Melaninternet Pitch Deck

1 Expand meaningful AI participation

Pew: Black and Hispanic workers are more concentrated in occupations with lower exposure to AI. Melaninternet targets deeper workplace use, advanced skills, ownership, and enterprise participation.

2 Expand founder participation

Crunchbase: companies with a Black founder or co-founder received only about 0.32% of U.S. venture funding in 2025. Melaninternet supports grants, pilots, partnerships, and scalable commercialization.

3 Reduce harmful AI bias

Make uncertainty, data limitations, and failure modes visible through testing, documentation, source grounding, and evaluation.

4 Promote practical AI ethics

Truthfulness, privacy, grounding, evaluation, oversight, security boundaries, and honest capability limits.

Reliability gap

LLMs can hallucinate, stall, or return inconsistent answers. Teams need repeatable behavior, not one-off impressive demos.

Grounding gap

Document-heavy work requires answers tied to source material, clear boundaries, and traceable evidence paths.

Governance gap

Enterprise and health-related work require privacy, observability, permissions, auditability, and human oversight.

Chat interface

• Mobile-first prompt experience
• General question answering
• Business-analysis prompts
• Code/demo paths
• Session reset and guided demo flow

Document intelligence

• PDF/TXT/Markdown analysis
• Source-grounded questions
• Complex material summarization
• Operational-risk extraction

Reliability layer

• Health endpoint
• Model check path
• Versioned constitution
• Test prompt suite
• Persistent telemetry
• Automated reliability gate

automated production checks passed, 0 failures

synthetic-user cases passed

tolerated provider warnings; every check passed outright

document grounding, citation, and cross-session isolation

Privacy boundaries

Anonymous guest telemetry stays separated from intentional opt-in contact information.

Reliability harness

Deployment verification, regression, live model checks, synthetic users, concurrency, document proof, and persisted PASS/FAIL reports.

Correct boundary

Defined MVP tests are not a claim of security certification, regulatory compliance, or production SLA.

1 Experience

Mobile-first chat, guided demos, forms, document upload

2 Application

FastAPI routing, sessions, files, workflow logic

3 Policy

Constitution, scope limits, document grounding rules

4 Model

NVIDIA Nemotron now; future model routing

5 Persistence

PostgreSQL telemetry, sessions, leads, evaluations

6 Evaluation

Synthetic users, regression, source checks, release reports

Enterprise workspace

Accounts, roles, workspaces, saved preferences, document controls, usage analytics, and admin settings.

Governed agents

Agents that analyze, draft, route, and recommend while inheriting policy, permissions, evaluation, and human approval gates.

Workflow integrations

Analysis → validation → structured output → human approval → audit log and ROI evidence.

Financial operations

Invoice review, AP exceptions, vendor docs, reconciliations, policy review, and reporting workflows.

Knowledge operations

Internal knowledge assistants, policy Q&A, admin document analysis, and team workflow templates.

Enterprise boundary

The LLM should advise and orchestrate; deterministic systems remain responsible for final records and transactions.

Research intelligence

• Literature and document synthesis
• Target / biomarker hypothesis mapping
• Experiment-plan summarization
• Research-question tracking

Clinical / study documents

• Protocol Q&A and extraction
• Inclusion/exclusion criteria review
• Study operations summaries
• Evidence-gap flagging

Health operations

• Policy and compliance review
• Prior-auth/admin documents
• Knowledge-base assistants
• Workflow evidence reports

1 Usage signals

Prompts, tasks, documents, clicks

2 Outcomes

Success, failure, user feedback

3 Evaluation sets

Curated cases and rubrics

4 Regression gate

Protect against regressions

5 RAG / policy updates

Knowledge and rules improve

6 Better workflows

More value, more usage

Why it belongs

Physical AI requires a model of environment, state, constraints, permitted actions, uncertainty, and consequences.

Required R&D

Perception, sensors, real-time control, hardware integration, cybersecurity, sandboxing, rollback, and task-specific validation.

Governance carryover

Constitution, permissions, telemetry, evaluation traces, audit logs, and human approval gates become safety infrastructure.

TAM: enterprise AI workflow layer

SAM: initial US operations buyers

SOM: 3-year practical target

1 Paid pilots

Scoped workflow pilots around document intelligence, analysis, financial operations, health operations, or research-admin use cases.

2 SaaS plans

Professional and business subscriptions for individual users, teams, and workspaces.

3 Enterprise usage

Pricing scales by users, document volume, model consumption, workflow complexity, security, integrations, and support.

Target users

Operations, finance, compliance, admin, knowledge-work, research, and health-operations teams handling document-heavy workflows.

Acquisition channels

Direct outreach, LinkedIn messages, NJ innovation ecosystem, accelerator programs, funder referrals, demos, and partnerships.

Conversion system

Early Access, Business Pilot, Design Partner. Capture use case, role, email, and timing.

Advertising path

Use paid ads after forms and analytics work; measure visitors, demos, leads, pilot requests, and conversion cost.

General AI assistants

Broad utility and strong models, but workflow reliability and internal proof systems are still emerging.

Document tools

Good retrieval and extraction, but not always built as a broader governed AI work layer.

Automation platforms

Strong workflow automation, but LLM-heavy tasks still need reliability and evaluation systems.

Status quo

Manual work, spreadsheets, email, and one-off prompts remain the main competitor in many workflows.

pre-seed funding goal

Use of funds

• Product reliability + evaluation
• Technical hiring/contracting
• Cloud/model inference
• Security/documentation
• Pilot execution + GTM

18–24 month outcomes

• Stable reliability harness
• 3+ paid pilots/customer proofs
• Repeatable ICP + pricing
• Enterprise workspace prototype
• Pre-seed/seed-ready evidence

Operator, not observer

Accounts payable professional in healthcare operations. Hands-on in invoice review, AP exceptions, vendor documents, reconciliations, and policy review.

Builder

Sole designer and engineer of Melaninternet.Volume_1: FastAPI, PostgreSQL, NVIDIA Nemotron 3 Super with automatic failover, document grounding, and an automated reliability gate that must pass before a release ships.

Domain fit, and building the team

The first commercial wedge is the work I do every day, so the product is designed from inside the workflow it automates. Recruiting a technical lead in NLP evaluation and calibrated verification; receiving SBIR coaching through NJSBDC. B.A. Psychology, Rowan University. Melaninternet LLC registered in NJ since 2021.

Jordan Powell

Founder, Melaninternet LLC
Melaninternet.Volume_1
Melaninternet.ai
jordan@melaninternet.ai

REASON · RESEARCH · CREATE · ELEVATE