Build Your Own Vision Model. Save Costs.
13 tasks are free. This model does the 6 that actually need judgment — the 6 strictly-vision-LLM tasks out of 20 a frontier model like Gemini covers, just 30.0% of the total. Own the rest outright. This is the complete build plan: architecture, data, training, API, deployment, cost, latency, reliability, security, and the decision framework.
Sheet 01 · The Trap
Why Founders, CEOs, and Developers Keep Paying for Math
Nobody Told You Where the Line Is — Because It Isn't in Their Interest To
Frontier vendors' own documentation markets breadth, not fit. Google's Gemini docs say the model can "reduce the need to use specialized ML models" — true, and exactly the message that keeps every call, even the free ones, routed through a paid endpoint. No vendor publishes the opposite guidance: "you don't need us for this." That guidance would cost them revenue, so it doesn't exist in their docs. It exists in this one.
This system covers 30% of what a frontier vision LLM like Gemini can do — and that 30% is not a limitation, it's the exact slice this pipeline gets billed for that actually needs judgment. The other 70% — OCR, video, dialogue, multi-image reasoning, translation, and more — covers real use cases, just not this one. Knowing that difference is the entire wake-up call.
How the Trap Actually Works
Why This Specifically Catches Founders and CEOs, Not Just Engineers
What Waking Up Looks Like
Sheet 02 · Objective & Scope
What the System Needs to Do, and Why a General Endpoint Doesn't Fit It
The Bet This Dossier Makes
A small number of frontier labs do not get to decide what your product costs, how fast it moves, or where your users' photos end up. Every API call you send them for a task a small model can learn is a piece of that decision handed away for free. Own the judgment. Rent nothing you don't have to.
The task in question is narrow, repeatable, and judgment-based: given a photograph and the context it was taken in, decide how visually and narratively significant it is, and produce short, grounded language describing it. That is the entire job.
A narrow, well-defined task is exactly the case where a small, purpose-trained model outperforms a large general one on the metric that actually matters — consistent judgment against a fixed rubric — while costing a small fraction to run at volume.
Explicitly Out of Scope for This Phase
Sheet 03 · Glossary
Terms Used Throughout This Dossier
Not knowing these terms yet is exactly why frontier labs can charge what they charge. Ten minutes with this page and the gap closes for good.
| Term | Meaning |
|---|---|
| VLM | Vision-language model — takes an image (and usually text) as input, produces text as output. |
| Forward pass | One complete run of input through the model to produce output. Cost and latency scale with the number of forward passes. |
| Embedding | A fixed-length numeric vector representing an image's content, used for similarity comparison. Not readable by a text-only model. |
| Quantization | Compressing model weights (e.g. 16-bit → 4-bit) to cut memory and speed up inference, at a small accuracy cost. |
| Token | The unit an LLM processes text in. API pricing is almost always per input/output token. |
| Distillation | Training a small model to reproduce a larger, more expensive model's outputs — used to bootstrap labeled data cheaply. |
| Fine-tuning | Continuing training on an existing pretrained model's weights for a new task. Faster than training from scratch; less architectural ownership. |
| LLM-as-judge | Using a language model to score or rank other outputs against a rubric. |
| Rubric | The fixed criteria a model (or human) uses to score consistently across many items. |
| Cold start | The delay before a scaled-to-zero GPU serves its first request after sitting idle. |
| Throughput vs. latency | Throughput = requests handled per second in aggregate. Latency = time for one request to complete. Optimizing one can hurt the other. |
| Batch inference | Processing many images in one queued request for efficiency — trades per-item latency for total throughput and lower cost. |
How to Read This Dossier Depending on Who You Are
Start here, then Sheets 02, 09, and 29. You almost certainly don't need to read the training or architecture sheets to make your first decision.
Jump to Sheets 29 and 26 first — your volume is probably too low for a full build to pay off. Then Sheet 28 for the honest comparison.
Sheets 24, 21, 22, 23, and 26 cover the operational depth. The rest of the dossier is the system those sheets assume.
Sheet 04 · Design Reasoning
Working Back from the Task to the Smallest System That Can Do It
Break the required capability into what it actually consists of, and each piece points to a different, buildable component — rather than one large model asked to do everything at once.
Decomposing the Capability
- —Technical quality — blur, exposure, framing. Deterministic signal processing. No learned model required.
- —Redundancy / similarity — embedding distance between images. A small pretrained encoder, not a language model.
