ApexLens — Strategy & Go-to-Market Rundown

100%

The accuracy contract. Players expect perfect accuracy — the product only reports a field it can verify to ~100%, and suppresses or flags anything uncertain behind evidence + confirmation. EST

$0 / match

Local core cost. CV/OCR on the user's machine — zero marginal cost to serve. MODEL

<$0.25 / match

Hosted ceiling. Vision-LLM gated to hard fields only. DOC

1,653

Labeled corpus frames across 7 matches + focused fixtures — raw material for the tuning flywheel. DOC

Executive summary

Six facts that summarize the entire strategy — product, wedge, economics, motion, the core assumption, and the state of evidence.

0º

Product: local-first web app + CLI turning any recording into an evidence-backed, correctable fight review.

0º

Wedge: every stat is a claim with an evidence frame — not a black-box OCR dashboard.

0º

Economics: zero-cost local core, LLM gated to hard fields, >60% margin target on paid.

0º

GTM: free community seed → coach/creator program → monetized Pro pilot by day 90.

0º

Core assumption: players expect 100% accuracy — the product only reports a field it can verify, and suppresses or flags anything uncertain. EST

0º

Evidence: analysis-only; revenue, pricing and gates are estimates/models, not validated.

Engagement signal — 10 min of gameplay (sample)

0:002:305:007:3010:00

Motion/red/sharpness fused into a weighted combat score; windows above a dynamic threshold become engagements. DOC

Detector trust ladder — precision > recall, by design

Signal
F1 .14

.136

Accept P
F1 .53

.525

Precision

.963

Recall

.361

raw signal-only accepted combat precision-first

Shipped detector is deliberately conservative: what it reports is trustworthy (96% precision); reporting more accurately is the tuned-CV work in progress — consistent with the 100%-accuracy contract. DOC

Core assumption — players expect 100% accuracy

Accuracy is the product contract, not a nice-to-have. This deck is built on one operating assumption: players assume every number is correct, so the product reports only what it can verify to ~100% — confidence-gated, precision-first, "null over guess" — and anything uncertain is suppressed, flagged, or gated behind evidence + human confirmation. This assumption is the load-bearing reason for the trust architecture (evidence frames, verify-before-report, and the correction→tuning flywheel) that runs through every section below.

"Upload (or point at) your Apex gameplay recording — get a trustworthy, evidence-backed fight and stats review with confidence on every number, and the ability to correct what the machine got wrong." — Canonical positioning sentence EST · source: strategy 01-candidate-target

The one-liner an agency would lead with

ApexLens competes with the manual VOD scrub, not with Tracker.gg. Trackers give scores; overlays are live and compliance-risky; nobody offers an offline, evidence-backed, correctable post-match review of your own recordings — built on a 100%-accuracy contract. That gap is the entire go-to-market.

The opportunity

What problem is being solved, for whom, and why the existing toolkit is structurally unable to solve it.

What the product is Definition

A local web app + CLI that ingests Apex Legends video — single match or long stream with auto-detected boundaries — and estimates advanced stats with OpenCV + Tesseract, plus an optional hosted vision-LLM layer for hard fields. Output is structured per-match JSON with value, confidence, provenance, status per field, and a review UI with timelines, charts and click-to-evidence frames — governed by the rule that only verified fields surface. DOC

Match boundariesPlacement & summary statsEngagement windows Weapon timelineRing progressionLive HUD Activity mixDamage taken & hit markers

Founder's itch, converged across three prior attempts

Three successive experiments (apex-legends v0 → apex-stats active product → nextgen planning) independently converged on the same unit of value: a post-match fight/stats review from ordinary recordings, with "transparency over guessing" as a through-line. The strategy is distilled from what was already built, not invented. DOC

Magnifying lens over a gameplay timeline — the wedge rendered — the wedge — lens over timeline

Why now / why this wedge Market logic

The real incumbent

Manual VOD scrubbing

Players and coaches already record with OBS/Shadowplay and review deaths/fights by hand — slow, tedious, easy to miss evidence. ApexLens automates exactly this job. EST

