Service

Data science

ML models, computer vision, NLP and analysis that answer the business question — not a model for its own sake.

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ML MODELSCOMPUTER VISIONNLPDATA ANALYSIS
What's included

Scope you can hold us to

01

Question framing before model work — what decision this answers

02

ML models you can explain to operators, not only to data teams

03

Computer vision for images, documents and quality inspection

04

NLP for classification, extraction, search and support triage

05

Analysis, dashboards and alerts wired to the decisions they support

06

Monitoring for drift, freshness and silent failures — plus docs for the next hire

Process

How we deliver data science

  1. 01Name the decisionThe question, the owner and the cost of being wrong — written first.
  2. 02Fix the pipesSources, labels and definitions. Models on dirty inputs are theatre.
  3. 03Model and validateBaselines before cleverness — whether it's tabular ML, vision or NLP.
  4. 04Put it in the loopAPIs, dashboards or alerts where the decision actually happens.
Start to submission

What that looked like on Plant Disease Classification

Agri research · Agriculture

  1. Phase 1

    Framed it as a field problem

    Detection had to work on a leaf photographed in the field, not a clean lab plate — and it had to justify itself, because an agronomist will not act on a label with no reasoning behind it.

    DeliveredScope, dataset and target disease categories

  2. Phase 2

    Transfer learning instead of training from scratch

    VGG16 and Inception were combined under transfer learning, so a limited agricultural dataset still reached usable accuracy across disease categories rather than overfitting a small sample.

    DeliveredTrained classifier with accuracy reported per category

  3. Phase 3

    Made the model explain itself

    Grad-CAM heatmaps expose which regions of the leaf drove each prediction, so a user can see whether the model read the lesion or the background before trusting the result.

    DeliveredGrad-CAM interpretability layer

    Grad-CAM heatmap showing model attention for plant disease prediction
    Grad-CAM
  4. Phase 4

    Shipped inference someone can actually use

    A Streamlit app serves real-time predictions on uploaded leaf images — the model reaches the person in the field instead of staying in a notebook.

    DeliveredDeployed inference app

    Streamlit plant disease classification app with uploaded leaf image
    Inference app
See the full project →
Related projects

Proof in production

All work →
FAQ

About data science

Ask ZAC →
Do you build models or just dashboards?
Both — but only when the decision is clear. Many briefs need trustworthy analysis first; we say which before any ML work.
When do you use computer vision or NLP?
When the signal lives in images, PDFs or unstructured text — quality checks, document extraction, ticket triage, search. We prove lift against a simple baseline first.
Can you work with our existing warehouse?
Yes. We prefer extending what you have over a greenfield rebuild unless the foundations are the bottleneck.
Next step

Not sure this is the right service? Ask ZAC.

Three minutes with ZAC Consultant gets you a recommended solution, a feature list, a timeline and a cost band. Then decide whether you want to talk to us.

  1. 01Describe the problem
  2. 02Get your roadmap
  3. 03Book a consultation
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