Synilly: Data Grounding & Improvement Paths

Why this matters: The same concept test question ("Would you pay $49/mo?") produces authentically different responses because each persona's grounding data creates distinct reasoning paths.

UX simulation: Messages appear sequentially with typing indicators and variable delays (600-2000ms). Live "insights" sidebar updates as themes emerge. All pre-scripted — no LLM in the loop.

Maya Chen's Study History (4 studies)

• Oct '24: Dog food concept — flagged $49 as borderline, wanted guarantee
• Jan '25: Meal kit study — drew parallels to pet food subscription fatigue

Cross-study signal: Her price sensitivity was consistent across both studies, and she connected the two topics unprompted. Consistency score: 82%

Derek Thompson (5 studies, 91% consistency)

• Always demands published data / clinical evidence
• Always calculates ROI before committing
• Pet monitoring study: "If I can't export the data, what's the point?"

4/5 panelists flagged $49/mo as too high. Acceptable range: $29-39/mo.

Every panelist demanded concrete evidence. Type of proof varies by segment.

Recommendation (High Priority)

Introduce a "Guidance Only" tier at $15-20/mo — separating nutritionist from food delivery.

The Problem

Our 50 personas are plausible but not validated. Maya's $95k income, Portland location, and UX career were authored by a human guessing at realistic combinations. We have no way to know if this profile actually exists in meaningful numbers.

The Solution

Feed real data sources (census, BLS, market research) into an LLM persona generator. Each generated persona comes with citations back to the source data. Demographics are statistically representative, not imagined.

Data Sources

• US Census / ACS — demographics, income, geography
• Bureau of Labor Statistics — occupations, salary bands
• APPA National Pet Owners Survey — pet spending data
• Pew Research — media habits, tech adoption

Key gain: Clients can ask "is this persona representative?" and we can point to real data instead of saying "we think so."

The Problem

Current discussions are fully deterministic. Same brief always produces the same conversation. No follow-up questions, no emergent insights, no ability to probe deeper on surprising responses.

The Solution

Each persona becomes an independent LLM agent with:

• A system prompt built from their full data model (50+ fields)
• Conversation memory (within session + cross-study)
• A Moderator Agent that manages turn-taking, probes, and phase transitions

What Changes

Same brief, different run = different conversation. Personas can disagree, build on each other's points, or reveal unexpected connections. The moderator can follow up on surprising responses in real time.

Example: What Multi-Agent Enables

Tom says "$49 is absurd." Maya responds: "Wait, I spend $50/mo on Farmer's Dog and it doesn't bother me... why does this feel different?" The moderator picks up on this and probes: "Maya, can you unpack that? What makes one $50 feel okay and another feel wrong?" This exchange can't happen in a pre-scripted system.

The Problem

We have no way to measure if synthetic research outputs are accurate. Our confidence scores (80%, 90%) are hand-assigned, not empirically validated. A client has no reason to trust them over their own intuition.

The Solution

Partner with clients running real focus groups. Run the same brief through Synilly. Compare outputs. Build a calibration dataset that measures where synthetic research agrees with real research — and where it diverges.

What This Unlocks

• Empirically-backed confidence scores
• Known weak spots (e.g., "synthetic personas underestimate price sensitivity by 15%")
• Correction weights we can apply to future outputs
• A credibility story for enterprise sales

Trust equation: A tool that says "we're 74% confident, and here's why" is more trustworthy than one that says "we're 90% confident" with no backing.

The Problem

Our personas exist in a frozen world. Maya's opinions about The Farmer's Dog were written months ago. She can't reference a recent price increase, a viral TikTok controversy, or a competitor's new product launch.

The Solution

Before each discussion, pull live context relevant to the research topic:

• Pricing: Current subscription prices from competitor sites
• Reviews: Recent sentiment from Reddit, Amazon, Trustpilot
• Social: Trending conversations on TikTok, Instagram
• News: Relevant industry developments

Inject this as additional context into each agent's prompt. Maya doesn't just have opinions — she has opinions informed by what's actually happening right now.

Research value: The insight shifts from "people think $49 is a lot" (obvious) to "people think $49 is competitive vs Farmer's Dog but weak vs Spot & Tango" (actionable).

The Problem

We present findings as if 5 synthetic personas constitute a valid sample. "4/5 panelists agree" sounds compelling, but N=5 has no statistical power. Experienced researchers will immediately question this.

The Solution

• Run multiple sessions: Same brief, 10 different panels of 5 = 50 data points
• Confidence intervals: "Price concern appeared in 78% of sessions (CI: 65-88%)"
• Sample size warnings: Flag when N is too low for claim strength
• Bias detection: Check if persona demographics over/underrepresent populations
• Sensitivity analysis: Would the finding change with different persona selection?

Key insight: The cost of running 10 synthetic sessions is trivial (minutes + API cost). We should exploit this advantage over real research where N is expensive.

The paradox: Showing less certainty (confidence intervals, bias warnings) actually increases trust with sophisticated research buyers.

1. The foundation is strong

50+ structured fields per persona, psychological contradictions, longitudinal memory, brand relationship topology. This data model is genuinely deeper than most competitors.

2. The critical gap is execution

The data exists but isn't being used by an LLM at runtime. Moving from pre-scripted to multi-agent discussions is the single highest-impact change.

3. Credibility requires honesty

Statistical validation and calibration are less exciting than AI agents, but they're what turns "interesting demo" into "tool a researcher would trust." Show uncertainty to earn trust.

4. The moat is the data pipeline

Anyone can prompt an LLM to role-play a persona. RAG grounding + calibration against real studies + statistical validation creates a defensible system that improves with every use.