Structural gap

Telemetry lacks fight context

Tracker/API products supply aggregates but not "what did I actually do in this fight, with which weapon, for how long." DOC

Compliance moat

Overlays are risky

Memory-reading and unofficial endpoints carry EA User-Agreement exposure. Video-only is safe by construction — a real advantage. DOC

Input freedom

Any recording works

No API dependency, no whitelist, no scraping permission — a recording is the only input needed. DOC

attempts → one converging thesis

Every prior attempt re-scoped "extract HUD → detect combat → produce stats"; consolidation now picks one implementation per concern and keeps the rest as legacy. DOC

Target market & customer segments

Three tiers of audience, sequenced deliberately: monetize convenience for professionals, anchor demand in self-improving players.

Primary · v1 · free seed

Self-improving Apex players

Solo or small-scrim PC players who already record and review deaths/fights manually or with Tracker/Overwolf tools. Motivating questions: "what did I actually do each fight," "which weapons when," "am I fighting too much / looting too long." They want evidence-grounded stats — accurate or not shown. EST

already recordsalready reviewsexpects accuracy

Secondary · expansion · Pro tier

Coaches, creators & scrim managers

Need truthful per-player match reviews and fight timelines across many clips: batch ingestion, per-player reports, shared scrim review. This is the segment that pays for convenience (cloud/batch) and becomes the distribution channel to players. EST

batch needper-player reportspays for convenience

Tertiary · later · optional

Other battle-royale titles (Fortnite · VALORANT · Warzone)

The engine is title-aware via HUD configuration; the same verified video-stats engine could expand later. Explicitly out of scope for v1 — do not build a game-agnostic platform at launch. EST

future titles

Agency caution

Primary segment is not validated. "Self-improving players post-match review" is inferred from on-disk docs, not from interviews. The cheapest validation is the GTM itself (free seed + demo content); no numeric demand evidence exists yet. EST

Positioning & unique competitive proposition

The defensible differentiator is not extraction quality — it is the trust model around extraction, now anchored by a 100%-accuracy contract.

Glass shield with evidence brackets — trust — every stat is a claim — evidence included

"A kill/death/stat reviewer that treats every number as a claim, shows you the evidence frame it came from, and lets you correct it — not a black-box OCR dashboard." — Unique Competitive Proposition DOC

Differentiators — ApexLens vs generic OCR/tracker tools

Verify-before-report (≈100% on surfaced fields)9.8/10

Evidence & confidence per field9.6/10

Human correction feeds tuning9/10

Local-first privacy9.4/10

Compliance safety (video-only)9.8/10

Agency-scored against the generic alternative on the dimensions that matter for this buyer. EST

Proof points already on disk Leads, not market proof

Implemented detector set

Boundaries, summary stats, engagement/signal, HUD combat, weapon OCR, ring tracking, live HUD, per-match calibration — running in the merged repo. DOC

Tuning machinery exists

Supervised-tuning + active-labeling in code, with a 1,653-frame labeled corpus across 7 matches plus focused fixtures. DOC

Architecture planned

Evidence/confidence/human-correction pipeline and the compliance boundary are designed in the merged plan docs. DOC

None of the above is market proof — these are engineering proofs the wedge is buildable. Revenue/competitive claims remain EST (no web research was available at strategy time).

Product scope: MVP & beyond

A staged scope built from components that already exist across the merged repos — the MVP is an integration and polish job, not a greenfield build.

P0 · launch · the core loop

ingest → analyze → review → correct

Video ingest — upload or point at MP4s; SHA-256 manifest; proxy clip + sparse frames
Core detectors — boundaries, placement/summary, engagement, weapon timeline, ring
Per-field value + confidence + provenance + status output schema
Review UI — match summary, timeline/charts, click-to-evidence, JSON/CSV export
Verify-before-report + human-correction feeding the active-learning labeler
Local-first end to end — no accounts in local mode

P1 · short post-launch · pro features

Batch / multi-match review for coaches & creators, per-player reports
Supervised-tuning dashboard — F1/precision/recall drill-down, train/val split, param search vs ground truth
HUD calibration persistence + season/patch versioning in the data model

later & optional: active-learner priority queue · consent-based Overwolf · multi-title HUD configs

