Custom AI Models Engineered
for Production
We design, train, and ship machine learning systems that hold up in production — from data pipelines to model serving, monitoring, and continuous retraining.
What we build
End-to-end AI development services
From raw data to a monitored, retraining model in production — we cover every layer of the ML stack.
Custom Model Development
We design and train ML models tailored to your data and use case — from classical algorithms to deep neural networks and transformer architectures.
Foundation Model Fine-Tuning
Adapt GPT, Claude, Llama, or Mistral to your domain with evaluation harnesses that prove the fine-tuned model actually outperforms the base.
MLOps & Model Lifecycle
Experiment tracking, model registries, automated retraining pipelines, and drift detection — so your model improves over time instead of silently degrading.
Vector Search & Embeddings
Embedding pipelines, vector store indexing (Pinecone, Weaviate, pgvector), and semantic retrieval that makes your data searchable by meaning.
Model Evaluation & Guardrails
Automated evaluation suites, adversarial test sets, bias audits, and output guardrails — catch regressions before they reach production users.
Data Pipeline Engineering
End-to-end pipelines for ingestion, labelling, feature engineering, and versioning — so your models always train on clean, reproducible data.
Model Serving & Inference
Low-latency model APIs with batching, caching, auto-scaling, and A/B serving — on GPU or optimised CPU infrastructure.
Predictive Analytics
Forecasting, anomaly detection, churn prediction, and propensity models — shipped as explainable, production-ready services your teams can act on.
AI System Integration
Plug AI models into your existing product, data warehouse, or operational systems — REST APIs, streaming inference, and event-driven triggers all covered.
50+
AI models deployed
3×
Faster time-to-market
90%+
Model accuracy achieved
24/7
AI monitoring
What you gain
The outcomes our AI practice delivers
Measurable results in production — not just in demos.
What you gain
The outcomes our AI practice delivers
Measurable results in production — not just in demos.
Models Shipped Faster
From POC to a monitored, production model in weeks — not quarters — with every step reproducible and traceable.
- Structured data audit in week one surfaces gaps and sets a realistic delivery timeline
- Experiment tracking from the first run means every iteration builds on provable results
- Guarded production rollout with monitoring in place before any user sees a model output
Accuracy That Holds After Launch
Drift detection and automated retraining keep model performance high as your data evolves — no silent degradation.
- Statistical drift thresholds trigger retraining automatically when performance drops
- Evaluation harnesses score output quality on every deployment before it reaches users
- Rollback logic restores the previous version in minutes if a new model underperforms
Reproducible Science
Every experiment tracked, every result versioned — audits and rollbacks take hours, not weeks of archaeology.
- MLflow or equivalent tracks every hyperparameter, dataset version, and evaluation result
- Model registry stores production and candidate models with full lineage and metadata
- Any past experiment can be reproduced exactly — no undocumented notebook magic
Full IP Ownership
Models, weights, training code, and pipelines handed over entirely — no vendor lock-in, no ongoing licensing fees.
- Complete source code handover — models, pipelines, evaluation harnesses, and infrastructure
- No third-party platform dependency baked in — deploy on your own cloud or on-premise
- Full documentation and runbooks so your internal team can maintain and extend everything
Real Cost Control
Model tiering, caching, and cost dashboards from day one — no runaway inference bills at scale.
- Model tier selection matches capability to task — cheaper models where they are sufficient
- Response caching reduces redundant inference calls on repeated or similar queries
- Per-request cost dashboards show spend by model, endpoint, and use case from launch
Safe for Regulated Industries
Bias audits, explainability reports, and compliance documentation — included as standard, not billed separately.
- Bias evaluation across demographic slices before any model goes to production
- Explainability reports document model decisions in language regulators and auditors accept
- HIPAA, SOC 2, and GDPR-compatible data handling built into the pipeline from the start
Our Approach
How we ship AI to production
Rigorous and iterative — every step validated before we move to the next.
Data Audit
Assess your data's readiness — volume, quality, labelling coverage, and the gaps we need to fill before training starts.
Problem Framing
Model Selection
Build & Train
Deploy & Monitor
Ready to put AI to work in your business?
Share your data and goals — we'll scope an AI roadmap and get you a delivery plan within 48 hours.
Technologies
Our AI/ML technology stack
OpenAI
LLM
Anthropic
LLM
PyTorch
ML Framework
TensorFlow
ML Framework
Hugging Face
ML Models
LangChain
Orchestration
Pinecone
Vector DB
LlamaIndex
RAG
FastAPI
Backend
AWS SageMaker
Cloud AI
Google Vertex
Cloud AI
Azure AI
Cloud AI
Docker
Infrastructure
MLflow
MLOps
Scikit-learn
ML Framework
Python
Language
Why mabzone
What sets us apart
The principles and practices that make our AI projects succeed in production.
Production-Proven Engineers
ML engineers who have shipped models to millions of users — not just notebooks or demos.
Evaluation-First Mindset
We measure before we claim a model works. No guesswork, no inflated benchmarks.
Data Problem Solved First
Most AI projects fail at data, not the model. We audit and fix your data in week one.
MLOps from Day One
Monitoring, retraining pipelines, and drift detection built in from sprint one — not bolted on.
No Black Boxes
Documented pipelines, reproducible experiments, and full code handover at project end.
Compliance & Standards
Compliance Standards That Shape Our AI Development
We build AI systems that meet the strictest data privacy, safety, and accountability frameworks — so your models are trusted by regulators and users alike.
GDPR (EU)
Data privacy and lawful processing for AI-driven systems
CCPA
Consumer data rights for AI applications serving California
HIPAA
Protected health information handling for medical AI
EU AI Act
Risk-based framework for responsible AI deployment in the EU
ISO/IEC 42001
AI management system standard for governance and accountability
NIST AI RMF
AI Risk Management Framework for trustworthy AI systems
SOC 2 Type II
Security and confidentiality controls for AI platforms
ISO 27001
Information security management for AI infrastructure
OWASP LLM Top 10
Security best practices for LLM-powered AI applications
IEEE 7000
Ethical AI design standards for responsible engineering
Frequently Asked Questions
Everything you need to know before starting an AI project with us.
Let's Build Together
Ready to put AI to work in your business?
Start with a free AI readiness assessment — we'll evaluate your data, identify high-ROI use cases, and scope a path to production.