QuoteAI — Andy Working Session #2
2026-05-05 morning Leadership pitch locked: Wednesday May 12, 10am-noon (Brehob HQ, Indianapolis) — T-7 days
Three things to land today
1. Do you agree with the four pillars of value?
| # | Pillar | What it means | Mid-scenario annual value |
|---|---|---|---|
| 1 | Labor cost savings | The 3-person sales support team spends ~2.5 hrs per quote today. QuoteAI auto-fills that work. | ~$240K |
| 2 | Institutional knowledge base | 22 years of John's quote DNA captured + every rep retrieves at John's level + anyone in the company can chat over the corpus. | ~$250K hard $ + significant strategic value |
| 3 | Quote throughput | Drafts in 90 seconds vs the 48hr-5day target today → ~20% more quotes through the funnel. | ~$1.18M |
| 4 | Quote win rate | Faster + more complete quotes win more deals. Close rate +1-2pp; quote completeness (auto-attach install + PM) captures revenue most reps leave on the table. | ~$2.5M ($1.7M close rate + $800K completeness) |
| Total mid-scenario | ~$4M / yr |
Conservative ~$2M (just labor + insurance + completeness — the most defensible drivers). Bullish ~$6M+ (close rate + knowledge democratization compounding).
How we got those numbers
| Pillar | Calculation | Mid value | Mechanism(s) |
|---|---|---|---|
| Labor savings | 2,160 quotes/yr × 2.5 hrs/quote × $60 loaded × 75% realization | $243K | Speed + Consistency |
| Knowledge insurance | $2-5M of John's annual book at attrition risk × tool-mediated retention | $175-350K | Searchability + Consistency |
| Quote throughput | Baseline 2,160 quotes × ~50% throughput uplift × 13% close × $40K avg deal × 35% margin | $1.18M | Speed |
| Win rate (close rate) | +1pp close rate ≈ $302K GP at current volume. Mid assumes +2pp on uplifted volume. | $1.7M | Speed + Accuracy + Consistency |
| Win rate (completeness) | 432 won quotes/yr × ~$5K avg install or PM revenue captured × 40% margin | $864K | Accuracy + Consistency |
The pillars interact — throughput uplift compounds with close-rate uplift. Full sensitivity in pricing-model.xlsx (Conservative ~$2M / Mid ~$4M / Bullish ~$6M).
The four mechanisms underneath
QuoteAI delivers value through four mechanisms that combine to power each pillar:
- Speed — drafts in 90s vs the current 48hr-5day turnaround
- Accuracy — catalog matching + structured pricing + verbatim phrasing reduces errors
- Consistency — same template, same phrasing, same structure across all reps and quotes
- Searchability — vector + structured retrieval over the corpus, exposed via chat
Open questions for you:
- Do these four pillars feel right? Anything missing? Anything that doesn't belong?
- Driver-by-driver — do these numbers feel real to a Brehob CFO?
- Which pillar will resonate most with leadership? Which least?
- The 3-person team framing — OK to surface, or too internally charged?
- The throughput assumption is 50% uplift in mid scenario — does that feel right for Brehob's funnel, or should we calibrate up/down?
2. Do you agree with the framing?
The framing we're proposing:
"QuoteAI is the first product on an institutional knowledge platform — quoting is the wedge; chat over the corpus is the next extension; CRM, territory intelligence, and strategic intelligence layer in over time. Same data layer, new use cases, no new integrations required."
The deck (v0.2.1) is built around this framing. Slide 16 — "The Platform Extends — Year 2 and Beyond" — is where the platform thesis lands explicitly.
Open questions for you:
- Does "institutional knowledge platform" land, or feel too abstract for the room?
- Is Slide 16 ambitious or vague?
- Should we lean harder on quoting-as-anchor (and treat platform as future story), or sharpen the platform language with more concrete extensions?
- Is there anyone else at Brehob with a large document corpus like John's? If yes, the platform thesis scales — same ingestion + retrieval layer, new corpus, new use cases. Materially changes the value proposition. Examples: long-tenured engineers with project notes, technical staff with sizing/spec libraries, ops people with vendor / install playbooks.
3. How do you think we should price it?
We want your unanchored read first. What would Brehob actually pay for this?
- What does the company typically pay for SaaS tools per year?
- What buying motion are we up against — software license? Services contract? Mixed?
- Who has signoff at what dollar threshold?
- Where does this rank against other initiatives competing for the same budget?
Indianapolis room intel — May 12
The leadership meeting is locked. We're flying in for it. You're our only source for the room — burn through these:
- Who's in the room? Names, roles, signoff power.
- CFO-heavy or ops-heavy? Drives whether we lead with margin math or labor capacity.
- Each attendee — skeptic, champion, or quiet? Helps us calibrate where to lean in vs back off.
- Any AI/automation history at Brehob — has anyone there pushed something similar before? How did it land?
- Setting — boardroom / conference room / informal? Affects demo laptop setup.
- Decision timeline expectation — react in the room, or take it back for deliberation?
- The Ask — soft (30-day evaluation), direct (90-day pilot), or open ("what would you need to see")? Which fits the room?
- LinkedIn recon — should we name specific salespeople in the audience, or stay generic?
What to build before May 12
We have ~7 days. What's worth building, what's not?
- Should we build the KPI dashboard for the leadership demo? Per PITCH-D5 it's already in Year 1 scope — quote count, win-rate trend, pipeline by salesperson. Thin layer over data we already track. ~1 day of Next.js work. Would it land as a "this is for me" moment for leadership, or feel premature without real production data behind it?
- Any other features that would land hard with the leadership room? What did you wish QuoteAI had when you imagined the demo from leadership's perspective? (Examples we've considered: real Brehob customer quote regenerated live, completeness moment with auto-attach install/PM, equipment datasheet auto-attach.)
- What should we NOT spend time on? If something's a "nice to have" but not load-bearing for the meeting, calling it out now saves a week we could spend on what actually matters.
Information we'd love your help getting
- What ERP does Brehob run on, and does it expose APIs? (If you can share.) Foundation of the Year 2+ integration story — and of the bigger Hannah Labs thesis: if business systems expose APIs, we can wrap them in MCP servers and connect them all to an AI agent. Knowing Brehob's ERP + API maturity tells us whether that path is open. Likely candidates: Epicor Prophet 21, Infor CloudSuite Distribution, NetSuite, Acumatica.
- Has Brehob been pitched by Canals, Distro, BoltWise, or Endeavor AI? Or any other AI-quoting startup targeting industrial distributors? These four emerged 2024-2025 and target your exact buyer profile. Canals especially — their public pitch line is "captures the institutional knowledge of your best talent before they retire," which is essentially ours. Knowing if leadership has already been approached materially changes how we frame our differentiation in the room.
- From Matt: quote volume + win rate + avg deal size, broken down by region. Tightens the value model.
- Brehob air-division revenue split — confirming our working ~$30M assumption.
- 3-year trailing GP baseline — needed before the trip; whole-company or air-division-only?
- Brehob's existing CRM / lead-management tools (separate from ERP) — informs the Year 2 integration story.
- Customer loyalty split — loyal-to-John vs loyal-to-Brehob. Tightens the knowledge-insurance lever.
What we're showing today
- Deck walkthrough (v0.2.1) — focus on what's changed since you last saw it
- Live demo + scoped chat
- Value model — driver by driver
Not showing today: pricing options. We want your read first (Q3 above), and pricing should be locked AFTER leadership reacts to value, not before.
Next steps after this session: integrate your feedback into v0.3 deck polish. v0.3 ready for your final review pre-trip (target: 2026-05-09).