ML models built
for your use case.
TensorFlow, PyTorch, scikit-learn. Production ML with real evals. 50+ systems shipped.
Afnexis Results
48h→2h
credit decisions
ShinyLoans
50+
ML systems shipped
across 10+ countries
4-8 wks
to production
avg. timeline
4.9/5
client rating
30+ clients
WHAT WE BUILD
Which ML system do you need?
We've shipped ML in healthcare, fintech, real estate, and logistics.
Predictive Analytics
Churn prediction, demand forecasting, fraud detection. Trained on your data, not generic benchmarks.
Classification Models
Document routing, intent classification, image labeling. Accuracy evals before any deployment.
Recommendation Engines
Collaborative filtering + content-based hybrid. Works with sparse data and cold-start users.
ML Pipelines and MLOps
Data ingestion, feature engineering, model training, eval, and retraining loops. All automated.
LLM Fine-Tuning
LoRA and QLoRA on your domain data. Cuts inference cost 60% vs a larger base model.
Embeddings and Vector Search
Semantic search, duplicate detection, clustering. Pinecone, Weaviate, pgvector. Built to scale.
By Muhammad Aashir Tariq · CEO & Head of AI, Afnexis · Updated April 2026
REAL RESULTS
Numbers from real deployments.
48h→2h
decision time
ShinyLoans fintech
94%
avg. model accuracy
across classification tasks
50+
ML systems shipped
in production
4.9/5
client rating
30+ clients
"ShinyLoans needed credit decisions in under 2 hours. We trained and deployed an ML pipeline that cut their decision time from 48 hours to under 2."
CTO · ShinyLoans · Fintech
HOW IT WORKS
From call to production in weeks.
Data Audit
We assess your data quality, volume, and labels. If the data can support the model, we scope it.
Train and Eval
Model training with a proper eval suite — not just accuracy, but the metrics that match your business goal.
Deploy and Monitor
Deploy to your cloud with a monitoring dashboard. Model retraining triggered when drift is detected.
PRICING
Fixed price. No surprises.
Ranges from 50+ real projects. Milestone billing. No retainers.
| Project Type | What's Included | Timeline | Starting At |
|---|---|---|---|
| Classification Model | Binary or multi-class, eval suite, API endpoint | 3-5 weeks | $15K |
| Predictive Analytics | Time-series or tabular, feature engineering, dashboard | 4-8 weeks | $25K |
| Recommendation System | Collaborative + content-based hybrid, A/B eval | 5-10 weeks | $35K |
| Full ML Platform | End-to-end MLOps, retraining pipeline, monitoring, API | 10-18 weeks | $80K |
FAQ
Quick answers.
How much data do I need?
Depends on the task. Classification usually needs 1,000+ labeled examples per class. Recommendation systems can work with 10,000+ interactions. We audit your data before quoting.
Do you handle data labeling?
We can coordinate labeling via Label Studio or partner labeling services. It's scoped separately and priced at cost.
What cloud does the model deploy to?
AWS SageMaker, Azure ML, GCP Vertex AI, or a plain EC2 instance with a FastAPI wrapper. We deploy where your other infrastructure lives.
What if accuracy isn't good enough?
We set accuracy targets before we start. If we don't hit them, we keep iterating at no extra cost until we do.
READY TO START?
Let's build your first agent.
30-min call. No pitch. We map the workflow and quote it.