AI companies sell trust before they sell software. A hallucinated answer, a biased result, a leaked training set, or a single viral failure case can freeze an enterprise pipeline that took eighteen months to build. The response you give in the first six hours decides whether you keep those deals.
The Problem
A single screenshot can override a year of trust-building
AI products fail in public and in ways that are easy to capture. A hallucinated medical answer, a chatbot that says something offensive, or a model that fabricates a citation becomes a screenshot that spreads through X and LinkedIn before your team has seen it. Unlike a backend outage that few people witness, a bad model output is self-documenting evidence anyone can reshare. The same accuracy story that won the deal now works against you, and the buyer who championed you internally is the one who has to explain the screenshot to their boss.
The technical buyer and the economic buyer hear the crisis differently
When something breaks, your ML lead wants to talk about eval scores, edge-case rates, and the fix in the pipeline. The economic buyer – a VP or a board – wants to know if the company is exposed and whether they made a bad bet. A crisis response written in model-evaluation language reassures the engineer and terrifies the executive who signed the contract. Most AI companies default to the technical framing because that is who runs incident response, and they lose the room that actually controls renewal.
Regulatory and accuracy claims you made in sales become liabilities
AI buyers are nervous about bias, data handling, and overstated accuracy, so your sales motion leaned hard on reassurance about exactly those things. When an incident hits one of them, every claim in your deck, your security questionnaire, and your marketing site is now evidence. A 99 percent accuracy claim looks like a lie the moment a customer hits the one percent in a high-stakes use case. The crisis is not only the failure – it is the gap between what you promised about safety and what just happened.
Silence reads as cover-up in a field already braced for it
The AI industry operates under constant scrutiny about safety, copyright, and whether the technology is being deployed responsibly. Audiences are primed to assume the worst when an AI company goes quiet after an incident. A delay that would be forgivable for a CRM vendor reads as evasion when the product is a model people already distrust. The instinct to wait until engineering fully understands the root cause is the instinct that turns a contained problem into a narrative about a company hiding what its AI does.
How We Help
We start before the crisis, because the AI companies that survive incidents are the ones that prepared for the failure modes their product actually has.
From there we build the response architecture before you need it. That means pre-drafted holding statements for the failure types most likely to surface – a hallucination in a regulated context, a bias finding, a training-data or output-leak question, a viral failure screenshot – each written in two registers so the technical buyer and the economic buyer both get an answer that lands.
When something live happens, we run the response. We separate what is known from what is being investigated, write the external statement and the direct enterprise-account outreach in parallel, and brief your founder or spokesperson so the message holds under questioning.
After the acute phase, we manage the recovery, which for AI is mostly about proving the fix is real to people who no longer take your word for it. We help you turn the incident into a credible change – a published eval, a guardrail, a process – that lets champions defend you internally again.
Measurement for crisis work is not impressions. It is whether the deals that froze unfroze, whether the narrative stayed contained instead of becoming the thing your category is known for, and whether your champions still champion you. We track which accounts re-engaged, how the story moved across the channels that matter to enterprise buyers, and whether your post-incident claims held up to the next round of scrutiny.
What we deliver
- An exposure map of your model's likely public failure modes and the claims that amplify them
- Pre-drafted holding statements written for both the technical buyer and the economic buyer
- A response chain – who speaks, who approves, how fast – built for the six-hour window
- Live incident response: external statement, enterprise-account outreach, and spokesperson briefing
- A recovery plan that converts the incident into a credible, provable change
An AI crisis is rarely the technical failure. It is the gap between the accuracy and safety you promised in the sale and what the model just did in public – and the fix has to close that gap, not paper over it.
Our Methodology
Crisis work for AI companies runs in two modes – readiness and live response – and the readiness work is what makes the live response survivable. We begin with an exposure audit: where your model can fail in public, what you have claimed about accuracy and safety, which enterprise accounts are most fragile, and how an incident currently moves through your org. From that we build pre-drafted statements, a response chain with named owners and approval speeds, and a spokesperson brief, so the first six hours are execution rather than improvisation.