Out of scope for v1 — the moat is a boundary, not a plan

🚫 Live in-game overlay & memory reads — cheating-adjacent features kill the compliance moat and carry EA UA risk. DOC

🚫 Hosted multi-tenant SaaS at launch — local-first first; paid cloud tier is a later, deliberate decision. EST

🚫 Surfacing unverified stats — accuracy contract: nothing uncertain is displayed as fact. EST

one lens, one title — for now

MVP success criteria (foundational alpha)

① Analyze a 15–45 min local recording to a reviewable match summary in reasonable wall-clock. ② Every user-facing metric carries confidence + evidence the user can open. ③ Reported fields hold to the verification bar — unverified data is suppressed, active labeling reduces frames needing human review. ④ A user can correct a stat and the correction persists into ground truth for tuning. DOC

North-star — business

Trusted corrections × matches reviewed

A review habit that compounds: corrections → ground truth → better detectors → faster review → more reviews. DOC

North-star — MVP

% of matches with ≥1 correction or export

Proves trust + habit formation; paired with match-review time saved vs manual browsing. DOC

Business model & pricing

A free-anchored, convenience-first monetization model: the zero-cost local core is the funnel; value is captured on batch, cloud, and professional needs.

Free · local

Local analysis on your machine
Full JSON / local export
Community support

Zero cost to serve — the funnel DOC

anchor

Pro

$6–10 /mo

Batch processing
Hosted cloud analysis
Per-player reports
Priority tuning access

Value on convenience + professional workflow EST

Coach / Team

Custom

Multi-player & shared scrim review
Team dashboard

Highest willingness-to-pay; priced per need EST

Pricing principle

Anchor: free local core. Never charge for trust — charge for convenience (cloud/batch) and professional needs. Accuracy is non-negotiable at every tier. DOC EST

Pricing is explicitly flagged EST — "refine with user" in the source strategy. No pricing experiments or willingness-to-pay work has been done.

Cost model & unit economics

The economics are the strategy's quietest strength: a product whose base unit costs nothing to serve, with an explicit per-match ceiling on the paid tier.

Cost per match — the ladder

Local core (CV/OCR on user machine)$0.00

Hosted vision fallback (hard fields only)<$0.25

Exploratory dev budget (total, one-time)<$20

Build to MVP (already sunk, 3 repos)~2–4 wk

Local mode costs nothing incremental to serve; only the convenience tier carries model cost. MODEL DOC

Pro tier unit economics — per user / month

LLM
cost

$4.50

Rev
(mid)

Contrib
(low)

$1.50

Contrib
(high)

$5.50

marginal cost revenue @ $8/mo gross contribution

Assumptions: ~30 matches/mo, ~60% needing hosted fallback at $0.25 — model inputs, not measurements; the two variables that decide margin. MODEL

Target margin

>60%

Gross margin on Pro once LLM usage is gated to hard fields. MODEL

Free tier economics

Local-mode users cost ~$0 to serve — the free tier is effectively free to operate, so the funnel has no bleed. MODEL

Agency read

Directionally credible, numerically unproven. The design has the right instinct (gate the LLM, keep local default); re-derive gate-specific economics from real usage before scaling paid acquisition. MODEL

Go-to-market strategy

Community-led, founder-shaped, content-powered — with paid acquisition deferred until the trust loop is proven.

WHEEL

CorrectionsUsers flag what the machine got wrong

Ground truthEvery correction is a labeled sample

Better detectorsSupervised tuning raises verified accuracy

Faster reviewTrust increases; review effort shrinks

Glossy 3D flywheel loop — north-star: trusted corrections × matches reviewed

Positioning & channel plan Sequenced

"Your recordings, reviewed." Against hand-browsing VODs (tedious) and telemetry-only trackers (no fight context, no evidence). The emotional emphasis is trust + evidence — the anti-black-box stat tool for a privacy- and compliance-conscious playerbase. EST

Phase	Channel	Mechanism
Seed (free)	r/apexlegends · creator discords · coach communities · scrim groups	Short demo video: upload clip → fight timeline + evidence. Founder-led distribution.
Content-led	Shareable per-match breakdowns	"How many seconds do you actually spend fighting?" — UGC fuel, self-similar across matches.
Program	Creator / coach program	Free tool for creators & coaches reviewing many players' clips — they become the channel to players.
Paid (later)	Focused Reddit / YouTube	Apex self-improvement audiences; demo-to-download. Only after trust loop + demand signal.

