Data engineering and pipeline architecture
Design, build, and operation of data pipelines and processing systems for large scale structured and semi structured data.
Government and public agencies
Public data work has to hold up when somebody checks it.
That is the standard I trained under. Nine years building assessment data infrastructure means shipping systems that get audited, published, and questioned by people whose job is to find the flaw.
Registration and business status
Stated as facts, for the contracting officer who needs them in one place.
- Legal name
- Melanin Tech & AI Solutions LLC
- UEI
- JL9UVZ66TND5
- CAGE code
- 21DU5
- SAM.gov registration
- Active through 06/04/2027
- Primary NAICS
- 541512, Computer Systems Design Services
- Place of business
- Oakland Park, FL 33334
- Single member Florida limited liability company
- Woman owned small business
- Minority owned, Black owned small business
- Registered in SAM.gov, active through 06/04/2027
Melanin Tech & AI Solutions LLC has not yet performed as a prime contractor on a federal contract. The experience described on this site is the owner’s professional experience, offered as evidence of technical capability.
Core competencies
Cloud infrastructure on AWS
Architecture, migration, and operation of cloud based data platforms, including infrastructure design, cost control, and production reliability. AWS is Amazon Web Services, the cloud platform much of government runs on.
Assessment and education data systems
Data architecture for large scale assessment programs, covering student and performance data, scoring workflows, and reporting.
Artificial intelligence and machine learning
Model development, evaluation of proposed AI use cases, and advisory support on responsible AI adoption in learning and operational settings.
Analytics, reporting, and data visualization
Dashboards, decision support reporting, and analytic products built for program and operational staff.
Learning systems and adaptive instruction
Design of adaptive learning architectures that integrate learner modeling, competency frameworks, and performance tracking.
How the work runs
Work is performed directly by the owner. There is no layered account management and no handoff between a proposal team and a delivery team.
Requirements before architecture
The first job is turning what a program office needs into something a system can actually be built against. Most trouble downstream starts as an unclear requirement upstream.
Build it so it can be audited
Public data work has to be traceable. That means documented pipelines, a record of what ran and when, and results somebody outside the project can reproduce.
Evaluate AI use cases honestly
Some proposed AI uses are a good fit. Some are not, and saying so early is cheaper for everyone. Evaluation covers what the data supports, what it does not, and where a model would be operating without evidence.
Hand over documentation, not dependence
Your team receives the architecture, the runbooks, and the reasoning. The goal is a system your staff can operate after the engagement closes.
Request a proposal
Send the requirement, the vehicle, and the timeline. You will get a written response stating scope, deliverables, schedule, and price.
Request a proposal