- —Contextual significance — the one genuinely open-ended judgment call. The only piece that needs a trained reasoning model.
Why Train Rather Than Call an Existing Endpoint
Why Train Rather Than Only Fine-Tune an Open Checkpoint
Sheet 05 · System Architecture
Three Components, Each Doing One Job
~85M params
pixel-shuffle + linear
~135–360M params
structured JSON
Vision Encoder
A standard Vision Transformer, trained to represent image content as a sequence of patch embeddings. Consumes the raw photo; produces no language, only structured visual features.
Modality Projection
A compact bridge — a spatial compression step followed by a linear layer — that reshapes visual features into the same embedding space the language decoder reads natively. This is also why an external LLM-as-judge can't consume this model's embeddings directly.
Language Decoder
An autoregressive transformer decoder that reads the projected visual tokens alongside a short context instruction and generates the structured output as text.
Sheet 06 · Extracted Information
Exactly What the Model Reads Out of a Photo
Every field below is produced by a single forward pass through the model for one image and one context tag. Nothing here is identity recognition — people are counted, never identified.
| Field | Type | What It Captures |
|---|---|---|
| score | float, 0–1 | Overall significance / curation-worthiness |
| context_alignment | float, 0–1 | How well the image matches the supplied context tag |
| caption | string | Short, factual description of what's in the frame |
| rationale | string | Why the score landed where it did, grounded in context |
| tags | string[] | Notable subjects and keywords detected in the scene |
| people_count | integer | Number of people present — count only, no identity |
| narrative_fragment | string | A short narrative-style sentence for downstream editorial use |
What Is Deliberately Not Extracted
Sheet 07 · Coverage vs. Frontier
30.0% of What a Frontier Vision LLM Can Do — On Purpose
This Was Never Trying to Be Gemini
A frontier model like Gemini 3.1 Pro does far more than curate photos — OCR, document understanding, video, dialogue, translation, object localization, and more. Against a 20-capability baseline representative of that surface area, this system implements exactly 6. That's not a shortfall. It's the whole point: pay for the 30.0% this pipeline actually uses, not the other 70.0% sitting idle in every API call.
The Calculation
| What This System Covers | API Field |
|---|---|
| Contextual significance / curation-worthiness scoring | score field |
| Context-to-image alignment scoring | context_alignment field |
| General image captioning | caption field |
| Judgment rationale generation | rationale field |
| Open-vocabulary scene / subject tagging | tags field |
| Narrative-style text generation grounded in an image | narrative_fragment field |
Sheet 08 · The Other 70%
What Every Uncovered Capability Would Take to Build
The 14 rows below are what separate this system from a general-purpose frontier vision LLM. None of them are needed for this pipeline — but the honest cost of closing each gap is listed anyway, so the scope decision is an informed one, not an assumed one.
| General Vision-LLM Capability | What's Needed to Build It |
|---|---|
| Open-ended free-form visual question answering | Free-form Q&A training data + a decoder not constrained to fixed JSON fields |
| Object detection & bounding-box localization | A localization head + box-annotated training data (COCO-style detection, not just captions) |
| OCR / text extraction from images | Dedicated text-recognition training data and typically higher input resolution |
| Document, chart & table understanding | A layout-aware encoder + a structured-document training corpus |
| Spatial & depth reasoning | Depth- or geometry-annotated training data |
| Multi-image comparison / reasoning across a set | Cross-image attention — this architecture takes one image per forward pass — plus interleaved multi-image training data |
| Video understanding (temporal reasoning) | A temporal/frame-sequence encoder and a video training corpus — explicitly out of scope |
| Multi-turn image-grounded dialogue | Conversation-history handling and dialogue training data — explicitly out of scope |
| Instruction-following image-edit description | Paired before/after edit-instruction training data |
| Aesthetic / quality critique as a standalone output | Dedicated aesthetic-critique labels — currently folded informally into rationale |
| Content moderation / safety classification | A separately trained, dedicated safety classifier — tags/caption are a signal, not a substitute |
| Facial identity recognition | Identity-linked training data — excluded on purpose for privacy, not a capability gap |
| In-image text translation | OCR as a prerequisite, plus translation training pairs |
| Open-set object counting (beyond people) | Counting-specific training data across arbitrary categories |
What Closing the Gap Would Actually Cost
Every row above adds its own training data, and several — multi-image reasoning, video, dialogue — add architectural surface area this design deliberately avoids. Building all 20 would mean re-deriving a general-purpose frontier model from scratch: the exact cost and dependency profile this dossier argues against. The 30.0% covered is the 6 capabilities this pipeline actually bills for — nothing rented, nothing idle.
