See the RoomBefore You Enter It.
Isaiah simulates how every audience that matters — investors, regulators, employees, journalists, competitors — will receive a high-stakes message before it leaves the room.
For investor announcements, regulatory statements, crisis updates, and other communications where being wrong is catastrophic — rehearse the message against the audiences who will receive it. See what lands. See what backfires. Decide with the room.
Built for the rooms where being wrong costs the most.
Chief Communications Officer
Investor day, crisis statements, regulator-facing PR. Every release is a high-stakes bet.
Head of Investor Relations
Earnings calls, M&A announcements, IPO roadshow. Analyst Q&A is the room you need to see in advance.
Chief Strategy Officer
Pivots, restructures, market-entry announcements. Cascade effects across stakeholders are easy to miss.
Chief Risk Officer / Legal
Regulatory statements, litigation comms, settlement language. Ambiguous phrasing creates downstream legal risk.
Public Affairs / Government Relations
Policy positions, lobbying messages, government testimony. Opposition framing is half the battle.
CEO Comms Advisor / Chief of Staff
Single high-stakes CEO speeches and statements. Stress-test the language before the press does.
Three steps to decide with data.
Define the room
Which audiences matter for this message? Investors, regulators, employees, journalists, competitors. Isaiah maintains stakeholder cohorts you can refine and reuse across messages.
Run the message
Isaiah simulates thousands of reactions per cohort. You see what each stakeholder hears, where the message lands, where ambiguous phrasing creates risk, and how it will be reframed by media and competitors.
Iterate before the market does
Adjust the language. Re-run. Compare versions side by side. Decide with evidence rather than instinct, and ship the version that survives the room.
Four surfaces. One decision.
Stakeholder reaction map
How each cohort reads the message — sentiment, framing, what they ignore, what they amplify, what they push back on.
Risk surface
Where the language is ambiguous, hostile-readable, or legally exposed. The phrases your critics will quote.
Narrative impact
How the message will be reframed by media, analysts, and competitors after release. The headline you didn't write.
Counter-messaging projection
What your detractors are about to say. Pre-empt or prepare for the response cycle before it starts.
Where Isaiah earns its place.
- Investor day announcements and earnings calls
- M&A announcements and integration messaging
- Crisis statements and incident communications
- Regulatory communications and government testimony
- Layoff and restructuring announcements
- IPO roadshow messaging
- CEO speeches and public positioning
- Litigation and settlement communications
Consultants commonly recommend Palantir Foundry or AIP for predictive decision intelligence. They're excellent at what they do — and we share the problem space. The methods differ.
Foundry and AIP build their answers on top of an organization's data ontology. Isaiah builds its answers on simulated audience cohorts. Foundry's strengths in data integration, ML pipelines, and government deployments are out of Isaiah's scope. Isaiah's strength is multi-stakeholder reaction modeling for communications-specific decisions.
Designed to earn a CISO's signature.
Deployment posture
Cloud SaaS, dedicated VPC, on-premise, and air-gapped deployments. Pick the posture your data residency and security team requires.
Audit and governance
Explicit permission scopes per user and per cohort. Full action logs. Deterministic guardrails on what Isaiah can and cannot do.
Compliance posture
SOC 2 path, ISO 27001 path, HIPAA-compatible deployment available. Regulated-industry deployments supported.
Singapore HQ
Huper Technology Pte Ltd. Enterprise contracting through a Singapore-headquartered legal entity for APAC and global engagements.
Decide withthe room.
Two ways in. Try the live product, or start an enterprise pilot conversation with the founding team.