Frequently Asked Questions

No. SparkData Analytics provides research, evidence synthesis, and analysis for educational and informational purposes only. We are not licensed professionals in medicine, law, or finance, and our reports do not constitute professional advice, diagnosis, legal opinions, or investment recommendations. All decisions—especially in areas like health, law, and finance—must be made in consultation with appropriately qualified and licensed professionals who know your specific situation. Think of us as providing research support to inform discussions with your advisors.

Great question! While you could ask a single AI model, our approach provides several key advantages: (1) Multi-model triangulation - we analyze your case independently across GPT, Claude, Gemini, and Perplexity to prevent "echo chamber" errors. (2) Domain-appropriate evidence integration - we cross-reference AI findings with authoritative sources (peer-reviewed research, legal databases, industry standards, etc.). (3) Rigorous quality controls - we use fresh chat sessions, adversarial review, and systematic verification aligned with global AI governance standards. (4) Structured methodology - our 5-phase process ensures comprehensive analysis rather than one-off queries. (5) Professional reporting - you receive a formatted evidence synthesis report designed for collaboration with your advisors.

Short answer: No, we don't paste your raw identity into chatbots. Before any information touches an AI model, we: (1) Remove direct identifiers like your name, email, phone number, street address, and medical record numbers. (2) Replace them with neutral labels (e.g., "Subject A", "Physician 1", "Hospital X"). (3) Limit context so only the relevant, de-identified portion of your case is analyzed. The AI systems see cases, not people: de-identified timelines, lab values, medication regimens, and symptom patterns—not your full identity. We also do not use your case data to train our own models without explicit consent, and we select AI providers/settings that prohibit training their base models on your prompts wherever that is available.

We use our proprietary SparkData orchestration layer to coordinate analysis across multiple leading AI platforms: OpenAI GPT 5.2 Pro, Anthropic Claude Opus 4.5, Google Gemini 3.0 Pro, and Perplexity Deep Research. What makes us different isn't just the models—it's our SDA methodology with CoVe (Chain-of-Verification), contrarian analysis, and multi-agent QA controls that prevent echo chambers and ensure accuracy. For high-risk analyses, we require findings from different model families to ensure architectural diversity in reasoning. We fully disclose which models were used in every report.

We align with global AI governance standards including AICPA Quality Management, EU AI Act, and PCAOB guidance. Key safeguards include: (1) Fresh chat separation - reviewer agents never see builder reasoning, only factual outputs. (2) Multi-model triangulation - independent analysis across different AI platforms. (3) Domain-appropriate evidence validation - all findings cross-referenced with authoritative sources (peer-reviewed research, legal precedent, industry standards, etc.). (4) Adversarial review - devil's advocate challenges when agreement exceeds 80%. (5) Risk tiering (T0-T3) - higher-risk work requires more verification layers. (6) Complete audit trails - decision logs, review reports, and model attribution for every analysis.

Timeline varies based on complexity and domain, but most analyses take 5-7 business days from initial consultation to report delivery. This includes: intake and problem framing (1 day), domain research and data acquisition (1-2 days), multi-model AI analysis (2-3 days), triangulation and synthesis (1-2 days), and report finalization (1 day). Rush services may be available for time-sensitive situations - contact us to discuss your timeline needs.

The more context you provide, the better our analysis. Helpful information includes: (1) Timeline of symptoms or events, (2) Current medication and supplement regimen (with dosages), (3) Recent changes to your regimen, (4) Any lab results or medical records, (5) Photos of supplement labels or medication packaging, (6) Symptom logs with dates and severity. Don't worry if you don't have everything - we'll work with you to identify critical data gaps during the intake process.

Pricing varies based on scope and complexity of the analysis. A standard medical/health data analysis typically ranges from $500-$1,500. We provide transparent quotes after an initial consultation where we assess your needs. There are no hidden fees. Contact us for a personalized quote based on your specific situation.

Currently, we do not bill insurance directly. However, some clients have successfully submitted our reports for reimbursement under HSA/FSA accounts or out-of-network benefits. We can provide detailed invoices and documentation to support reimbursement requests. Check with your insurance provider about coverage for independent medical research or consultation services.

Absolutely - that's exactly what our reports are designed for! We format our evidence synthesis to facilitate productive conversations with your healthcare team. The report includes: comprehensive case overview, research methodology disclosure, primary proposed mechanisms supported by literature, clinical considerations, and specific questions you can ask your doctor. Many clients find that bringing structured, evidence-based research helps their medical team understand complex situations more quickly.

If our analysis identifies potentially serious or time-sensitive findings, we clearly flag them in the report and recommend immediate medical consultation. Remember, we provide research synthesis - not medical diagnosis or treatment. Any concerning findings should be discussed with your healthcare provider as soon as possible. We can help you formulate the right questions to ask, but clinical decisions must be made by licensed medical professionals.

Medical and health data analysis is our flagship service and current focus. Our triangulation methodology and quality controls can be applied to other complex analytical challenges, but we're intentionally starting with one vertical to perfect our approach. If you have a unique analytical need, feel free to reach out - we're always exploring new applications for rigorous, multi-model AI analysis.