What if your problem doesn’t fit one of our proprietary systems?
Then we build it for you.
When your problem doesn’t fit one of our proprietary systems, Trinzik builds it: custom AI automation and bespoke research and content tools, built on the same principles our proprietary systems run on.
Trinzik’s proprietary systems are those principles packaged for a common need. A custom build takes that same measurement-first, citation-grounded, anti-fabrication discipline and shapes it to a problem only you have. Same standard, built to your surface.
Seven phases
The same proven process. A different outcome every time.
These seven phases run on every engagement, in the same order, from discovery to support. What changes is the work inside them: your requirements, scope, and outcomes are shaped by what your project actually needs.
Discovery
We learn your business before we propose anything.
Every engagement starts here. We conduct deep-dive sessions with your team to understand your workflows, technology stack, business objectives, and constraints. Discovery is not a formality, it is the foundation that shapes everything we build. We ask the questions that reveal what you actually need, which is often different from what you think you need.
Key activities
- Stakeholder interviews and workshops
- Existing infrastructure audit
- Workflow and process mapping
- Goal alignment and success criteria definition
Services
End-to-end AI, from strategy to production.
Every engagement is tailored to your business. We don’t sell packages or ship off-the-shelf deliverables; we scope the work around what you actually need.
Strategize & plan
AI Strategy & Consulting
Identify where AI creates real value in your business, and build the roadmap to get there.
- AI readiness assessment & scoring
- Workflow & process audit
- Technology stack evaluation
- Prioritized implementation roadmap
Build & deploy
Custom AI Development
AI systems engineered for your specific use case, built to run in your infrastructure.
- Model selection & configuration
- Data pipeline design & implementation
- System integration into existing infrastructure
- Production API development
Design & develop
Full-Stack Web & SaaS
Websites, web applications, and SaaS platforms, designed and built from the ground up.
- UI/UX design & prototyping
- Frontend development with React & Next.js
- Backend API & authentication systems
- Database architecture & cloud deployment
Design
Frontend
export default function App() {
const data = useSWR()
return <Grid cols={3} />
}Backend
- GET/metrics200
- POST/users201
- PUT/config200
Data
Automate & optimize
Workflow Automation
Turn repetitive, manual processes into automated workflows that run on their own.
- Process audit & opportunity identification
- AI-powered decision logic integration
- Integration with CRM, ERP & existing tools
- Monitoring dashboards & alerting
Architect & integrate
Data Engineering & Infrastructure
Clean, structured, reliable data systems that power everything else.
- Data architecture design
- ETL/ELT pipeline development
- Data warehouse & lake implementation
- API integration layer development
Monitor & maintain
Ongoing Support & Maintenance
Your systems stay running, updated, and improving, long after launch.
- System monitoring & uptime management
- Incident response & resolution
- Model performance monitoring & retraining
- Security patches & dependency updates
Monitoring
99.97% uptime
Incidents
Response & resolution
- Memory thresholdmitigating
- DB failoverresolved
- SSL renewalresolved
Model health
Performance & retraining
Security
Patches & dependencies
11 / 12 current
Does it work in practice?
Yes. A mid-sized legal services provider cut manual UCC processing time by 85%.
Their paralegals were losing 20+ hours a week to extracting, reviewing, and classifying hundreds of UCC filings out of a state database by hand. As the volume rose, error rates climbed with it, and real legal work kept getting pushed aside for data entry.
We built an end-to-end pipeline that does the reading and routes the judgment calls to a person. Python pulls the filings and Claude classifies them, with Sonnet handling the standard ones and Opus the complex edge cases. A custom review interface keeps a paralegal in the loop on every result, and the whole thing runs inside the firm’s own Azure environment.
96.2%
classification accuracy on standard filings
45s
per filing, down from 12 minutes
0
compliance incidents in the first three months
Delivered in six weeks across four phases of custom AI development, data engineering, and workflow automation, built with Python, Claude (Sonnet and Opus), Azure, and a custom review interface.
Where to next?
A custom build rests on the same principles as the rest of Trinzik. The technology page covers what keeps them honest, and the flagship shows them in their most complete form.
Bring us the problem that doesn’t fit.
A walkthrough starts from your actual workflow and shows where a custom build would take it, on the same principles as everything else we make.