Groundline
AI annotation for SMEs — lower cost than labeling vendors

AI models do the labeling.
Your experts only review.
The cost stays SME-sized.

Groundline runs vision models on your raw photos, video, and logs. They draw most of the boxes. People only check the uncertain 8–15%. You are not paying a farm to click every pixel — that is why we come in below typical annotation competitors.

Get a Free Dataset Assessment See How It Works
Models pre-label ~85% Humans review the rest Below vendor labeling rates

Raw Data

Your files

Assessment

We profile it

Pre-Label

AI handles 85%

Expert Review

Team checks 15%

Verified Data

100% accurate

Model

Gets better monthly

Built for SMEs in

Any country. Typical customer: 15–200 people, no data-science department, a few experts who still decide by eye.

Manufacturing
Food Production
Garment & Textile
Electronics
Agriculture
Retail & E-commerce

Groundline

Annotation intelligence for plants that cannot staff a labeling team.

Most SMEs already have the photos. They do not have a data department. Groundline is the system between those files and a model the floor can actually run.

Vision models draw most of the boxes before a person looks. Your QA lead reviews only the uncertain cases. You keep the verified labels. The models stay with us — so labeling stays cheaper than sending the same volume to a farm, and the dataset keeps up when the product mix changes.

01

Models do the annotation

Groundline pre-labels most items automatically. People only touch the 8–15% the model is unsure about. You pay for judgment, not for thousands of clicks.

02

Built for a 15–200 person shop

No ML engineer on your side. A QC lead or owner, a browser, about 15 minutes a day. Remote-first; on-site when the line requires it. Any country.

03

Cost below labeling vendors

Competitors bill for every box. Each Groundline cycle the models take more of the load, so cost per label falls — without an enterprise labeling budget.

04

A dataset that stays alive

New SKUs and drift are the job, not a change order. Monthly cycles recapture what changed, retrain, and keep inspection aligned with the floor.

“We are too small for a data team. They sat with the one person who knows the product, wrote that down, and the model started catching what she would have caught at 6 a.m.”

— Owner, food packing SME

Why the cost is lower

AI models do the annotation work. People only touch the uncertain cases.

Most competitors still pay humans to draw every box. Groundline runs models first, then a short expert review. You pay for judgment, not for thousands of clicks.

Typical annotation vendors

  • A labeling farm draws every box, polygon, and tag by hand
  • Billed per image or per hour — cost scales linearly with volume
  • Your staff still have to teach the vendor what “fail” means
  • A new product variant means paying for another full pass

Groundline

85%

of items pre-labeled by models before a person looks

  • Vision models propose labels on your actual shop-floor files
  • Humans review only the 8–15% the model is unsure about
  • Your QA lead’s knowledge is captured once, then reused
  • Each cycle the models handle more — cost per label falls, not rises

That is the whole cost advantage: model-first annotation, not cheaper click-workers. SMEs get a verified dataset and a v1 model without an enterprise labeling budget.

The Problem

Most AI Projects Never Ship.

Not because of the algorithm. Because of the data.

87%

of AI projects fail to reach production

The #1 reason: poor quality training data

40–60%

of ML project time spent on data prep

Time that should go to building the model

1.9%

of SMEs have AI in day-to-day operations

The gap is the opportunity — most tools are built for enterprises

“We had months of production photos on a shared drive. Nobody knew how to label them. The cheap camera system we bought never worked. We stopped.”
— Owner-operator, food packing SME

The Process

From Raw Data to a Working Model — in 4 Weeks

Models pre-label most of the set. Your people confirm the rest. That is how a small plant gets labeled data without a labeling-farm invoice.

1

Upload Data

Drag-and-drop upload. Any format: images, video, CSV, sensor data.

2

Knowledge Capture

Two sessions with your engineers. We turn their expertise into annotation rules.

3

AI Pre-Labels

Groundline models draw boxes on 85–92% of items, with a confidence score on each.

4

Expert Review

People only review the uncertain 8–15% — not a farm clicking every image.

5

Model Improves

Each cycle the models take more of the load. Accuracy up, cost per label down — below what annotation vendors charge for a full human pass.

Then we repeat — models do more, your review time and cost drop

Why It Works

Six Values That Compound Over Time

Speed to First Model

First verified, model-ready dataset delivered in 4 weeks.