Sheet 09 · Commercial Use Cases
What the Model Fields Enable in a Real Product
Every use case below is built directly from score, context_alignment, caption, rationale, tags, people_count, and narrative_fragment — no additional model output required.
Automated culling / curation
score
Highlight reel generation
score
Accessibility alt-text
caption
Auto-assembled photo stories
narrative_fragment
Search & filtering
tags
Editorial audit trail
rationale
Personalized recaps
score + tags
Photo-book auto-layout
score
Client proofing tools
score + rationale
Listing quality scoring
context_alignment
Social share recommendation
score
Context-mismatch flagging
tags + context_alignment
Common Thread
Every one of these is a judgment or ranking task, not a retrieval or transformation task. That distinction is what determines whether a use case belongs here or in the free tier.
Sheet 10 · Task Split
13 Tasks Are Free. 6 Need the Vision LLM.
Don't Estimate What You Can Count
Every task this pipeline needs is enumerated here — nothing is rounded. 13 of the 19 total tasks are solved for free, today, on commodity hardware. The other 6 require language-grounded visual judgment, and those 6 are exactly the strictly-vision-LLM tasks counted against Gemini's own capability set.
The Count
| Requires the Vision LLM — Task | Why Nothing Else Can Produce It |
|---|---|
| score | Judgment against a rubric, not a measurement |
| context_alignment | Relates image content to an arbitrary text label |
| caption | Open-ended language generation grounded in the image |
| rationale | Explaining a judgment in language |
| tags | Open-vocabulary content description |
| narrative_fragment | Free-form narrative language generation |
Sheet 11 · The Free Tier
The 13 Tasks That Never Touch the Model
Each one has a fixed, known answer format, so it's solved once as an algorithm and stays solved regardless of scale.
| Free, No Vision LLM — Task | How |
|---|---|
| Blur / sharpness detection | Laplacian variance — pure math |
| Exposure / contrast scoring | Histogram analysis — pure math |
| Duplicate / near-duplicate detection | Perceptual hashing — deterministic |
| Resize, crop, rotate, convert | Standard image libraries |
| EXIF metadata extraction | Direct file parsing |
| Thumbnail generation | Standard resizing |
| Color palette extraction | Pixel histogram — pure math |
| Red-eye reduction, white balance | Classical CV filters |
| Watermarking | Direct pixel compositing |
| Corrupt-file validation | File header checks |
| Panorama stitching | Classical feature matching, no learned model |
| Basic face detection & counting | Lightweight open-source detectors, not an LLM |
| Fixed-category object tagging | Small pretrained classifiers, not generative |
What This Buys You
Everything above runs entirely offline, at effectively zero marginal cost, on commodity hardware — no API call, no GPU inference server, no per-image billing of any kind. Every photo touches this tier; only the curation-worthy subset ever reaches the paid layer.
Sheet 13 · The Cost of Not Waking Up
$59,280 a Month — The Exact Price of Skipping the Free Tier
This Is What the Trap Actually Costs
At 3,000,000 images/month, routing all 13 free tasks through a frontier vision LLM instead of the free tier costs approximately $59,280/month — 6.2× more than the entire cost of the judgment task this pipeline actually needs, for work a Laplacian variance calculation and a file-header check already do for nothing.
The Calculation
Annualized, list price
$711,360
Annualized, batch discount
$355,680
vs. the judgment task that's actually necessary
6.2×
Sheet 14 · Worldwide Scale
$0.8B–$6.5B a Year, Worldwide — A Fermi Estimate, Not a Fact
This Sheet Is Different From Every Other Number in This Dossier
Every other calculation here counts something enumerable — fields, tasks, tokens, dollars at a stated volume. A worldwide figure can't be counted; it has to be estimated from published market sizes that disagree with each other by 5× or more, plus at least one real judgment call with no data behind it at all. Shown here anyway, labeled for exactly what it is.
