needl.ai

THE ASK: Approve 8 weeks of engineering effort ($220K at regional cost benchmarks) to build an AI Research Brief Generator that transforms clustered search results into structured briefs in under 30 seconds.

THE BET: We believe 65% of enterprise researchers (source: Q3 user survey, n=112) will generate ≥3 briefs/week within 4 weeks of launch, saving 3.7 hours/week per user by eliminating manual synthesis.

THE ROI EQUATION:
2,800 active researchers (source: MAU dashboard, Aug 2025) × 156 briefs/year × $42 saved per brief (source: Gartner knowledge worker productivity study) = $18.3M/year recoverable value.
If adoption is 40% of estimate: $7.3M/year.
(Cost basis: 2 FT backend @ $8.5K/month, 1.5 FT frontend @ $7.2K/month, 1 FT ML @ $11K/month × 2 months)

THE KILL CRITERIA: If <15% of eligible users generate ≥1 brief/week by D30, pause and reassess.

This feature is an automated brief generator for pre-clustered content with configurable audience/output templates. It is not a raw data analyzer, real-time collaborator, or primary research tool.

Competitor approaches:

• Notion: Manual template filling with no content auto-population
• Salesforce Einstein: Summarizes single documents but can’t cross-analyze clusters
• Miro: Visual synthesis boards requiring manual drag-and-drop

Our wedge is cross-source synthesis because only we ingest clustered content from Slack/docs/email to generate narrative insights.

WHO / JTBD: When a research lead at a Fortune 500 firm aggregates findings across 20+ sources, they need to distill insights into an executive-ready brief — so stakeholders can make decisions without reviewing raw data.

THE GAP: Users can cluster related content but cannot auto-synthesize it into narrative formats. This forces manual copy-paste into slide decks, costing 3.7 hours/week (source: time-tracking study, n=89, July 2025) and introducing inconsistency risks.

QUANTIFIED BASELINE:

Business case: 2,800 users × 156 briefs/year × 3.7 hrs × $68/hr blended cost = $108.9M/year recoverable. Auto-generation reclaims 17% of this: $18.3M/year.

CORE MECHANIC:

1. User selects saved cluster → triggers brief modal
2. Answers:
   • Brief type (executive/technical/risk)
   • Audience (C-suite/legal/product)
3. AI outputs: key findings, citations, open questions, next steps

ADVERSARIAL STRESS-TEST:

• Attack: 50+ sources overwhelm context window
  Mitigation: Prioritize top 15 by relevance score; surface "sources truncated" warning
• Attack: User selects unrelated items
  Mitigation: Flag cluster coherence score <40%; require confirmation
• Accepted limitation: Cannot resolve conflicting data without human input

WIREFRAMES:

┌───────────────────────────────────────────┐
│ Generate Research Brief                   │
├───────────────────────────────────────────┤
│ Cluster: [Competitor Analysis Q3] ▼       │
│ Brief type: [Executive] ▼                 │
│ Audience: [C-suite] ▼                     │
│                                           │
│ [Generate]                       [Cancel] │
└───────────────────────────────────────────┘

┌───────────────────────────────────────────┐
│ Research Brief: Competitor Analysis Q3    │
├───────────────────────────────────────────┤
│ KEY FINDINGS                              │
│ - Competitor X shifted focus to SMBs...   │
│                                           │
│ SOURCES (12/20 shown)                     │
│ 1. Slack #market-trends (Sep 3)           │
│ 2. Gartner Report 2023.pdf                │
│                                           │
│ OPEN QUESTIONS                            │
│ - Impact on enterprise retention?          │
│                                           │
│ NEXT STEPS                                │
│ [Schedule deep dive]    [Export to PPT]   │
└───────────────────────────────────────────┘