4 weeks vs 5–7 months (industry average)

85%

less spend than a full human labeling pass by Cycle 3 — models did the boxes

Accuracy You Can Prove

72%
80%
86%
91%
94%

Verified IAA ≥ 0.85 guaranteed on every delivery

Knowledge That Stays

Your retiring QA expert's knowledge, captured in an annotation guidelines document that survives their departure.

Handles New Products

New variant arrives → model detects drift → alerts you → 50 new samples → retrained in 1 week.

Your labels, always

Every verified label is yours. Export COCO, YOLO, or CSV and take the dataset anywhere. Model weights and architecture stay with Groundline — that is the system that keeps labeling cheap.

0%

Average model accuracy by Cycle 3

0%

Lower cost vs typical annotation vendors

0 wks

Time to first working model

0 hrs

Expert team time per month (after Cycle 3)

SME use cases

Sized for a shop floor — not an enterprise program.

These are the kinds of teams we work with: owner-operators and small plants that cannot staff an ML department. Results are representative; company names withheld.

Food packing QA

Family plant, ~40 people, one QA lead

MetricBeforeAfter
Defect catch82%96%
Line speedLimited by one personSame person reviews only uncertain packs
ReworkEnd of shift-50%

Kept the senior inspector's eye after she cut to 3 days a week

Job-shop parts check

Machine shop, ~25 people, no IT staff

MetricBeforeAfter
Model accuracy71%94%
Customer returnsWeeklyRare
Who inspectsOwner + 1Owner reviews exceptions

A bought-in camera kit was failing; we fixed the labels, not the hardware

Produce grading

Packing house, seasonal crew of ~30

MetricBeforeAfter
Grading agreement64%96%
Top-grade yield71%78%
Overtime in peak weeksHeavy-40%

More consistent grades without hiring a night grader

Garment defect spotting

Workshop, ~80 sewers, two QC tables

MetricBeforeAfter
Catch rate67%95%
Scrap7%0.8%
Buyer complaintsRegularDropped

Fewer rejected lots without a bigger QC bench

Board / assembly check

Electronics SME, ~60 people, mixed SKUs

MetricBeforeAfter
False alarms40%4%
Missed defects2.1%0.3%
QC overtime3 people covering0.5 person on exceptions

New board revisions no longer mean hiring another inspector

Catalog tagging

Regional webshop, 6-person ops team, ~8,000 SKUs

MetricBeforeAfter
Tag accuracy71%94%
Time per SKUMinutesSeconds + spot check
Who tagsEveryone overtimeOne person reviews

Search started finding the right item without a 15-person tagging crew

Every Cycle, Your Model Gets Smarter

Models take more of the labeling each cycle. Your cost falls. That is the compound effect of AI-first annotation — not hiring a cheaper farm.

Cycle 3 · Production readyCycle 5 · Competitive moat

Accuracy

Goes up every cycle.

Human effort

Less review time each month.

Cost per label

Drops each cycle as models pre-label more — below a vendor’s full human pass.

Engagement schemes

Three ways to start. Same path from data to production.

These schemes are for SMEs. AI models do most of the annotation, so the engagement stays cheaper than sending the same volume to a labeling vendor. Scope is set after we see your files — we do not publish a rate card.

Most requested

Scheme 1

First Label

For SMEs with unlabeled production photos, video, or logs

Typical first run: 3–4 weeks

  • Dataset assessment report
  • Customer AI Profile (taxonomy)
  • Annotation guidelines document
  • 2,000–5,000 verified labeled samples
  • Working v1 model on your data (hosted or on-prem — weights stay with us)
  • Accuracy benchmark report
Start with this scheme

Scheme 2

Model Fix

You already bought a camera or a model — it still misses the floor

Typical recovery: 4–6 weeks

  • Everything in First Label, plus:
  • Data quality audit report
  • Root cause analysis of low accuracy
  • Label conflict resolution
  • Retraining with corrected data
  • Before/after accuracy certificate
Fix the current model

Scheme 3

Living Dataset

Products change; you cannot hire another inspector every season

Monthly cycles, stop when you are ready

  • Monthly annotation cycle
  • Active learning (only uncertain cases)
  • QA report with IAA score
  • Model retrain + version deploy
  • Drift detection + alerts
  • Monthly cycle report (EN / 日本語 / Tiếng Việt)
Talk about a living set

From the field

What changed for SMEs when a scheme ran

Representative work with small and mid-size operators. Company names withheld at customer request. Scale is typical of a shop that cannot staff a data team.

