BrokerTriage

1. One-liner

Scores every broker quote for a discontinued part so a buyer knows whether to buy, test, or walk.

2. Trend signal — why now?

The volume of parts falling off the authorised channel hit a record last year, and most of it happened silently. A record 621,909 electronic components were discontinued or declared obsolete in 2025 — roughly one-third more than in 2023 — and 52% of those parts (323,286 components) reached end-of-life with no Product Change Notification from the manufacturer at all. Of the PCNs that were issued for the remaining 48%, only about 26% met JEDEC standards.

That failure has a specific downstream consequence, and it’s the one nobody sells a tool for. When a part vanishes without notice, the small buyer doesn’t get a graceful last-time-buy. They get a stockout, and then they get pushed into the open market. Roughly 25-30% of lifecycle changes happen without preceding PCNs reaching all affected customers, and smaller buyers who are not on those lists often discover a part is obsolete only when they try to reorder and find zero stock. By then, the LTB window has already closed — and the standard window was only about six months to begin with.

The open market they land in is large and genuinely risky. The grey market accounts for 6-8% of the broader electronic components market, as much as $60 billion. Pricing on broker market parts can be 30-70% below authorized when allocation is tight, which is exactly what makes a desperate buyer click yes. But up to 15% of replacement parts globally are suspected to be fake when sourcing obsolete components, and 34% of emergency component replacements require at least one PCB modification due to overlooked package tolerances or pinout differences. Meanwhile lead times on the authorised side are ugly enough to force the issue — AI server demand has stretched power-device lead times to as long as 270 days in 2026.

The industry’s own answer is “just don’t do it.” The Electronic Components Industry Association has issued a renewed call to source parts exclusively through manufacturer-authorized channels. That’s good advice a small manufacturer with a stopped line cannot follow. They are going to buy from a broker. The only real question is which offer, and whether to pay $200–$2,000 per batch for third-party testing before it goes on the line.

Provenance:

• Signal 1 (demand): Record 621,909 components EOL’d in 2025 with 52% issuing no PCN at all; small buyers discover obsolescence at reorder after the LTB window closed — https://www.dasenic.com/blog/knowledgeHub/obsolete-components-in-2026-eol-list-shortage-statistics-what-to-do-about-it and https://j2sourcing.com/blog/component-obsolescence-eol-last-time-buy-playbook-2026/ — 2026-08-17
• Signal 2 (feasibility): $60B grey market with quantified counterfeit and rework rates (up to 15% suspect parts; 34% of emergency replacements need a PCB mod); third-party test services priced at $200–$2,000/batch, making a buy/test/walk recommendation economically actionable — https://www.componentsense.com/blog/what-is-the-electronics-grey-market — 2026-08-17
• Signal 3 (economic): PCNshark ships a focused SMB lifecycle tool at $149–$999/mo, proving small hardware teams pay real money in this exact category — while explicitly scoping itself to processing notices that arrive, not to sourcing decisions — https://pcnshark.com/compare/siliconexpert-alternative — 2026-08-17 Category: Underserved niche

3. The opportunity

Every existing tool in this space is oriented around the notice. SiliconExpert, Z2Data and Accuris sell lifecycle databases — they tell you a part’s status if you go and look, and they’re priced for aerospace and defence primes, with no public self-serve tier. PCNshark, the closest SMB-priced player, is explicit about its scope: it’s for teams whose “primary problem is execution after a notice arrives,” and it states it does not replace lifecycle forecasting, sourcing information, or alternate-part research.

So the entire category assumes a notice exists. For 52% of last year’s EOL events, it didn’t.

The unserved moment is downstream and much sharper: it’s 4pm, the line is down, a buyer has three broker quotes for the same obsolete MPN in their inbox at wildly different prices, and they have to decide which one to trust. Today that decision is made on gut feel, a phone call, and whether the broker’s website looks legitimate. The buyer has no structured way to weigh a 60%-below-authorised price against a 15% counterfeit base rate, or to know that this particular replacement will probably need a board change anyway.

This is a triage product, not a database. It doesn’t need to out-crawl S&P Global on part coverage — it needs to take the offers a buyer already has and turn them into a ranked, defensible recommendation with a reason attached. That’s a job an AI-first two-person team can do well, and one the incumbents structurally aren’t pointed at.