Why this motion is right

Every channel converts an existing behavior (record + review) into product usage, and each user's corrections improve detection for everyone — a compounding, community-flavored loop that fits a solo founder's budget. Paid spend is deliberately last because undifferentiated ads can't sell a trust product before trust exists. EST

Competitive landscape

Four incumbent categories, one whitespace. Rankings and estimates are flagged; nobody was interviewed and no web research was run at strategy time.

← live / overlay offline / review →

Tracker / API tools

Overwolf + Game Events

Manual VOD review

DIY CV / OCR

ApexLens — the gap

Positioning is schematic, from the strategy's competitive analysis. EST

Category	What they do	Competitive relationship
Tracker / stat aggregators Apex Legends Status · Tracker.gg	Aggregates + live-ish stats, telemetry/API-backed; Tracker Network requires whitelisting and forbids scraping	Not a direct build — they score from data, not fight videos. Complementary on stats, absent on fight review.
Overwolf + Apex Game Events Provider	Consent-based in-game surface: damage, kill feed, location, match summary; live/overlay oriented	Overlapping on "what happened," but live, PC-only, dashboard-angled. Our video-first approach is complementary/competing.
Manual VOD review (the real incumbent)	Players and coaches scrub OBS recordings by hand	The default alternative. Slow and easy to miss evidence — the behavior ApexLens automates.
Generic CV/OCR DIY	Tesseract/OpenCV exist as libraries	No ready-made Apex fight-review-with-evidence product — building blocks, not competition.

The whitespace

No mainstream tool gives per-field confidence + evidence frame + human-correction loop on ordinary recordings. Trackers tell you scores; manual VOD review is expensive; overlays are live and compliance-risky. ApexLens sits in the gap: offline, evidence-backed, verify-before-report post-match fight review. EST — the single claim that most needs community validation.

Risk register

Seven tracked risks with mitigations, plotted by likelihood × impact — and the two that actually matter rolled up. EST

Low impact

Med impact

High impact

High likelihood

—

Season/patch HUD churn versioned calibration

Consolidation broke code subtree preserve + legacy

Med likelihood

—

Cost blowup on paid budgeted & gated

Accuracy below expectation · No market fit accuracy contract + gates + free seed

Low likelihood

—

EA ToS · privacy video-only moat · local-first

Roll-up — the one risk that decides everything

Kill risk — the 100%-accuracy contract: players assume every number is correct. If detection can't reach near-100% on the fields we do report — or allocates excessive human review so corrections don't reduce effort — the tool fails its core assumption and becomes a worse tracker. The mitigation is architectural: report only what is verified (confidence-gated, precision-first, "null over guess"), surface every field with evidence, and let corrections feed supervised tuning so the reported set keeps growing without eroding trust. This exists in the repo today. DOC EST

Kill-risk anatomy

Verify-before-report on every fieldbuilt

Confidence + provenance + evidencebuilt

Human correction → training loopbuilt

Labeled ground-truth corpus1,653

"Null over guess" philosophybuilt

Technical foundation & moat

What exists today (in code) that turns this strategy from a slide deck into a shippable wedge — and what the moat actually is.