When a live incident hits, we separate known facts from open questions, write the external and direct-to-account messages in parallel, and keep the founder on-message under pressure. The discipline is honesty without either over-explaining model internals or dismissing the failure as an edge case. The recovery phase turns the incident into a provable change – a guardrail, an eval, a process – because AI buyers stop taking your word and start needing evidence.
What makes this different from a PR agency is that we treat the technical-buyer and economic-buyer split, the accuracy-claim exposure, and the enterprise sales cycle as the core of the problem rather than chasing media coverage. An agency optimizes for sentiment in the press. We optimize for keeping the enterprise pipeline alive and keeping your champions able to defend you inside their own companies.
How We Work
There are two ways companies engage us. The first is readiness – a focused four-to-six-week build that produces the exposure map, the pre-drafted statements, the response chain, and a tabletop run-through against your most likely failure modes. The second is live response, where we step in during an active incident and run the first hours and the following weeks. Companies that did the readiness work first move dramatically faster when something real happens, because the playbook already exists.
On a live engagement, the first hours are about triage and containment – establishing what is actually known, drafting the holding statement, and reaching the enterprise accounts most at risk before they hear about it secondhand. The following days are about the substantive response and the spokesperson work. The following weeks are recovery: proving the fix, re-engaging paused deals, and rebuilding the credibility the incident dented.
Our team brings a communications lead who owns the message and a strategist who manages the enterprise-account and narrative side, working directly with your founder, your head of comms or marketing if you have one, and your ML lead for the technical ground truth. From your side we need fast access to what actually happened and the authority to approve statements quickly – a slow approval chain is the most common way AI companies lose the window. The cadence during a live event is hourly, then daily, then weekly as the situation stabilizes.
- Frozen enterprise deals re-engaged instead of lost
- A contained narrative that does not define your category
- Champions who can still defend you inside their own companies
- Post-incident claims that hold up under the next round of scrutiny
If your ai / machine learning company needs crisis communications leadership, we should talk.
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Frequently asked questions
How much does a crisis communications engagement cost for an AI company?
A readiness build – the exposure map, pre-drafted statements, response chain, and a tabletop exercise – typically runs in the $20K-$50K range depending on how many failure modes and enterprise relationships are in scope. Live response during an active incident is priced separately because the intensity and duration vary with the event.
How fast can you respond when an AI incident is already live and spreading?
If you have done the readiness work, we are executing a prepared playbook within the first hour, which is the difference between containment and a runaway narrative. If you are coming to us cold during a live incident, we move into triage immediately – establishing known facts, drafting the holding statement, and identifying the at-risk accounts – typically within the first few hours.
How does your team integrate with our engineering and incident-response process?
We sit alongside your existing incident response rather than replacing it. Your ML lead gives us the technical ground truth – what failed, what is known, what is still being investigated – and we translate that into messages for the technical buyer and the economic buyer without overclaiming or dismissing.
Why does an AI crisis need a different approach than a normal software incident?
A normal software outage is invisible to most people and forgotten in a week. An AI failure is self-documenting – a screenshot of a bad output is shareable evidence – and it lands in an industry already braced to distrust what these models do.
How do you measure whether the crisis response actually worked?
We track the things that affect the business: which paused enterprise deals re-engaged, whether the story stayed contained or became a label attached to your category, and whether your internal champions can still defend you. We watch how the narrative moves across the channels enterprise buyers actually read, not vanity impression counts.
Should we prepare a crisis plan before anything has gone wrong?
Yes, and for AI companies it is the single highest-leverage thing you can do, because the failure modes are predictable even if the timing is not. You already know your model can hallucinate, can surface bias, and can produce an output someone screenshots – so the statements for those scenarios can be written calmly in advance rather than at 2 a.m. mid-incident.
What kind of AI company is the right fit for this service?
Companies between Series A and growth stage, selling into enterprise, where a public model failure could freeze deals and dent hard-won trust get the most value. If your product makes consequential decisions, touches regulated data, or has made strong accuracy or safety claims in the sale, your exposure is high enough to justify readiness.