First Label

First working check on a food packing line

Owner-run packing SME · ~35 people

Data scale
~12,000 phone and line photos from a few weeks of production. Almost none labeled. One QA lead.
Timeline
Two short sessions in week 1. Verified set and a v1 model in 3.5 weeks — the owner stayed on the line.
What we received
A shared-drive dump, a handwritten defect list, and evenings with the person who still decides pass/fail by eye.
What improved
Catch rate 82% → 96% on hold-out lots. The QA lead now reviews only uncertain packs instead of every unit.
Effect on the business
They stopped adding Saturday overtime for inspection and could take a new private-label order without hiring a second inspector.

“We are too small for a data team. They sat with the one person who knows the product, wrote that down, and the model started catching what she would have caught at 6 a.m.”

Owner, food packing SME
Model Fix

A cheap vision kit that did not match the shop

Job-shop metalworks · ~22 people, no IT hire

Data scale
~9,000 stills plus an off-the-shelf detector stuck at 71% on real parts.
Timeline
Label audit in 10 days. Cleanup and retrain in 5 weeks, mostly remote.
What we received
The kit they had already paid for, mixed labels from whoever was free that shift, and parts a customer had sent back.
What improved
Accuracy 71% → 94%. Returns on that family of parts dropped from a weekly argument to a rare exception.
Effect on the business
They kept a customer that had started sampling every lot — without buying new cameras or hiring an engineer.

“We already ‘had AI’. It just did not agree with the bench. They showed us the labels were fighting each other. After that, I stopped standing next to the camera all day.”

Shop owner, precision parts SME
Living Dataset

Assembly check that keeps up when SKUs rotate

Electronics assembly SME · ~60 people, mixed short runs

Data scale
~8,000–15,000 boards a month. Mix changes whenever a customer sends a new revision.
Timeline
First useful cycle at week 6. A short monthly review since — about 20 minutes for the QC lead.
What we received
Live inspection images, a notebook of operator overrides, and the two people who still know the ‘feel’ of a bad board.
What improved
False alarms 40% → 4%. Missed defects 2.1% → 0.3%. QC overtime covered by exceptions, not a third inspector.
Effect on the business
New revisions no longer mean a hiring panic. The same small QC bench absorbs the mix.

“A one-shot model died the first time the drawing changed. The monthly cycle is what we can actually run. We look at the weird boards; they retrain; we do not hire another night person.”

QC lead, electronics SME

Not sure which scheme fits? Book a 30-min assessment. We will tell you which path matches your data — and what we would measure first.

Common Questions

Because models do the annotation, not a room of click-workers. Groundline pre-labels most items with AI, and your expert only reviews the uncertain slice. Competitors that label everything by hand bill you for every box. We bill you for the judgment that machines cannot yet make — so the same volume costs less.

No. We built this for SMEs. We run the annotation pipeline. Your side is a short review of uncertain cases — typically a QA lead or owner, about 15 minutes a day in a browser. No code, no new hires, no ML knowledge.

Customer data is processed in isolation. We never share it between customers. If it must stay in the building, we deploy on your hardware — available to SMEs in any country.

We guarantee measurable accuracy improvement on every cycle, or we re-label at no cost. Our cycle reports show exact before/after metrics so there is no ambiguity.

First Label package: 3–4 weeks from first upload to first model. This includes two knowledge capture sessions with your engineers, which take about 2 hours total.

Yes. Every verified label you pay for is yours: COCO JSON, YOLO TXT, CSV, or custom. What we do not hand over is model weights or architecture. You keep the dataset. The models that pre-label cheaply stay with Groundline; you use them while we run together.

Your AI Project Deserves Better Data.

Book a free 30-minute look at your files. We will tell you how much the models can pre-label — and why that usually lands below a labeling vendor for the same volume.

Your data is never shared. We respond within 24 hours.

Prefer to pick a time directly? Schedule a call

Free Assessment