What does Resect AI's NeuroWave actually do?

NeuroWave inspects large language model internals during inference, not after generation is complete. It claims to detect patterns that precede hallucinations, intervene to modify model behavior, and preserve an audit trail. The suite includes vLLM integration, agent controls, and behavioral modification tools. It is currently available via waitlist and demo only.

How is runtime intervention different from output filtering?

Output filtering evaluates text after the model has already finished generating it. Resect AI's approach claims to identify a developing hallucination while the model is still computing the answer, allowing behavioral modification before the response is complete. Whether that claim holds across diverse enterprise models and workloads remains to be demonstrated in production.

What open-source evidence has Resect AI published?

Resect released two Apache 2.0 Veritas fact-checking models on Hugging Face: a 0.6B and an 8B model built on Qwen3. On LLM-AggreFact, the 0.6B scored 72.30% balanced accuracy versus 64.93% for base Qwen3. The 8B scored 75.47% versus 73.17% for Qwen3-8B. These are post-hoc fact-checking models, not the claimed in-stream intervention system.

Which industries does Resect AI target?

The company specifically names publishing, finance, healthcare, research, and education: sectors where a fluent fabrication can cause a compliance incident or an expensive operational error. It is not positioning as a general-purpose observability tool.

How big is the hallucination problem in production AI?

Stanford's 2026 AI Index found hallucination rates ranging from 22% to 94% across 26 models on one accuracy benchmark, with performance varying sharply by model and test design. Resect AI cites industry estimates that AI hallucinations cost businesses tens of billions of dollars globally per year.

Resect AI: $25M to Stop Hallucinations During Inference

On September 3, 2026, Resect AI emerged from stealth with $25 million in private equity funding and a claim that distinguishes it from the crowded AI observability market: its technology intervenes inside a running language model during inference, not after generation is complete. If that claim is validated in production, it represents a meaningful architectural shift in how enterprises can govern AI reliability.

The Problem Resect AI Is Attacking

Enterprise AI adoption has stalled at a specific choke point: hallucinations. Not the occasional quirky wrong answer, but the fluent, confident fabrication that reaches a financial analyst, a physician’s workflow, or a customer-facing chatbot. Stanford’s 2026 AI Index measured hallucination rates between 22% and 94% across 26 models on a single accuracy benchmark, with performance shifting sharply depending on model and test design. That range is wide enough to make the phrase “we tested it” nearly meaningless for most procurement decisions.

The European Commission began enforcing AI Act provisions on August 2, 2026, tightening governance requirements on high-risk AI applications. For sectors like finance and healthcare, that legal pressure has converged with technical pressure: neither “label it experimental” nor “add a human review step” is a sufficient answer at the output volumes AI-native enterprises are now running.

The result is a trust gap. Generative AI works well enough in controlled demos. It fails unpredictably in production at scale. The gap between those two states is where Resect AI is operating.

What Resect AI Is Building

Co-founders Kevin Owens (CEO), Tim Walton (Chief AI Officer), Tyler Gerber, and Tommy Lofgren describe their approach as opening the black box. Traditional monitoring products evaluate generated text, score it for factuality, and flag or discard bad outputs. Resect AI claims a different insertion point: the model’s internal decision-making process during inference.

The company’s planned NeuroWave Product Suite includes:

Real-time hallucination detection, targeting patterns inside model internals before generation completes
Behavioral modification tools to alter model behavior when a developing hallucination is detected
vLLM integration for organizations running open-weight models on their own infrastructure
Audit logs that preserve a record of model state and intervention for compliance teams
Agent controls for organizations where multiple AI systems operate autonomously

Owens describes the product as “a polygraph for neural networks” in materials from the official launch. That framing is useful: it positions NeuroWave as a source of evidence, not just a quality filter.

NeuroWave is currently available through a waitlist and demo request. The company has not disclosed pricing.

The Evidence Resect AI Has Published

The strongest verifiable evidence Resect AI has released publicly is not NeuroWave itself but two open-source Veritas fact-checking models. Both are released under Apache 2.0 on Hugging Face. Both are built on Qwen3 and operate as post-generation factuality assessors, not as the claimed in-stream intervention system.

Model	Benchmark	Resect Veritas Score	Base Qwen3 Score	Improvement
Veritas-0.6B	LLM-AggreFact	72.30%	64.93%	+7.4 points
Veritas-8B	LLM-AggreFact	75.47%	73.17%	+2.3 points

The 0.6B model’s improvement is more notable than the 8B’s. Both show that Resect’s team can train specialized factuality models that outperform general-purpose base models on a fact-checking benchmark. That is meaningful evidence of technical competence. It is not the same as demonstrating real-time intervention inside inference for a deployed enterprise model.

That gap between what Resect has shipped (open-weight fact-checkers) and what it has claimed (in-stream behavioral modification) is the central open question for enterprise buyers evaluating the company.

Why the Claim Matters If It Holds

Most enterprise AI governance products sit at the application layer. They wrap model calls, score outputs, route bad results to human review. That architecture works, but it has a ceiling: at high output volumes, human review queues fill faster than teams can clear them, and latency increases as outputs pass through an additional evaluation step.

Runtime intervention, if it works as described, would operate at a different layer. Rather than adding a second system that judges the first system’s output, it would modify the first system’s behavior in place. The audit trail would be generated inside the inference call itself, not as a side-channel evaluation.

For regulated industries where every AI-assisted decision may need to be documented, a native audit record rather than a bolted-on evaluation log is a significant procurement argument. It is also why Resect AI’s target markets (finance, healthcare, publishing) are the ones where compliance teams have the most direct authority over AI adoption decisions.

The Market Context

Resect AI enters a field that is filling quickly. Companies like Arize AI, Galileo, and Weights and Biases address AI observability and evaluation at the output layer. Agent security firms like Zenity, which raised $125 million in August 2026, address agent governance from a permissions and supply-chain angle. Resect AI’s insertion point, if the technology holds, is below both: inside the model’s generation process rather than around it.

Whether enterprises adopt a specialized in-stream intervention tool or wait for foundation model providers to build equivalent capabilities into their APIs is the competitive question. OpenAI, Anthropic, and Google are all investing in alignment and refusal improvements at the model training level. If those training-time improvements close the hallucination gap, the market for post-training runtime intervention narrows. If they do not, or if enterprises require vendor-neutral governance tools that work across model families, there is a durable buyer base for what Resect AI is describing.

The $25 million gives the team roughly 18 to 24 months of runway to move NeuroWave from a waitlisted product to reference customers in production environments. That is the window in which Resect AI needs to produce the evidence its current launch materials do not yet contain.

What Enterprise AI Teams Should Watch

The honest assessment for enterprise buyers today: Resect AI has published verifiable fact-checking models and a compelling technical thesis, but the core NeuroWave product is not yet available for independent evaluation. Three milestones would change that:

General availability of NeuroWave beyond the current waitlist, with documented pricing and integration guides
Independent benchmark results showing that in-stream intervention works across diverse model families in production, without degrading accurate responses
Reference customers willing to describe production deployments in quantifiable terms

For organizations that are already running AI in regulated environments and feeling the governance pressure of the AI Act or sector-specific compliance regimes, this is a category worth tracking. The problem Resect AI is addressing, the enterprise trust gap caused by unpredictable hallucinations, is real and growing.

For organizations where AI is still in pilot or internal tooling stages, the more immediate question is less about runtime intervention and more about the operational discipline that keeps AI confined to well-scoped tasks. We cover that readiness framework in our enterprise AI transformation maturity guide.

If you want to talk through your organization’s AI reliability and governance posture before the next compliance deadline, book a call with Enera.