What you made
We keep what holds up, decompose the business logic properly, and restructure the data model for scale. Monitoring and backups arrive before launch.
You built the app. We get it ready for real users.
We review AI-assisted prototypes, funded MVPs, and aging line-of-business systems, then handle the security, data, hosting, monitoring, and handoff work they need in production.
Prototype to production
Customer-facing apps
You keep the IP
A working demo is a start. Production adds another set of requirements.
AI-assisted tools make prototypes faster to build. Before customers depend on one, it still needs sound authentication, a durable data model, secure deployment, monitoring, and reliable payment handling where money is involved.
- Security pass Secrets come out of the code, dependencies get audited, and authentication gets done properly.
- Data model We check the schema, migrations, constraints, and indexes against expected use.
- Payments and billing Flows account for retries, duplicate requests, webhooks, refunds, and reconciliation.
- Hosting and cost controls Infrastructure is sized for current use with room to grow.
- Monitoring and backups Errors, uptime, and restores are checked before launch.
- Handoff or hosting Everything stays documented and yours. We can run it, or we can hand it over.
Four lanes of software work.
What does not exist
Custom software from the ground up. Registration systems, integrations, reporting pipelines, and the tools nobody sells off the shelf.
What is aging
Legacy systems are refactored in planned stages. We have replaced live payment flows without interrupting users.
About code refactoringWhat might be next
R&D and proof-of-concept work across systems, networking, and applications. For established clients, we regularly fund pilot projects ourselves so decisions rest on working results.
Where we use AI.
AI adoption
We find workflows where AI can save time, such as document handling and support triage, then test one use case before expanding.
Private AI
We design and operate private AI environments when contracts or data requirements rule out public services. For RocketDocs, briskData helped design the private inference environment and continues to support and manage it in production.
AI-built software
We work in AI-generated codebases every day. We keep the useful parts and replace what will not hold up under production use.
Data and reporting
Automated ingestion and daily KPI reporting, like the morning numbers a cancer treatment center now runs on. Leadership reads them because they can trust them.
Questions about the work.
Will you work with an AI-built codebase?
Yes. AI-assisted codebases are a regular part of our software work. We review them the same way we review any other application: keep what works, identify the risks, and make a practical plan.
Do I have to rewrite everything?
Usually not. The prototypes we take to production keep 40 to 60 percent of their original code. We preserve the parts that work, then fix the structure, data model, security, and infrastructure where needed. You see that list before work starts.
Can you run it after launch?
Yes. Most clients move onto a managed operations plan once the work ships, and we carry the pager from there.
What does it cost?
We price the work after reviewing the code. Projects are usually quoted in defined phases. Our fees have typically been 20 to 30 percent below comparable firms, but the quote depends on the system and scope.
Request a production review
Send a line about what it does, where it lives, and what happens if it fails. We read the code before we quote the work.
- You keep ownership: repo, infrastructure, and IP stay yours.
- Fixed scope, written in plain English.
- Best for customer-facing apps, internal systems, and funded MVPs.
- If a rewrite is unnecessary, we say so.
Best fit: an application headed for customers, employees, revenue, compliance review, or one already struggling with real users.