Python 3.12 + uvFastAPI + async jobsOpenCV + NumPy Tesseract OCRffmpeg / ffprobeGemini via OpenRouter Vite + ReactSHA-256 manifests + evals

Ground-truth corpus — 1,653 labeled frames

1,653frames

COMBAT16.5%273

MOVEMENT34.4%568

LOOTING10.6%175

DOWNTIME5.3%87

NON_GAMEPLAY32.3%534

RING_DAMAGE1.0%16

7 matches + focused fixtures (ring, damage, weapons, squads, ammo, accuracy, summary) — the raw material for the tuning flywheel. DOC

Corpus growth — before & after expansion

Baseline
(2 matches)

548

Expanded
(7 + fixtures)

1,653

3.0× more labels, incl. negatives that harden false-positive control — the foundation of verified accuracy. DOC

What the moat actually is (honest order)

Tuned detector + labeling loopcompetitors must rebuild the corpus and tuning discipline

Trust UX — verify-before-reportevidence + correction is a product-surface moat

Compliance boundarylegal where overlays aren't; extraction code alone is replicable

Load-bearing technical risks (carried from the docs)

① HUD/OCR reliability across resolutions & seasons → Gemini calibration + tuning + confidence gates. ② Frame-sampling vs full-scan cost tradeoff → local/zero-cost default, LLM gated to hard fields. ③ Detector accuracy without a neural model → treat the tuned CV pipeline as supervised ML. ④ Merge-dedupe risk → one implementation per concern, legacy preserved. DOC

90-day plan & go/no-go gates

Three phases, three gates, one decision point: whether to scale paid or stay free/local-plus-donation. Consolidation is already complete.

Pre — ConsolidateWeeks 0–1 · DONE
Single apexlens repo from three sprawled projects; detectors/CLI deduped; pytest green; strategy package promoted into docs/strategy/.
Phase A — Foundational alphaWeeks 2–4
Ship the local MVP (upload → analyze → review UI with evidence + corrections) to a private group of 5–10 Apex players/coaches. Measure time-to-review vs manual and correction rate.

GO/NO-GO 1 · clean reviewable summary on core fields + ≥1 trusted correction used · reported fields clear the near-100% verification bar (nothing unverified surfaces)
Phase B — Community seedWeeks 5–8
Public free local release to Reddit / discords / creator circles with demo content ("your fight uptime"). Pilot 1–3 coaches/creators for batch/per-player demand signal.

GO/NO-GO 2 · community traction (active review sessions/wk) + labels flowing and tuning showing verified-accuracy gains
Phase C — Monetization pilotWeeks 9–13
Launch the Pro tier (batch / cloud / per-player) to the coach & creator cohort; validate willingness-to-pay.

GO/NO-GO 3 · Pro cohort retention + demonstrated margin → decide: scale paid, or stay free / local-plus-donation

Gate 1 · week 4

Core loop usable + corrections work. Proposal: ≥80% of alpha matches produce a clean reviewable summary; ≥50% of alpha users make ≥1 correction; reported fields verified. MODEL

Gate 2 · week 8

Community traction + labels → tuning. Proposal: ≥100 active review sessions/wk and verified-accuracy gains on the same corpus. MODEL

Gate 3 · week 13

Monetization pilot validates. Proposal: ≥60% Pro-cohort week-4 retention and ≥50% gross margin — scale only if both clear. MODEL

Numeric gate thresholds are proposals (§14), explicitly deferred to the founder in the source strategy.

KPIs & measurement

The scoreboard: the accuracy contract, a north-star, phase-level proof metrics, and the economics to watch.

Metric	Level	Why it matters
Reported-field accuracy ≈ 100% (verify-before-report)	CONTRACT	The core assumption: only surface fields verified to ~100%; everything uncertain suppressed/flagged. The single most important scoreboard line. EST
Trusted corrections × matches reviewed	NORTH-STAR	Compounding flywheel: corrections → ground truth → better detectors → faster review → more matches DOC
% of matches with ≥1 correction or export	MVP	Trust + habit proof; the engaged-review rate DOC
Match-review time saved vs manual	VALUE	The actual exchange of value — what Pro pricing should be anchored to DOC
F1 / precision / recall per detector	TUNING	Directly measures the kill-risk; must trend up as labels flow DOC
Active-review rate (sessions/wk)	GATE 2	Community traction before monetization EST
Pro cohort retention + margin	GATE 3	Willingness-to-pay and unit economics before scaling paid EST
Hosted-fallback ratio & $/match	ECONOMICS	The margin sensitivity variables — measure from day one of Pro MODEL

Trust engine

per-detector F1 trend = the kill-risk meter; must rise as labels flow. DOC

Value meter

hrs

match-review time saved per week vs manual — the anchor for pricing. DOC

Economics radar

$/m

actual hosted-fallback ratio and cost per match on real Pro usage. MODEL

Agency recommendations

What a paid strategy desk would tell you to do on Monday — the value-add on top of the written strategy. All proposals, clearly marked.