| Input | Value | Confidence |
|---|---|---|
| Global annual spend on vision-capable LLM API calls | $3B–$15B/yr | Source data disagrees 5×+ |
| Share following the naive one-call-per-task pattern | 30%–50% | Not sourced — stated judgment call |
| Waste share of naively-routed spend | 86.1% | Exact — computed from Sheet 13 figures |
The Calculation
Sheet 15 · Hybrid Scoring
A Better Score Is a Composite, Not a Single Signal
The VLM score alone can't see the batch, so it can't penalize redundancy. Embedding distance alone can't see content, so it can't tell you which duplicate to keep. The most accurate score fuses both, gated by objective technical quality, with no additional LLM calls.
final_score = quality_gate × (α·vlm_score + β·context_alignment) × distinctiveness_factor
Quality Gate — Hard Multiplier, 0–1
Blur / exposure / composition from the classical CV layer. Not a weight — a cutoff. A technically ruined photo shouldn't outrank a clean one regardless of content: 0 if blur_score < threshold, else a soft ramp toward 1.0.
Base Score — VLM Output, Unmodified
α·vlm_score + β·context_alignment, straight from the model's fields. Start at α=0.7, β=0.3 — content significance dominates, context-fit is a secondary correction — and tune against the held-out reference set.
Distinctiveness Factor — Embeddings, Batch-Level
Cluster the batch by embedding cosine distance (~0.9 similarity = same cluster). Within each cluster, rank by base score and decay the rest: decay_rate^(rank_within_cluster). Singletons keep a factor of 1.0.
Sheet 16 · LLM-as-Judge
Where an LLM-as-Judge Still Earns Its Keep — and Where It Doesn't
An external LLM-as-judge has no trained alignment to this model's embedding space. A text judge can only ever consume the structured JSON output, never embeddings and never raw images.
Call Ordering That Maximizes Use of the Vision Model
What Stays on the Self-Hosted Model
The One Job Left for an LLM-as-Judge, If Kept at All
Net Effect
Token-billed LLM calls drop from once-per-image to at most once-per-batch, and only for the narrow slice of judgment — cross-item narrative balance — that the vision model was never positioned to do. Every call you don't make is a call nobody else can meter, throttle, or bill you for.
Sheet 17 · Training Data
One Public Foundation, One Bootstrapped Layer
General Grounding — Public, Freely Licensed
Representative Photo Sample — For Bootstrapping
Task-Specific Judgment Layer — Bootstrapped by Distillation
Continuous Refinement — After Launch
Sheet 18 · Training Plan
Three Passes, Each Validated Before the Next Begins
Baseline Reproduction
Train the full architecture on general-grounding data only; confirm it reaches known reference accuracy on a standard visual benchmark before any task-specific work begins.
Blended Fine Pass
Continue training on roughly three-quarters general data, one-quarter bootstrapped task data — evaluated against agreement rate with reference judgments held out from training.
Iteration on Disagreement
Cases where the model diverges from the reference judgment are categorized and used to target additional bootstrapped examples for the next pass.
Approach
Full training, staged
Compute
Single high-end GPU, days not weeks
Tracking
Held-out eval each pass
Primary metric
Agreement with reference judge
Sheet 19 · API Endpoints & Payloads
POST /v1/assess
Request
{
"image": "https://... or base64",
"context_tag": "trip",
"image_id": "img_0192",
"metadata": {
"captured_at": "2026-06-11T14:22:00Z"
}
}Response · 200
{
"image_id": "img_0192",
"score": 0.87,
"context_alignment": 0.93,
"people_count": 2,
"tags": ["coastline","sunset"],
"caption": "...",
"rationale": "...",
"narrative_fragment": "...",
"model_version": "poc-0.3"
}POST /v1/assess/batch · GET /v1/jobs/{id}
Batch Request
{
"context_tag": "trip",
"items": [
{"image_id":"a","image":"url"},
{"image_id":"b","image":"url"}
],
"callback_url": "https://..."
}Job Status · 200
{
"job_id": "job_7f3a",
"status": "processing",
"progress": {
"completed": 1400,
"total": 3600
}
}GET /v1/health → { "status": "ok", "model_version": "poc-0.3" } · Batch accepts up to 3,600 items per job.
Sheet 20 · Deployment
Where Each Piece Runs
This is the moment the model stops living on someone else's infrastructure under someone else's terms of service. Every line below runs where you say it runs.
