Formless Solutions works where data, software, AI, and adoption need to support a real decision workflow. Each service starts by clarifying the decision context, then shapes the build, evaluation, training, or handoff path around what the team actually needs to use.
Clarify
Name the decision, users, constraints, and evidence needed.
Shape
Choose the data, software, AI, training, or integration path.
Measure
Test behavior, quality, adoption, and operational fit.
Handoff
Document ownership, review cadence, and next implementation work.
Fixed scope · 5–15 business days · R$ 2,500–12,000
Clarify what to test, build, train, defer, or stop before committing to a larger AI, data, software, or automation project. Compare three written diagnostic options with fixed scope and price.
Metrics, pipelines, decision workflows
Build a trusted decision layer around the data that matters: shared definitions, quality checks, ownership, reporting flows, and AI-ready contracts.
Internal tools, APIs, workflow automation
Turn repeated work into practical software: intake flows, internal tools, integrations, reporting automation, prototypes, and handoff-ready systems.
Models, RAG, source grounding
Evaluate AI, ML, retrieval, and automation workflows before scaling them, with clear test cases, source checks, review paths, and known limits.
Workshops, fluency, workflow change
Help teams use AI with shared judgment through role-specific examples, applied workshops, review habits, workflow redesign, and lightweight governance.
Cross-functional execution
Align strategy, data, software, AI, and adoption when several workstreams need one practical sequence, pilot scope, and decision map.
Access, isolation and recovery
Review identities and permissions, environment separation, and recovery plans for software, data, and automation. We agree on the systems and access boundaries first, then document risks and priorities. Implementation and any specialist testing are scoped separately.
Methods, experiments, pipelines
Research engineering is embedded in the service work: compare methods, test assumptions, build evaluation or data pipelines, and connect technical results to operating decisions.