Encode the 100%-accuracy contract before anything ships EST

Because players expect perfect accuracy, make "verify-before-report" a hard product rule from day one: a field is surfaced only when it clears the confidence/verification bar; everything uncertain is suppressed or flagged; reported fields carry evidence + a one-tap correction. Treat "reported-field accuracy ≈ 100%" as the number-one KPI and publicly own that unverified stats are never shown.

Set the numeric gates now, before building more MODEL

G1: ≥80% of alpha matches produce a clean reviewable summary AND ≥50% of alpha users make ≥1 correction, with reported fields verified. G2: ≥100 active review sessions/wk and verified-accuracy gains on the same corpus. G3: ≥60% Pro-cohort week-4 retention and ≥50% gross margin — scale paid only if both clear.

Sell the time saved, not the stats EST

Anchor pricing to match-review hours saved per week (which the MVP measures). If a coach saves 3–5 hrs/wk, $6–10/mo is trivially priced — test $7/mo as the midpoint and let the coach tier carry the margin.

Validate the whitespace + accuracy claim before the monetization pilot EST

Five 30-minute coach conversations + one community post ("would you trust verified-only stats? what would you pay?") is cheaper than building batch features nobody wants. Capture objections verbatim.

Protect the compliance + accuracy moat in every public word EST

Lead with "video-only, no memory reads, data stays on your machine, and we only show verified numbers" in the demo and landing copy.

Keep the flywheel honest: measure correction latency MODEL

Instrument "time from correction to next review" and "frames needing human review per match" from day one. If either degrades as the corpus grows, the loop is broken and nothing else matters.

Founder-led GTM is correct — make it deliberate EST

The community channels assume the founder is credible inside the Apex self-improvement scene. If not yet true, the first 90 days should include recording and publishing your own reviews with the tool — the product is also the marketing.

Sequence paid features by demand signal, not by roadmap EST

Batch and per-player reports are P1 assumptions. Let the 1–3 coach/creator pilots in Phase B vote with their workflows first; build and price the two features they ask for, rather than pre-building the whole Pro tier.

Appendix — sources, method & caveats

Evidence legend

DOC — grounded in repository code, docs, or the on-disk strategy package (verifiable in-repo).
EST — judgment call or estimate; no market data behind it.
MODEL — derived arithmetic under explicit assumptions; sensitive to those assumptions.

Method & caveats

Analysis-only: synthesized from the on-disk package and repo — no market research, no interviews, no pricing experiments, no competitor revenue data.
At strategy-write time web search was unavailable; competitor claims are from on-disk context (EST).
Numeric gates and pricing were deferred to the founder; §14 targets are proposals.
Video signals are the raw data source and are inherently estimates. Because players expect 100% accuracy, the accuracy contract governs what is ever reported: only verified/near-certain fields surface, and this honesty must remain in all public comms (never present an estimate as telemetry).

Source documents

docs/strategy/01–09 — customer target, UCP, MVP scope, GTM, cost model, technical stack, risks, landscape, 90-day plan
docs/strategy/strategy-summary.md — 1-page synthesis
docs/strategy/style-guide.md — the 7-model VLM style + IA guide this build follows
docs/output-schema.md, docs/openrouter-model-selection.md, optimization/evaluation_report.md, optimization/IMPROVEMENT_LOG.md

Bottom line

ApexLens has a credible wedge, a real moat, and the rare luxury of a near-zero-cost local product. What it does not yet have is evidence that anyone will pay — and it cannot exist unless reported fields hold to the 100%-accuracy contract. The 90-day plan is built to produce both under ~$20 of model spend: run it, set the gates, verify before you surface.