4. Target market

• Primary customer: Component buyer / procurement engineer at a small electronics OEM or contract manufacturer — 20–300 employees, running 5–50 active BOMs, typically in industrial, medical-device, instrumentation or automotive-tier-2. US, EU and India. No dedicated obsolescence manager on staff; a substantial number of OEMs have not established a formal process to address obsolescence, nor do they have a dedicated obsolescence manager.
• Why they buy: Their line stops, or a build is at risk, and they’re staring at broker quotes for a part nobody makes anymore. The downside is asymmetric and they know it — a bad batch means field failures on their name, and a single obsolescence-driven redesign costs between $20,000 and nearly $2 million according to Z2Data. Spending $200 on the right decision is trivially worth it; the problem is they don’t know which decision is right.
• Rough TAM reasoning: Tens of thousands of small and mid-size electronics OEMs and CMs globally sit below the enterprise-tool line. If even 20,000 of them worldwide have a buyer facing this decision several times a year, a product converting a low-single-digit percentage at $200–400/mo clears $1M ARR comfortably. This is deliberately a niche too small to interest S&P Global.
• Why now for them: The silent-EOL volume spiked in 2025, authorised lead times stretched to 270 days in 2026, and the ECIA is loudly telling them the open market is dangerous — without giving them an alternative when the part simply doesn’t exist anymore.

5. Product sketch (MVP)

• Forward or paste broker quotes (email, PDF, spreadsheet) for a part you can’t get through authorised channels; the product parses offer, price, quantity, date code, claimed provenance and stated certifications.
• A single triage verdict per offer — buy / test first / walk — with the specific reasons stated in plain language, not a black-box score.
• Price-sanity check: flags offers that sit implausibly far below the last known authorised price, using the 30-70% below-authorised band as the risk gradient rather than a hard cutoff.
• Broker reputation dossier: what the seller claims (AS6081/AS6171 posture, traceability, warranty, return terms), what’s verifiable, and what’s missing — the gaps are the output.
• Drop-in replacement check: whether the proposed alternate is genuinely form-fit-function or is likely to need a board change, given that 34% of emergency replacements require a PCB modification.
• Test-or-not economics: given batch value and application criticality, tells you whether $200–$2,000 of third-party testing is worth it on this specific buy.
• A one-page decision record per purchase — what was offered, what was checked, why you proceeded — so the buy is defensible to your quality team or your customer later.
• Watchlist on your riskiest MPNs so the next stockout isn’t a surprise.

6. AI angle — what’s load-bearing

Remove the AI and this product doesn’t exist. The core input is unstructured garbage: broker quote emails, scanned PDFs, inconsistent MPN formats, datasheet fragments, and free-text provenance claims that vary by seller. Parsing that into comparable structured offers is the first load-bearing job.

The second is the judgement layer — reading a datasheet pair and reasoning about whether an alternate is truly drop-in for this footprint, and weighing a bundle of soft signals (price delta, date-code plausibility, provenance language, seller posture) into a recommendation with an argued rationale. That’s exactly the kind of messy, context-heavy call that a competent human buyer makes well and a rules engine makes badly, and it’s now cheap to do at inference time. A spreadsheet of red-flag rules would be a worse product and every buyer would ignore it within a month.

What AI is not doing here: it is not authenticating a physical part. The product must never claim to detect a counterfeit — it triages the decision and tells you when to send it to a lab that can.

7. Localization angle (if any)

Mostly a global play — the open market is inherently cross-border and the risk logic is identical everywhere. Two real local wedges exist. India’s electronics manufacturing base is expanding fast under PLI-driven assembly growth, and those buyers are newer to open-market sourcing, less likely to have inherited a counterfeit-avoidance process, and priced out of enterprise tools — a ₹8,000–15,000/mo tier works where $400/mo doesn’t. Second, a large share of open-market supply is quoted out of Shenzhen-based sellers in mixed English, so parsing quality on non-native quote text is a genuine product differentiator rather than a cosmetic one.

