI agents that talk to customers

Start-up secures $4M to tackle a new front in customer AI

InfiniteWatch said it raised $4 million in a pre-seed round led by Base10 Partners to build tools that monitor and control AI agents that handle customer conversations. The funding positions the young company as a specialist in what its founders call “agentic customer interaction intelligence” — software that tracks, audits and reroutes autonomous AI assistants when they speak with customers by voice or through web chat.

The pitch is straightforward: companies are moving faster to let AI handle routine support and sales tasks, but those AIs act on their own and can make unexpected choices. InfiniteWatch wants to be the layer that watches those choices in real time, flags risky behavior and keeps a usable record for compliance and quality control. For companies worried about reputation, privacy and legal exposure, that promise is likely to resonate.

How the product watches and tames AI agents in real interactions

At its core, InfiniteWatch is building an observability platform for autonomous agents — software that acts like a safety net for other software. It captures the inputs and outputs of agent conversations across channels: phone calls, voice assistants, web chat widgets and embedded voice on sites. The company says it records not just the words exchanged, but the sequence of internal decisions the agent made, which developers call “agent traces.” That gives teams a way to replay what the agent thought and why it answered a certain way.

Technically, the product mixes three pieces people care about: live monitoring, policy controls, and APIs. Live monitoring shows supervisors what agents are doing in real time and surfaces risky patterns. Policy controls let businesses enforce guardrails — for example, stopping an agent from giving medical advice or from sharing a customer’s payment data. And APIs let companies hook the observability stream into their ticketing, CRM and analytics tools.

Two differentiators InfiniteWatch emphasizes are multi-channel coverage and developer-friendly integrations. Many legacy conversation tools focus on chat or phone recordings. InfiniteWatch aims to stitch together voice transcription, intent logs and the agent’s internal state so teams can see cross‑channel behavior in a single timeline. For developers, the company is promising lightweight SDKs and webhooks so agent frameworks can be instrumented without a big engineering lift.

Early use cases are predictable but practical: contact centers running AI assistants that need human handoff rules; fintech firms that must log decision trails for regulations; and ecommerce sites that want to keep automated sales assistants from making false claims. The company also pitches itself as useful for QA teams that need deterministic ways to reproduce agent failures and for compliance teams that must prepare audit-ready logs.

Where InfiniteWatch fits in the stack and who it will compete with

The market it targets sits between several adjacent categories. One neighbor is conversation analytics — firms that analyze recorded calls and chats for quality and coaching. Another is agent orchestration and workflow platforms that coordinate handoffs between AI and humans. A third is the newer field of AIOps for model governance and observability inside ML pipelines.

That position gives InfiniteWatch an opportunity: many firms lack tools that explicitly connect agent behavior to compliance and real-time control. Enterprises with regulated lines of business, large contact centers, or heavy dependence on automated web assistants are natural customers. The product could also appeal to startups that build agents for multiple clients and want to provide a managed safety layer.

Competition will come from a mix of established players and new specialists. Conversation-analytics vendors may broaden their offerings to include agent-state capture. Cloud providers and larger orchestration platforms could add observability hooks as a bundled feature. And a swathe of startups focused on model governance and agent platforms could either partner with or try to replace InfiniteWatch’s approach. The challenge for InfiniteWatch is to prove that its real-time controls and traceability are both reliable and low-friction enough for busy operations teams to adopt.

What the pre-seed tells investors about traction and risk

Base10 leading a $4 million pre-seed is a signal that professional VCs see a real problem to solve and believe in the founding team. At pre-seed size, the money will mostly go to product development and a small go-to-market push: building integrations with popular agent frameworks, hiring engineers and landing pilot customers in contact center or regulated industries.

For investors, the round implies early validation rather than product-market fit. It’s a classic high-upside, high-risk stage: success depends on getting a few enterprise pilots to stick and proving the product scales across channels while preserving privacy and performance. The cap table setup and the lead investor suggest InfiniteWatch is building a company meant to raise a larger seed or Series A, and its acquirers would likely be cloud vendors, contact-center software firms, or governance platforms looking to bolt on agent observability.

Milestones to watch and the main risks ahead

In the next 12 months investors and customers should watch four things. First, pilot launches with paying customers. Early revenue or multi-month pilots show the product solves a real operational pain. Second, integrations with major agent frameworks and contact-center stacks. The smoother those integrations, the lower the adoption friction. Third, the company’s privacy and logging posture: capturing rich trace data is valuable, but it must be designed to avoid unnecessary retention of sensitive customer data. Fourth, latency and reliability under load — if the observability layer slows or drops data, it becomes a liability, not a guardrail.

Two downside scenarios are straightforward. If larger incumbents treat agent observability as a standard feature and bundle it with their platforms, InfiniteWatch will need either deep technical differentiation or strong channel partnerships to stay independent. Alternatively, if the product is seen as too intrusive or costly to implement, adoption could stall at pilot stage. On the upside, proving success at a handful of regulated customers could make the company an attractive target for enterprise software buyers and create a clear path to scale.

Overall, InfiniteWatch is attacking a timely problem with a focused product and a sensible investor backer. The idea is sensible; execution and enterprise traction will decide whether it becomes a core piece of the AI customer stack or a niche tool for only the most cautious operators.

Sources

• prnewswire.com