Sienge GO | AI

ERP Platform with IA

Main Problem and Goals

The Brazilian construction sector, dominated by 200k+ micro and small enterprises (MSEs), faced limited access to ERP systems due to high costs, excessive complexity, and inflexibility. Softplan’s legacy ERP, while specialized, was too expensive and cumbersome for MSE adoption, creating a market gap for an intuitive, affordable solution.

Goals:

Design a cost-effective, user-centric ERP tailored to MSEs in construction.
Validate product hypotheses through structured research to ensure market fit.
Integrate AI (IBM Watson) to enhance data-driven decision-making and user proactivity.
Establish scalable UX processes to empower cross-functional teams (product, marketing, sales).

Key Achievements:

Launched an award-winning SaaS ERP (IBM Beacon Awards 2019).
Reduced implementation costs by 40% compared to legacy system customization.
Enabled 30% faster onboarding for SMEs through intuitive UI and chatbot guidance;
Scaled the UX team from 1 to 5 designers, institutionalizing user-centered practices.

Strategy

Differentiators:

Product Discovery Framework: A structured approach combining exploratory research, iterative prototyping, and AI-driven innovation.
AI Integration: Leveraged IBM Watson for predictive analytics and proactive chatbot interactions, transforming static data into actionable insights.
Cross-Functional Impact: Aligned UX outcomes with business metrics (e.g., Mixpanel dashboards tracked user behavior, directly informing marketing and sales strategies).

Tools & Methods:

Mixed-method research (qualitative interviews → quantitative surveys).
Benchmarking against 15+ ERPs to identify usability gaps.
Prototyping (Figma, Sketch) paired with usability testing and heuristic evaluation.
Agile collaboration with PMs, developers, and IBM technical teams.

Discovery

Objective: Uncover MSE pain points and validate market opportunities. Methods:

Conducted 12 in-depth interviews with construction business owners to map workflows and frustrations.
Analyzed open market data (IBGE, SEBRAE) to quantify ERP accessibility gaps.
Competitive benchmarking to identify feature parity and innovation opportunities.
Collaborated with IBM to assess AI use cases for financial and project management modules.

Outcome:

Identified critical needs: simplified budgeting, real-time financial alerts, and proactive risk mitigation.

Define

Objective: Prioritize features and align stakeholders on MVP scope. Methods:

Mapped findings to a CSD Matrix (Certainties, Suppositions, Doubts) to clarify hypotheses.
Facilitated roadmap workshops with PMs to prioritize modules (Finance, Purchasing, Scheduling).
Created user journey blueprints to visualize pain points and AI integration opportunities.

Outcome:

Defined MVP scope with focus on financial forecasting and chatbot-driven interfaces.

Develop

Objective: Build and validate AI-enhanced prototypes. Methods:

Designed low/high-fidelity prototypes for key flows (e.g., “Will the project run out of money?” chatbot interaction).
Conducted usability tests with 20+ MSE owners, iterating on IA navigation and dashboard clarity.
Partnered with developers to integrate Watson APIs for real-time data compilation (e.g., cash flow predictions).

Outcome:

Achieved 85% success rate in usability tests for core features.
Delivered chatbot logic that reduced user queries to support by 50%.

Delivery

Objective: Ensure seamless rollout and continuous improvement. Methods:

Documented UI specifications, user flows, and chatbot decision trees for developer handoff.
Implemented Mixpanel analytics to monitor feature adoption and identify friction points.
Trained post-sales teams on UX insights to improve client onboarding.

Outcome:

System adoption increased by 60% in the first year post-launch.
Recognized with the IBM Beacon Award for innovative AI application.