Ship, hold, scope, or add controls.
A simulation does not end at a vague score. It ends with a launch posture tied to the proposed agent, tool permission, policy threshold, or workflow change.
De-risk AI agents before launch.
Astraform is an AI launch risk simulator for regulated teams. Test one proposed customer-facing agent against synthetic hardship, dispute, fraud, and support paths, then leave review with a launch posture and replayable evidence.
First wedge: regulated banking and high-stakes support workflows where one unsafe agent path can create customer harm, policy exposure, or launch delay.
Regulated launch decisions
Customer harm before release
Policy and tool-risk traces
Evidence for review boards
Focused wedge
The first buyer is not everyone using AI.
Astraform is built first for regulated teams putting agents into customer-impacting decisions: hardship, disputes, fraud escalation, support automation, and policy changes. That is where an eval score is not enough and a replayable launch report matters.
Before launch
AI hardship agent denies vulnerable customers
Replay stressed personas, residual hardship, escalation gates, and policy-owner evidence before approval.
Before launch
AI support agent escalates fraud incorrectly
Expose false positives, customer restrictions, tool calls, and the workflow owner responsible for the control.
Before launch
AI workflow triggers a policy breach
Compare baseline versus candidate behavior and hand reviewers the launch posture, not another dashboard.
Decision artifact
Give the review board an answer, not a dashboard.
Each run ends with a launch posture: ship, hold, scope, or add controls, with affected cohorts, replay paths, and system owners visible.
Show who is helped, harmed, or missed.
Synthetic cohorts make customer consequence visible before real people absorb the failure. The report names affected groups, decision gaps, and scenarios that still need control.
Every claim links back to a replay.
Reviewers can trace the customer profile, agent action, tool call, domain response, side effect, and recommendation without trusting a screenshot or summary alone.
The owner of the failure stays visible.
Astraform runs the simulation and evidence layer. Your domain systems keep business state, policy logic, APIs, side effects, and the accountability that comes with them.
Urgent buyers
Built for teams whose AI launches can hurt customers.
How it works
Simulate the launch before customers feel it.
Every run connects a customer cohort, proposed AI change, system boundary, and evidence report so reviewers know what is safe to ship.
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01
Customer cohort
Controlled customers with goals, constraints, risk profiles, and success criteria.
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02
Proposed AI change
Agent change, policy threshold, tool permission, escalation rule, or workflow automation.
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03
System boundary
Existing agents, APIs, policies, workflows, and domain-owned business state.
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04
Evidence report
Replay links, side-effect ledgers, affected subjects, and report-ready recommendations.
Replay evidence
Every failure path gets a replay.
Astraform follows synthetic customers through agents, workflows, tools, domain systems, and side effects, then packages the path into evidence a reviewer can replay.
Control boundary
Astraform tests the consequence. Your systems keep the business logic.
The platform is not a replacement banking core, CRM, policy engine, or workflow database. It runs the simulation and evidence layer around the business systems a team already trusts.
Astraform owns
- Synthetic customer agents and cohorts
- Simulated time and scenario orchestration
- Baseline-versus-proposed comparison
- Replay evidence and side-effect ledgers
- Outcome reports for launch decisions
your APIs, policies, state, and side effects stay domain-owned
Your domain owns
- Business state and persistence
- Policy engines and rule logic
- APIs, tools, and workflow behavior
- Transaction or case semantics
- Domain-specific evidence projections
Banking proof run
Proof has to look like a launch review artifact.
Astraform's current banking proof run exercises a hardship-threshold launch decision across 100 generated customers and 30 simulated days. The report names the launch posture, affected cohorts, warning gates, and replay evidence.
The point is not benchmark theater. It is evidence hygiene: what changed, who was still at risk, what gate fired, and which business system owns the next control.
DecisionHardship threshold
Cohort100 customers
Horizon30 simulated days
PostureWATCH / control required
EvidenceReplay + cohort export
Partner Preview
Bring one risky AI launch decision.
We are working with a small number of early partners close to a real AI launch decision. Bring the proposed change, the system boundary, and the evidence target. Astraform helps turn the run into replayable customer outcome evidence before production.