Phase 1 — MVP (6 weeks)
US#1 — Brief generation

• Given 5-20 clustered items from ≥3 sources
• When user selects cluster and audience
• Then system outputs brief with:
  • P0: 100% accurate citations (zero tolerance)
  • P1: ≥99% factual accuracy in findings
  • P2: ≥90% relevance for next steps
• If story fails: Legal disclaims invalidated
• Validated by QA against 200-sample corpus

Out of Scope (Phase 1):

Phase 1.1 (3 weeks): Brief version history
Phase 1.2 (2 weeks): Regulatory compliance templates

PRIMARY METRICS

GUARDRAIL METRICS

WHAT WE ARE NOT MEASURING:

• Total briefs generated (vanity; doesn’t indicate value)
• AI confidence scores (internal signal only)
• Clicks on "Export" (secondary to time saved)

PERFORMANCE:

• Generate briefs in ≤12 sec P95 (5 sec avg) for 20-source clusters
• Support 50 concurrent users at launch

SECURITY:

• Briefs inherit source ACLs
• Audit trail for all generations

ASSUMPTIONS VS VALIDATED:

RISK 1 — Low Executive Adoption

• Trigger: C-suite briefs lack financial impact framing → Consequence: Stakeholders reject outputs → Impact: 40% adoption shortfall
• Probability: Medium | Impact: High
• Mitigation: Preload finance templates (Owner: PM; Deadline: Launch)

RISK 2 — EU AI Act Compliance

• Trigger: No "synthetic content" disclaimer → Consequence: Briefs violate Article 52 → Impact: France/Germany rollout blocked
• Probability: High | Impact: Critical
• Mitigation: Implement EU-mandated watermark (Owner: Legal; Deadline: Phase 1)

RISK 3 — Source Hallucination

• Trigger: Model cites unsaved Slack threads → Consequence: Loss of stakeholder trust → Impact: 25% CSAT drop
• Probability: Low | Impact: High
• Mitigation: Ground citations in indexed content only (Owner: ML lead; Deadline: UAT)

KILL CRITERIA (within 90 days):

1. 15% error rate in findings

2. <10% adoption by research leads
3. P95 latency >30 seconds

Decision: Output structure rigidity
Choice Made: Fixed sections (Findings/Sources/Questions/Next Steps)
Rationale: Rejected free-form narratives to ensure auditability and compliance
────────────────────────────────────────
Decision: Source citation depth
Choice Made: Show top 15 sources + "View all" toggle
Rationale: Rejected unlimited citations to prevent cognitive overload; preserves traceability
────────────────────────────────────────
Decision: AI model scope
Choice Made: Fine-tuned Llama 3 (70B) vs. GPT-4
Rationale: Lower hallucination rates in internal tests (4.2% vs 8.7%)
────────────────────────────────────────
Decision: Edit permissions
Choice Made: Lock source citations post-generation
Rationale: Prevents evidence tampering; allows findings edits

BEFORE/AFTER NARRATIVE
Before: Sarah (Research Lead, PharmaCo) spends Thursday morning copying Slack threads, email insights, and clinical trial PDFs into PowerPoint. She manually rephrases technical jargon for executives, loses 2 key sources, and submits the brief 3 hours late — delaying a drug approval meeting.

After: Sarah selects her "Trial Results Q3" cluster, chooses "executive/C-suite". In 11 seconds, she gets a brief with patient response rates, source links to the original data, and FDA submission next steps. She adds one insight and shares it 8 minutes before the meeting.

PRE-MORTEM
It is 6 months from now and this feature has failed. The 3 most likely reasons are:

1. We prioritized technical accuracy over executive narrative, so briefs felt robotic and were ignored by decision-makers.
2. Legal blocked EU rollout due to missing synthetic content disclaimers, killing 40% of projected revenue.
3. Competitors added cluster-to-brief workflows in existing tools (e.g., Notion AI) before we shipped Phase 1.2’s differentiators.

Success looks like: Research directors emailing screenshots of briefs saying "We shipped this to the board in half the time." Support tickets for manual synthesis drop by 65%. The CFO notes in earnings prep: "This finally makes our research spend measurable."