Clarity before code

For founders who need to decide what to build, what to cut, and what can wait.

Includes

• ·MVP direction
• ·User problem mapping
• ·Feature prioritization
• ·Validation roadmap

Best for: Pre-seed founders, solo founders, and teams before they commit to development.

Output: A clear AI MVP direction your team can design, build, or pitch with confidence.

For founders who need the product to feel clear, usable, and credible before development.

Includes

• ·Product flows
• ·AI interaction design
• ·Dashboard UX
• ·Clickable prototype

Best for: AI tools, internal platforms, customer portals, copilots, dashboards, and AI-assisted workflows.

Output: A clean product experience ready for development, testing, and demo conversations.

For founders who want a real AI product using ready model APIs without training their own model.

Includes

• ·AI API integration
• ·Prompt design
• ·Structured outputs
• ·MVP frontend/backend

Best for: AI assistants, generators, analysis tools, recommendation helpers, summarizers, and early AI product experiments.

Output: A working custom AI app that proves the core product value quickly.

For founders who need AI answers grounded in their own documents, policies, content, or knowledge base.

Includes

• ·Document preparation
• ·Retrieval setup
• ·Grounded answer flows
• ·Content update process

Best for: Document-heavy products, internal knowledge tools, customer support, compliance workflows, training platforms, and research assistants.

Output: An AI knowledge MVP that answers using your own content instead of relying only on generic model knowledge.

For founders and teams who want AI to save time inside a real business process.

Includes

• ·Multi-step workflows
• ·Human review points
• ·Lightweight integrations
• ·Routing logic

Best for: Operations, sales, customer support, recruiting, research, reporting, and back-office workflows.

Output: A working AI workflow MVP that reduces manual work while keeping human judgment where it matters.

For teams with proprietary data, repeated use cases, and a proven workflow that needs better consistency.

Includes

• ·Readiness assessment
• ·Training-data review
• ·Quality benchmarks
• ·Model improvement roadmap

Best for: Teams with enough real examples, repeated tasks, domain-specific language, or quality requirements that a prompted model cannot reliably meet.

Output: A practical custom-model plan or prototype that improves consistency only when the data and use case justify it.

For complex workflows where AI needs to plan, check, route, or complete tasks across multiple steps.

Includes

• ·Agent workflow design
• ·Tool/action permissions
• ·Human approval checkpoints
• ·Failure and fallback paths

Best for: Complex research, operations, internal tools, multi-step analysis, document processing, and workflows where one AI step is not enough.

Output: A controlled autonomous-agent prototype that shows where agents add value without making the MVP fragile or unpredictable.