Serving Stack
GPU Hosting — Options Considered
Sheet 21 · Reliability & Operations
What Breaks in Production, and How This Is Designed to Fail Safely
Rate Limiting & Backpressure
Retries & Idempotency
Model Versioning & Rollback
| What to Actually Monitor | How |
|---|---|
| Agreement-rate drift | vs. reference judge, per Sheet 18 |
| Latency percentiles | p50 / p95 / p99 |
| GPU utilization & queue depth | autoscale trigger health |
| Cost per image, trailing 7-day | catches storage/CDN creep |
Sheet 22 · Security, Privacy & Data Ownership
Where the Photos and Their Derived Data Actually Live
Self-hosting is the privacy lever this whole design pulls — data never leaves the environment, every call, by design. That default doesn't remove the need for basic data governance; it just means the governance decisions are entirely this team's to make.
What the Model Itself Withholds
| Encryption & Access | Implementation |
|---|---|
| In transit | TLS on every API call, internal and external |
| At rest | Object storage server-side encryption, zero-egress tier |
| API access | Per-key auth with rotation; scope keys to context_tag if multi-tenant |
Compliance — Not Legal Advice
This dossier is planning-grade engineering guidance, not a compliance determination. Photos of people trigger data-protection regimes (GDPR, CCPA, and similar) that vary by jurisdiction and by whether minors are depicted; involve legal/compliance review before launch if the pipeline handles user-uploaded photos at any real scale.
Sheet 23 · Peak Load Capacity
What the Fleet Can Absorb at Once
Throughput Model
| Scale | Capacity |
|---|---|
| 4 GPUs (reference) | ~80 req/sec, ~15 min clear for 72K backlog |
| 8 GPUs | ~160 req/sec, ~7.5 min clear |
| 20 GPUs | ~400 req/sec, ~3 min clear |
Sheet 24 · Latency End to End
Where the Milliseconds Actually Go
| Stage | Typical Range |
|---|---|
| Network round-trip (client → API) | 20–150ms, region-dependent |
| Queue wait (fleet saturated) | 0ms typical, up to several seconds at peak |
| Cold start (scale-to-zero, first request after idle) | 3–15s |
| Model inference (single image, warm GPU) | 50–150ms at this model size |
| Response serialization | <5ms |
Cold Start Is the Single Biggest Latency Risk
Scale-to-zero is the cheapest hosting option and the one most exposed to cold-start latency — a request landing on an idle fleet can take 20–100× longer than a warm one. If a p95 latency SLA matters, keep one warm instance standing and let autoscale handle burst above it.
Sheet 25 · Monthly Cost, Itemized
Reference Volume: 100,000 Images / Day
| Line Item | Basis | Monthly |
|---|---|---|
| Inference compute | ~1.4hr GPU-time/day × safety margin × $0.70/hr | $90–150 |
| Object storage — rolling raw | ~9TB rolling window × $0.015/GB | $130–150 |
| Object storage — retained | ~1.35TB/mo growth × $0.015/GB | $20–30, compounding |
| CDN / delivery | Usage-based, cache-hit dependent | $50–150 |
| Queue + metadata store | Managed small tier | $50–200 |
| Monitoring / logging | Standard managed tier | $20–50 |
| Total | ~$360–730 / month |
Marginal Cost Per Image
At reference volume (~3M images/month), total cost lands at roughly $0.0001–0.0002 all-in per image processed — dominated by rolling storage and delivery, not compute, at this model size.
Sheet 26 · Cost at Different Scales
| Scale | Volume | Approx. Monthly | Notes |
|---|---|---|---|
| Freelance / side project | 500/day (~15K/mo) | $15–40 | Fixed costs dominate; compute is nearly free |
| Small startup | 10,000/day (~300K/mo) | $80–180 | Storage and CDN still dominate over compute |
| Reference | 100,000/day (~3M/mo) | $360–730 | Compute becomes a real line item |
| Growth stage | 1,000,000/day (~30M/mo) | $2,500–5,000 | Committed-capacity discounts (20–40%) start to matter |
Below Roughly 5,000–10,000 Images/Day, Check the Crossover
Sheet 27 · Cost vs. Frontier API
Same Reference Volume, Priced Against Gemini 3.1 Pro
This Is the Number That Should Change Your Mind
Up to 7× cheaper, self-hosted, at the exact same volume. That's not a rounding error — that's the entire margin of a business, sitting in a vendor's invoice instead of your pocket.