8. Business model — path to $1M–$5M ARR

• Pricing: $199/mo starter (1 seat, 10 triages/mo), $399/mo team (5 seats, unlimited triage, watchlist), $899/mo for multi-site CMs. Deliberately benchmarked just under PCNshark’s $149/$499/$999 ladder, which established the band.
• ACV: ~$4,800 realistic blended.
• Rough math to $1M ARR: 210 customers × $399/mo × 12 ≈ $1.0M. That’s a small number of small manufacturers, which is the point.
• Rough math to $5M ARR: ~1,000 paying accounts, plus an expansion into per-decision fees on high-value buys and a referral cut from partnered test labs. Needs the watchlist to become the daily surface so the product isn’t only opened during emergencies — this is the main thing that has to be true.
• Expansion path: Seats first, then BOM watchlist volume, then a lab-referral margin on the test-first verdicts the product itself generates.

The honest weakness: this is bought in a panic, and panic purchases churn once the panic ends. The watchlist exists specifically to convert an emergency tool into a subscription, and if that conversion doesn’t happen the ARR math above is optimistic.

9. Go-to-market wedge — first 100 customers

• Scrape EOL and PDN notices published by the major manufacturers and distributors — these are public. Cross-reference the affected MPNs against public teardowns, Octopart/Nexar part pages and job-board postings that name the part or product family, to build a list of small OEMs likely holding an affected design. Email the buyer with the specific part they’re exposed on and a free triage of any quote they’ve received. This is a named list with a named pain, not a persona.
• Camp in the places these buyers already ask for help: the DigiKey forum’s obsolete-part and replacement threads, r/PrintedCircuitBoard, r/AskElectronics and EEVblog have a steady flow of “this part is gone, is this seller legit?” posts. Answer them properly for free, with the product’s reasoning visible. That earns the first 30 accounts and doubles as the source of the customer-voice research this proposal is currently short on.
• Partner with 3–5 independent test labs already selling $200–$2,000 batch testing. The product routes qualified test-first verdicts to them; they refer buyers who need a decision framework but not always a test. Aligned incentives, no channel conflict with the incumbents.
• Target CM quality managers directly with the decision-record output — they carry the liability when a suspect part reaches a customer’s product, and a defensible paper trail is an easier sell than a sourcing tool.
• Publish a monthly “what went EOL silently this month” digest built from the same scrape. It’s genuinely useful, it compounds, and it makes the watchlist upsell obvious.

10. Build complexity — justification

Low. Everything is off-the-shelf: document parsing and reasoning on commodity model APIs, part metadata from Nexar/Octopart and distributor APIs, standard web stack. No custom models, no hardware, no physical inspection. The genuinely hard parts are prompt and evaluation discipline on the triage verdict, and the EOL-notice scraper that powers distribution. A technical founder plus a procurement-domain advisor ships a credible v1 in 8–10 weeks.

11. Gating checklist

Gate	Pass?	Note
Legal in target market	✅	Advisory tool on public and user-supplied data. Must carry clear disclaimers — it triages decisions, it does not certify authenticity.
Ethical — no harm / dark patterns	✅	Reduces counterfeit parts reaching real products. Ethical duty is to never overstate confidence and to route genuinely risky buys to a lab.
Market exists (evidence above)	✅	Record EOL volume, $60B grey market, and a competitor already charging $149–999/mo in the adjacent slot.
1–5 person team can build this	✅	Two people, ~10 weeks.
Launchable with <$50K / ₹40L	✅	Well under — inference and data API costs dominate, both usage-scaled.

All five pass.

12. Feasibility score

Axis	Weight	Score	Notes
Problem intensity	20	16/20	Line-down urgency with asymmetric downside; redesign costs run $20K–$2M. Not 18+ because the acute moment is episodic per buyer, not daily.
Demand evidence	15	12/15	Strong sourced market and volume data, plus a priced competitor in the adjacent slot. Held below 13 because I could not surface verbatim buyer quotes for this specific decision — the pain is inferred from industry data, not yet heard first-hand.
Build feasibility	15	12/15	Off-the-shelf throughout; the eval discipline on verdict quality is the only real engineering risk.
Distribution clarity	15	11/15	The EOL-scrape-to-named-buyer list is genuinely concrete and the forum channel is real, but conversion rates are unproven.
Revenue mechanics	15	11/15	Price band validated by PCNshark. Docked for the emergency-purchase churn risk — the watchlist-to-subscription conversion is an unvalidated assumption.
Time to first revenue	10	7/10	Acute pain shortens the cycle; a buyer with a stopped line will pay this month. Needs a short pilot to build verdict trust.
Defensibility	10	3/10	Honestly weak. No proprietary data at launch and the mechanism is copyable. Accumulated broker-behaviour and verdict-outcome data is the only real moat, and it takes a year to matter.
Total	100	72/100

