01
PROBLEM
AI speeds up development. It does not prove trust.
Founders can reach launch or funding before they can explain what is reliable, what is risky or what remains unknown.
Trust Before Ship independently examines AI-built applications and turns technical evidence into a decision founders, funders and buyers can defend.
We combine automated analysis, human interpretation, targeted business-logic checks and clearly declared limits.
Trust Before Ship is validating a new category of independent assurance for software created with AI.
Founders can reach launch or funding before they can explain what is reliable, what is risky or what remains unknown.
A read-only forensic audit combining automated analysis, human interpretation and targeted business-logic checks.
We record the scope, locate findings, interpret the evidence and state clearly what the review cannot confirm.
Six applications have been examined, together with a controlled experiment testing critical business promises.
The immediate milestone is ten independent paying customers and a clearer repeatable ideal-customer profile.
Build secure intake, repeatable audit orchestration, evidence reporting, retesting and partner distribution.
The method has early evidence, but commercial demand is still being validated. The next proof is paying customers, repeatable delivery and evidence of willingness to pay.
More software is being created faster, often with less visibility into the code, decisions and risks underneath it.
Founders need more than a working demo before launching, raising, handing over or committing more money.
Investors and acquirers need a clearer technical position before relying on software they did not build.
Agencies, developers and technical partners need evidence about what was examined, what needs repair and what remains outside the review.
Trust Before Ship is building the assurance layer between AI-generated code and the people being asked to depend on it.
Trust Before Ship combines multiple evidence sources into a recorded technical position that can support a real decision.
The supplied application, access method, evidence sources and exclusions are documented before conclusions are made.
Findings are connected to the relevant files, behaviours or business obligations instead of being left as raw output.
Important product promises can be checked alongside code quality, security candidates and structural signals.
The evidence pack states what the review can support, what remains unknown and where further testing may be required.
The moat is not ownership of a single scanning tool. It is the growing assurance system built around how evidence is collected, interpreted, compared and turned into decisions.
A consistent review structure across different AI-assisted applications.
Patterns and comparisons become stronger as more audits are completed.
Findings are translated into proceed, fix, rebuild or retest decisions.
Secure intake, orchestration and reporting can become software-assisted over time.
Over time, Trust Before Ship can develop a stronger evidence base, clearer risk patterns and a more efficient assurance workflow—without pretending that software can ever be proven defect-free.
The service proves demand and develops the method. Software then makes evidence handling, delivery and retesting more repeatable.
Complete ten independent paid audits, refine the ideal customer and confirm which decisions create the strongest demand.
Build secure intake, evidence capture, audit orchestration, report assembly and controlled retesting around the proven method.
Serve agencies, investors, accelerators, acquirers and development partners through repeatable reviews, licensing and partner workflows.
Trust Before Ship scales by standardising evidence handling while preserving human interpretation where context, materiality and business promises matter.
The opportunity is to make independent assurance more repeatable and accessible without reducing it to another automated score.
Trust Before Ship grew from a practical question: what evidence should exist before people are asked to trust AI-built software?
Mohan combines industrial engineering, software project management and decades of practical web-development experience.
His background is centred on systems, process, failure points and the difference between something appearing to work and being safe to depend on.
The audit considers how technical evidence, business promises and operational risks connect.
The method is shaped by real development, deployment, client and handover environments.
Tool output is treated as evidence to interpret, not as automatic proof of safety or correctness.
The immediate focus is paid customer validation, repeatable delivery and disciplined productisation.
Trust Before Ship is not built around a promise that one tool can solve assurance. It is built around evidence, process, judgement and honest limits.
The immediate goal is not growth at any cost. It is to validate demand, win the first ten paying customers and productise only what proves repeatable.
Introductions to founders, agencies and investors responsible for AI-built applications.
Support refining the offer, ideal customer, go-to-market model and willingness to pay.
Guidance building secure intake, repeatable delivery and a software-assisted assurance platform.
That evidence will determine the right product, channel, funding requirement and path to scale.