AEVIDENCE · BEFORE · TRAFFIC
ABOUT ABLAZE
Make the next growth decision with evidence, not preference.
ABlaze is the pre-traffic experimentation layer for teams deciding which landing-page experience deserves real acquisition budget and engineering time.
Good experiments should begin before expensive traffic does.
Teams often spend acquisition budget or engineering time on a variant that has never been tested outside an internal review. The debate is usually subjective, the audience is absent, and the reasoning disappears once a decision is made.
ABlaze creates a practical step before the live A/B test: grounded personas navigate the real pages, attempt the same action across every arm, and leave an auditable trail of what helped, what created friction, and what deserves live traffic next.
WHERE IT HELPS
Use cases that begin with a consequential choice.
ABlaze is designed for teams that have real alternatives and need sharper directional evidence before committing budget, traffic, or development time.
Landing-page messagingCompare benefit framing, proof, hierarchy, and calls to action before buying traffic.Pricing and packagingTest how different offers communicate value, trust, and perceived friction to target buyers.Signup journeysFind where onboarding copy, forms, or action paths cause hesitation and abandonment.Campaign destinationsCheck whether a page fulfils the promise made by an ad, email, launch, or audience segment.
THE ABLAZE BUILD
Not a concept deck. A working, inspectable system.
Created during the Hermes Buildathon sponsored by GrowthX, ABlaze connects audience research, dynamic agent roles, real browser execution, durable recovery, evidence calibration, customer workspaces, payments, exports, and operational monitoring. The product’s limits are stated as clearly as its capabilities: ABlaze provides directional pre-traffic evidence, not a causal conversion claim.
Real surfacesLive URLs and browser actionsStored evidenceRuns, screenshots, friction and latencyCalibrated callsLead, winner, or honest inconclusive resultOperational productAuth, billing, recovery and observability
APPLIED AI / MLBuilding AI systems people can inspect, trust, and use.
Anish Posim Reddy is passionate about applied AI, experimentation, and turning ambiguous questions into systems that can be tested, inspected, and improved.
Through public projects, open-source work, and builder communities, he explores how AI can make evidence more practical and auditable. His UC Irvine Master’s in Data Science adds a foundation in validation and uncertainty to that work.
Independent, deeply prepared, open to feedback, and quick to turn suggestions into working improvements.
A concise paraphrase of the public recommendation on Anish’s LinkedIn profile from his Machine Learning Engineering mentor.
See the evidence trail for yourself.
Run the free community study or inspect the guided replay before deciding whether ABlaze earns your trust.