ProofGuard
BuildingAn AI agent system focused on validating claims, evidence and digital proof before information is trusted or acted upon.
Intelligence & financial technology
AI Development & Intelligent Automation
I build AI features around a task that can be evaluated: finding useful information, processing documents, preparing a response or supporting a decision. I define evidence, human review and failure behavior alongside the happy path.
Where the friction appears
- Useful knowledge is scattered across documents and disconnected systems.
- Research and support teams repeat the same information-processing steps.
- An AI idea has no clear evaluation, review boundary or workflow owner.
What I can build for this scope
- Assistants, agents and AI-supported business processes with defined review points.
- Search, document processing, knowledge systems and source-linked answers.
- Support workflows, application integrations and workflow automation.
- Research, intelligence gathering, opportunity scoring and evidence-led prototypes.
How I choose the technical approach
I separate deterministic rules from model-generated suggestions, define what data may be sent to a provider, and test representative inputs and failure cases. Model outputs need evaluation and appropriate human oversight; automation does not make them inherently correct.
- Inputs and permissions
- Workflow and decisions
- Useful output and review
How I deliver the work
I frame a measurable task, assemble agreed example inputs, prototype the workflow, evaluate source quality and failure behavior, then connect it to the surrounding product with logging and review boundaries.
Relevant work and its actual stage
ProofGuard is Building: its specification describes claim verification and evidence trails. SignalScout is a Prototype for social intelligence and engagement. They show my direction and scoped systems thinking, not fabricated production AI outcomes.
SignalScout
PrototypeA social intelligence and engagement system designed to identify promising conversations, score opportunities and help create better responses for X/Twitter growth.
Practical questions
Can an AI assistant use my business documents?
Potentially. I first assess permissions, document quality, retrieval needs and provider data policies. The scope should specify sources, access controls and how users check an answer.
How do you evaluate AI behavior?
I use task-specific examples, known failure cases and explicit acceptance criteria. Where the answer affects an important decision, I design a review step and make uncertainty visible.
What testing and documentation are included?
I agree checks with the scope: key user journeys, data transitions, failure handling and relevant accessibility or device behavior. Handover documentation covers the agreed setup and operating needs; maintenance is scoped separately.
Start a Project
Tell me what needs to work.
Share the problem, existing systems and the outcome you need. I will use the details to assess fit and scope.