Discovery + Design + Build + Validate.
Discovery
- Scalability
- Flexibility
Technology talent that joins distributed teams and works across compatible dynamics and time zones.
Product
- Scalability
- Flexibility
Multidisciplinary teams focused on your product, project or roadmap.
Engineering
- Scalability
- Flexibility
Process automation combining rules, workflows and Artificial Intelligence.
AI
- Scalability
- Flexibility
Applications and features using Generative AI, LLMs and intelligent capabilities.
Validation
- Scalability
- Flexibility
Intelligent document processing to extract, classify, interpret and transform information.
DiscoverWe understand the problem, opportunity and goals.
Three fronts converge into a single hypothesis
Manual work
Repetitive tasks that consume hours of the team’s time.
Bottleneck
Waiting and handoffs between teams without traceability.
Scattered data
Information in silos, without a single source.
Automation opportunity
Prioritized use case · Impact / effort
DesignWe turn the idea into a concrete experience and solution.
From a hypothesis to something people can see and navigate
Idea
Value hypothesis
User flow
The complete journey, step by step.
Key screens
Only those needed to test the hypothesis.
Prototype
Navigable, ready to present and discuss.
Success criteria
What we will measure once users have it.
Concrete solution
EngineerWe validate technical feasibility and define the architecture.
Technical risks first. Architecture second.
Technical decision
Layer 01
Stack
Technologies chosen for delivery speed.
Layer 02
Architecture
Services, data and pilot-scope boundaries.
Layer 03
Build plan
What gets built, in what order and with which team.
BuildWe build what is needed to test the solution.
The minimum needed to learn something real
MVP
Functional
Ready to be tested with users
ValidateWe measure whether the solution works.
Evidence, not opinion. Three possible paths.
User testing
Real sessions using the MVP.
Usage metrics
Adoption, timing and drop-off points.
Evidence
Against the success criteria
ScaleWe define what comes after validation.
A clear plan for what comes after the pilot
Scale
What comes next
OurTechnologies
Frontend
- JavaScript
- Vue
- Angular
- React
Backend
- Node.js
- Java
- Laravel
- PHP
- Django
- Ruby
- Go
- GraphQL
- .NET
- API
AI & Data
- AI
- Big Data
- Generative AI
- Machine Learning
- Data Science
- Deep Learning
- SQL
- Python
Cloud & DevOps
- DevOps
- AWS
- Azure
- Google Cloud
Mobile
- iOS
- Android
- React Native
- Flutters
CRM & ERP
- Salesforce
- HubSopt
- SAP
- NetSuite
- ServiceNow
- NeatSuite
Blockchain & Web3
- Blockchain
- Smart Contract
- Ethereum
- Cryptocurrency
01Discovery & Strategy
Tools & platforms
- Miro
- FigJam
- Notion
- Confluence
- Jira / Linear
Capabilities
- Discovery Workshop
- Product Discovery
- Business & Technical Discovery
- Requirements definition
- Opportunity identification
- Feature prioritization
- MVP definition
02Product & UX
Tools & platforms
- Figma
- Figma Prototypes
- Design Tokens
- Storybook
- Maze / UserTesting
Capabilities
- Product Strategy
- User Journey
- UX/UI Design
- Wireframes
- Interactive prototypes
- Experience design
- User validation
03Tech & Architecture
Tools & platforms
- AWS
- Azure
- Google Cloud
- Docker
- Terraform
- OpenAPI / Postman
Capabilities
- Technical Discovery
- Solution architecture
- Technology assessment
- Stack selection
- Cloud architecture
- Integrations & APIs
- Technical feasibility assessment
- Technology risk identification
04AI & Automation
Tools & platforms
- OpenAI API
- Anthropic Claude
- LangChain / LlamaIndex
- Vector DBs (pgvector, Pinecone)
- n8n / Make
Capabilities
- AI Discovery
- AI use-case identification
- AI Proof of Concept
- AI Agents
- Generative AI
- RAG · Intelligent automation
- AI integration
- Model and technology assessment
05Build & Validation
Tools & platforms
- Next.js / React
- Node.js / Python
- LangChain / PostgreSQL / Supabase
- Vercel / AWS
- GitHub Actions
- Playwright
Capabilities
- AI Discovery
- AI use-case identification
- AI Proof of Concept
- AI Agents
- Generative AI
- RAG
- Intelligent automation
- AI integration
- Model and technology assessment
06Roadmap & Scale
Tools & platforms
- Jira / Linear
- Notion
- Metabase / Looker Studio
- Grafana / Datadog
- Sentry
Capabilities
- Product Roadmap
- Technical Roadmap
- Evolution roadmap
- Effort estimation
- Prioritization
- Implementation plan
- Scaling recommendations
Clarity
A clear definition of the problem and opportunity.
Product
Product proposal and priority features.
Technology
Architecture and technology strategy.
Prototype / MVP / POC
A tangible solution to validate.
Validation
Results and lessons learned.
Roadmap
A plan to move from validation to production.
What is Quick Start?
A short, focused process that turns an idea, opportunity or technology challenge into concrete evidence before a larger investment.
What is the difference between an MVP and a POC?
A POC validates technical feasibility; an MVP delivers the smallest product version that can test value with real users.
Does Quick Start work for Artificial Intelligence projects?
Yes. It validates data, models, architecture, experience and business value before scaling an AI solution.
What do I receive at the end?
A problem definition, solution proposal, architecture, prototype or validation, and a clear roadmap.
Can I start with an idea that is not yet defined?
Yes. Discovery structures the opportunity, identifies risks and turns the initial idea into a testable scope.
Can Quick Start include development?
Yes. Depending on the goal it can include a prototype, POC or functional MVP.
Do not build blindly. Validate first.
Turn an idea, opportunity or technology challenge into a testable solution.
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