13. Qualitative modifiers

Founder-fit tags

technical-heavy · domain-expertise-required — needs someone who has actually bought parts on the open market, or a co-founder who has. Without that, the verdicts will be plausible and wrong.

Key assumptions to validate (3–5)

1. Assumption: Small-OEM buyers face this buy/test/walk decision often enough to subscribe rather than buy once in a crisis. How to test: 30 interviews with buyers at 20–300 person OEMs; ask how many open-market buys they made in the last 12 months and how they decided. Need a median of 4+.
2. Assumption: A structured triage verdict is trusted enough to change behaviour. How to test: Run 25 historical broker offers past experienced buyers blind; measure agreement between the product’s verdict and theirs. Below 70% agreement, the product is noise.
3. Assumption: The watchlist converts an emergency tool into a retained subscription. How to test: Track 90-day retention on the first 20 accounts acquired during an acute stockout. This is the single biggest risk to the ARR model.
4. Assumption: Buyers will forward genuinely sensitive broker quotes and pricing to a third-party tool. How to test: Ask directly in the first 30 interviews; watch for procurement-confidentiality objections.

Risk flags

1. Liability / overclaim: If a buyer reads “buy” as “authenticated” and ships counterfeit parts into a medical or automotive product, the reputational and legal exposure is severe. Product language and contract terms must be conservative from day one. This is the risk that could actually kill the company.
2. Data dependency: Reliant on distributor and part-data APIs (Nexar/Octopart, DigiKey) for pricing baselines. Terms or pricing changes on those APIs directly hit the core feature.
3. Weak moat: Defensibility scored 3/10. A funded incumbent could bolt this onto an existing lifecycle product in a quarter. The bet is that they won’t bother with a segment this small, which is a real but unglamorous bet.
4. Episodic demand: Panic-bought tools churn. Addressed by the watchlist, but unproven.

14. Structured verdict

Score:                  72/100
Verdict:                GO
Confidence:             Medium
Best-fit builder:       Technical founder paired with an electronics procurement veteran
Time to revenue:        6–10 weeks
Capital to launch:      $8–12K / ₹7–10L
Top 3 assumptions to validate first:
  1. Open-market buy frequency ≥4/year per buyer — 30 buyer interviews
  2. Verdict agreement ≥70% vs experienced buyers on 25 blind historical offers
  3. 90-day retention on emergency-acquired accounts — watchlist conversion
Kill criteria:
  - Abandon if <70% verdict agreement with experienced buyers in blind testing — the core product is then unreliable and unfixable by UX
  - Abandon if median open-market buy frequency is <2/year — it's a one-off purchase, not a subscription
  - Abandon if >50% of interviewed buyers refuse to share broker quotes with a third-party tool

Confidence is Medium, not High, for one specific reason: I have solid industry-level data on the market and the risk rates, and a validated price band from a live competitor, but I could not source verbatim quotes from buyers describing this exact decision moment. The 30-interview sprint below exists to close that gap before anyone writes code.

15. Next step — 1-week validation sprint

• Day 1–2: Build the buyer list from public EOL/PDN notices published in the last 6 months. Book 30 calls with buyers at 20–300 person OEMs and CMs.
• Day 3–4: Run the interviews. Two hard numbers only: how many open-market buys in the last 12 months, and how they decided on the last one. Collect real broker quote emails from anyone willing.
• Day 5: Blind-test — take 25 real historical offers with known outcomes and have three experienced buyers verdict them independently, then compare against a hand-run version of the triage logic.
• Decide go / no-go on: median open-market buys ≥4/year across 30 interviews, AND ≥70% verdict agreement in the blind test. Both must clear. Either one failing kills it.

Falsifiable: two numbers, both measurable in a week, either of which sinks the idea.