| Approach | Basis | Monthly |
|---|---|---|
| This system, self-hosted | Sheet 25 total | ~$360–730 |
| Gemini 3.1 Pro, list price | ~700 input + ~150 output tokens/img × $2/$12 per M × 3M images | ~$9,000–9,600 |
| Gemini 3.1 Pro, batch discount | 50% batch-API rate applied to the above | ~$4,500–4,800 |
| Ratio, worst case for this system | ~6–7× cheaper self-hosted |
Why the Gap Widens With Scale, Not Narrows
Beyond Raw Cost
Sheet 28 · Three Paths, Compared
Full Build, Fine-Tuned Open Weights, or a Frontier API
| Dimension | Full Build (This Path) | Fine-Tune Open Weights | Frontier API |
|---|---|---|---|
| Weight ownership | Complete | Complete | None |
| Architecture ownership | Complete | Inherited | None |
| Upfront cost | Low–moderate | Low | ~None |
| Marginal cost at volume | Lowest, falling | Lowest, falling | Highest, linear |
| Data leaves the org | Never | Never | Every call |
| Time to first version | 6–10 weeks | 1–3 weeks | Immediate |
| Dependency risk | None | None once trained | High |
Why Not the Frontier API, Beyond Cost
Cost alone is enough to disqualify a frontier API at any real volume for a narrow, repeated task — but it isn't the only reason. A general-purpose model iterates on a schedule this team doesn't control: a flagship vision model in this market was superseded and deprecated within roughly four months of a prior release, requiring every downstream integration to re-validate against a new model version on short notice. Someone else's roadmap should never be a load-bearing part of your product.
Sheet 29 · Decision Framework
Which Path Fits You
Sheet 28 compares the three paths on their merits. This sheet maps reader type to a starting recommendation — still worth checking against your actual volume before committing.
Start with a frontier API. Optimize for speed to a working first version, not cost — you don't have usage data yet to size anything against. Revisit once you do.
Check actual volume first. Below roughly 5,000–10,000 images/day, a frontier API is usually still cheaper all-in once your time is priced in. Fine-tuning only pays off if the same need recurs across multiple client projects.
Fine-tune an open checkpoint. Fast enough to ship, full data ownership, marginal cost already falling with volume.
Build it. Own the architecture outright, keep a warm instance once real traffic justifies it, and negotiate committed compute pricing once volume is proven. This is the path that puts every future model version, every pricing change, every rate limit — entirely under your control. That's not a technical nicety. That's the whole point.
| Reader Type | Recommended Path | Primary Constraint |
|---|---|---|
| First-time builder | Frontier API | Speed to first version |
| Freelancer, low volume | Frontier API | Total cost incl. your time |
| Startup, proven need | Fine-tune open checkpoint | Cost / ownership balance |
| Expert, max control | Full build | Cost + latency + ownership ceiling |
Sheet 30 · Implementation Detail
What Actually Gets Built, File by File
| File / Path | What It Is |
|---|---|
| models/vision_transformer | Vision encoder |
| models/modality_projection | Pixel-shuffle + linear bridge |
| models/language_model | Decoder-only transformer |
| models/vision_language_model | Combined forward pass |
| data/bootstrap.py | Reference-model distillation script |
| data/blend.py | General + task-data mixing |
| train.py | Staged training loop |
| serve/ | Quantization + inference server config, API layer |
Build Sequence
- —Stand up the environment — Install the training stack; confirm a single GPU runs one forward and backward pass end to end.
- —Reproduce the baseline — Train on general data only; confirm known reference accuracy before changing anything.
- —Build the bootstrap dataset — Run the distillation script against a reference judgment source; convert output into the training schema.
- —Blend, train, evaluate, serve — Retrain on the blended mix, iterate against held-out agreement, quantize, and stand up the API.
Sheet 31 · Timeline & Resourcing
A Proof of Concept Measured in Weeks, Not Quarters
| Phase | Duration | Output |
|---|---|---|
| Environment & baseline reproduction | 1–2 weeks | Validated pipeline |
| Task dataset bootstrap | 2–3 weeks | Labeled training set |
| Blended training & iteration | 3–4 weeks | Candidate model |
| Evaluation, API & serving setup | 1–2 weeks | Working proof of concept |
| Total | 6–10 weeks | Independently trained system |
Resourcing for This Phase
What This Proof of Concept Establishes
A working, independently trained, privately held vision-language system with a defined API contract, that reproduces a specific judgment task at a quality worth building on — and a validated pipeline for producing the next version, larger or more capable, from the same foundation. Six to ten weeks from today, this stops being a vendor's model running on a vendor's schedule at a vendor's price — and starts being yours.
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