Son — here's how I'd scope the CV/multimodal contract before our call.
You're hiring a senior CV/multimodal lead for visual-media-vs-reference-doc compliance flagging — both image and video. I read the spec, mapped a path, and put my honest read here so the call can be confirming a direction, not running discovery. If any of this is wrong for your reality, the call corrects it fast. If it's right, we have a head start.
The bounded prototype is doable in 4-6 weeks (under your 8-12 estimate) if we pick the right architecture upfront and build eval discipline alongside the model — not after. Two viable architectures (single-tower VLM fine-tune vs hybrid pipeline). My lean: hybrid pipeline for compliance-flagging because the doc-side reasoning is brittle inside a VLM. Honest 80/20 read: the model is the easy 80%; eval rigor is the 20% that kills. Senior multimodal lead range: $15-30K for a 4-6 week scoped engagement.
1The use case, as I read it
- Inputs: visual media (image + video) + reference docs (format unknown — likely a mix of structured policy + PDF guidelines).
- Output: a "flag" decision — whether the media violates compliance rules defined in the reference docs.
- The flag: probably hybrid — strict rules where they exist (e.g., "no firearms in frame"), model judgment where the rules are interpretive ("brand-safe context"). Worth confirming.
- Data: proprietary, real, already exists. Labeling status is the first scoping question — labeled / partially labeled / unlabeled changes the architecture choice.
- End state: production-ready prototype. Not an exploratory notebook — an actual deployed inference path with monitoring + a human-review loop.
2Two architecture options
Option A · Single-tower vision-language model fine-tune
Take a strong open-weight VLM (Qwen2-VL, Llama 3.2 Vision, InternVL, or LLaVA-OneVision depending on license + your inference hardware), fine-tune end-to-end on (media + doc context) → flag pairs.
- Best when: compliance rules are interpretive, fuzzy, or change frequently. The model learns the doctrine.
- Pros: single inference call. Easy to deploy. Strong on visual-textual reasoning out of the box.
- Cons: opaque decisions — hard to explain *why* it flagged. Brittle on edge cases not in training data. Fine-tune cost scales with media volume.
- Honest: if the docs are short + the rules are vibe-y, this works.
Option B · Hybrid pipeline (visual encoder + doc reasoner + decision layer)
Decompose: a vision model produces structured frame-level features (objects, scene, OCR'd text, brand marks), a separate doc reasoner ingests the reference policy + the structured features, and a decision layer (rules + small judgment model) outputs the flag.
- Best when: compliance rules are dense, the docs are long-form policy, and explainability matters (audit trail for why a flag fired).
- Pros: interpretable. Each layer testable independently. Easier to update one piece without retraining the rest. Doc-side reasoning lives in a focused model that can be cheaper at inference time.
- Cons: more moving parts. Eval has to validate each stage, not just end-to-end.
- Honest: for compliance flagging at production scale, this is the architecture that survives audits + lets non-ML stakeholders trust the system. My lean.
3The 5-layer prototype path
Data strategy
Audit the proprietary data. Define the eval slice before touching a model — what's the held-out test set? What's the rate of confirmed-flag examples? Identify gaps. Decide labeling strategy if labels are partial.
Model selection
Run a 3-model bake-off on the eval slice before committing. Open-weight VLMs vs hybrid components. Pick based on accuracy, latency, deployability, and cost-at-inference — not just paper benchmarks.
Fine-tuning + decision layer
Fine-tune the chosen architecture on real data. Build the decision layer (hybrid path) or specialize the VLM (single-tower). Continuous eval against the held-out set as we train.
Eval discipline
Build the eval harness in parallel — confusion matrix, per-rule slice metrics, false-positive cost vs false-negative cost. This is where most multimodal projects quietly fail. Eval discipline is the deliverable, not the afterthought.
Deploy
Inference path on lean infra (your stated preference). Human-review loop for confidence-edge flags. Monitoring on production traffic. Documentation of failure modes + retrain trigger criteria.
4Honest 80/20 — where the 20% kills
Eval rigor — and the cost asymmetry of false positives vs false negatives
Most multimodal compliance work over-indexes on overall accuracy. Compliance flagging cares about the COSTS of being wrong in each direction:
- False positive (flagged but compliant) → human reviewer time + creator-experience hit
- False negative (missed violation) → compliance failure, possibly regulatory exposure
The right model is the one that minimizes weighted error against your actual cost function — not the one with highest F1 on a generic benchmark. This is where eval rigor pays for itself. Defining the cost weights up-front is a 1-hour conversation with whoever owns compliance — and it changes which model you ship.
The other quiet killer: data drift on production media. The flagging system that worked in Q1 quietly degrades by Q3 as the input distribution shifts (new content formats, new violation patterns). Building the retrain trigger into the prototype = production-ready. Skipping it = "production-ready" in name only.
5Timeline + cost honest read
What trades for the 4-6 vs 8-12 estimate
The 8-12 week budget probably assumes a multi-architecture exploration phase. If we commit to the hybrid pipeline (Option B) on the call, we skip 2-4 weeks of architecture bake-off and ship to working prototype faster. The trade: less in-prototype validation that single-tower wouldn't have been better. We can run a side-by-side validation in week 5 if it matters — adds 1 week.
If the data labeling story is messier than the spec implies, add 2 weeks to layer 1. Everything else stays.
Mon/Tues call · Solana Beach office · Async-ready
I sent the same to son@inspected.com so we have both rails. Toss me 2-3 windows that fit your day and I'll lock the best one. If you can send 1-2 sample media frames + the reference doc format ahead of time, I come in fully scoped.