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AI systems built around real data and real workflows.
I design and build retrieval, document, automation, and custom software systems from the data layer through production deployment.
What I build
AI knowledge systems
Private, source-grounded assistants and research tools built around your documents, data, and workflows.
For organizations with valuable internal knowledge that generic AI tools cannot represent safely or precisely.
Useful outcomes
- Make internal knowledge searchable and usable
- Return answers with inspectable source context
- Give teams one reliable interface for fragmented information
Typical system components
What I build
Search and retrieval infrastructure
Semantic, full-text, and hybrid search systems for large or multilingual collections.
For products whose users need to find the right passage, record, answer, or document—not merely a plausible result.
Useful outcomes
- Improve relevance across difficult document sets
- Support natural-language and exact-term discovery
- Build retrieval foundations that can grow with the corpus
Typical system components
What I build
Document intelligence
Ingestion systems that turn PDFs, scans, audio, and inconsistent source files into structured, searchable data.
For teams with a large archive or recurring document workflow that cannot be solved by manual processing.
Useful outcomes
- Convert archives into a usable digital corpus
- Automate repetitive extraction and processing work
- Preserve provenance from source file to indexed passage
Typical system components
What I build
Custom AI software
Purpose-built SaaS products, internal tools, and workflow automation around a specific operational problem.
For founders and teams who know the workflow they need to improve and want an engineer who can own the system end to end.
Useful outcomes
- Replace fragile manual processes with dependable software
- Connect AI capability to an actual user workflow
- Ship a maintainable product instead of a disconnected prototype
Typical system components
What I build
Backend and cloud systems
The databases, APIs, security controls, deployment, and operational foundations behind production software.
For teams that need the surrounding system—not only the model call—to be secure, observable, and maintainable.
Useful outcomes
- Move a prototype toward production reliability
- Design data and API boundaries for future growth
- Reduce infrastructure fragility and deployment friction
Typical system components
cat delivery.process
A practical path from uncertainty to production.
The shape changes by engagement, but the work stays anchored to the problem, the source data, and a verifiable definition of useful.
- 01
Define the problem
Clarify the users, source material, workflow, constraints, and what success must look like.
- 02
Design the system
Choose the retrieval, data, model, application, and infrastructure boundaries that fit the problem.
- 03
Build and verify
Implement the product in testable increments, evaluate quality, and keep source behavior inspectable.
- 04
Deploy and improve
Ship the system, monitor the workflow, and refine it against real use rather than demo assumptions.
./scope_project.sh
Start with the problem, not a solution label.
Tell me what people need to do, where the information lives, and what is currently slow, unreliable, or impossible.