Production infrastructure that moves AI from prototype to profit—with the observability, governance, and economics to keep it running safely at scale.
Most ML projects stall between proof-of-concept and production. The demo works, the business case looks solid, but somewhere between Jupyter and prod, things break. Models drift. Pipelines fail silently. Costs spiral. Teams spend more time firefighting than shipping. At Fornax, we build ML platforms that bridge the gap between what data scientists create and what engineers can operate. Our approach combines feature infrastructure, model serving, observability, and governance into systems that run reliably under real-world conditions—high traffic, messy data, evolving requirements, and budget constraints.
We design platforms that support the full model lifecycle: from experimentation and training to deployment, monitoring, and retraining. Every component is built with operational realities in mind: latency requirements, cost per inference, model versioning, A/B testing, and the inevitable moment when you need to roll back at 2am. The result: ML systems that teams trust to run in production, business leaders trust to deliver ROI, and compliance teams trust to meet regulatory standards.
How do we get models from notebooks into production without rebuilding everything?
What infrastructure do we actually need versus what vendors say we need?
How do we keep dozens of models running reliably without a massive ML ops team?
Why do our inference costs keep climbing, and how do we control them?
How do we ensure models stay accurate as data changes?
Centralized feature engineering with consistent computation across training and inference. Online/offline stores, point-in-time correctness, and reuse across models to reduce redundant work.
Complete lineage tracking from data and code to trained artifacts. Reproducibility guarantees, experiment comparison, and the ability to promote or roll back any model version instantly.
Flexible serving patterns—REST APIs, batch scoring, streaming inference, edge deployment. Auto-scaling, traffic splitting for A/B tests, shadow mode for validation, and blue-green deployments.
Real-time tracking of model performance, data drift, prediction distribution, and business metrics. Alerts that catch degradation before it impacts outcomes, with root-cause analysis built in.
Model cards, bias detection, explainability tools, and audit trails. Privacy controls (PII handling, differential privacy), approval workflows, and documentation that satisfies regulators.
Per-model economics tracking, inference cost attribution, and optimization levers (model quantization, batching, caching, tiered serving). Clear visibility into what's driving spend.
How we build your platform
Map the Model Portfolio
Inventory existing models, understand latency/throughput requirements, identify shared components, and clarify which models justify custom infrastructure versus shared services.
Design the Architecture
Select the right patterns for your scale and budget: feature stores, model registries, serving layers, and monitoring systems that fit your team's capabilities and compliance requirements.
Build Core Infrastructure
Implement production-grade components with proper abstractions: versioned feature pipelines, scalable serving, comprehensive logging, and automated testing at every layer.
Establish Governance & Controls
Embed safety checks, approval workflows, bias detection, and explainability into the deployment process. Make compliance automatic, not manual.
Operationalize & Optimize
Deploy initial models, establish monitoring baselines, tune performance and costs, and create runbooks. Train teams on the platform and iterate based on real operational feedback.
Reuse as leverage
Shared features, preprocessing logic, and serving infrastructure mean each new model costs less to deploy. Your second model in production costs a fraction of your first.
Right-sized serving
Not every model needs the same infrastructure. Batch jobs run on spot instances. High-frequency predictions use dedicated endpoints. Occasional inference calls go through serverless. Match the pattern to the economics.
Transparent unit costs
Track cost per prediction, per model, per use case. Identify expensive outliers and optimization opportunities. Budget based on actual usage patterns, not vendor quotes.
Policy automation
Security scans, bias checks, performance validation, and compliance verification happen automatically in the deployment pipeline. Models that don't pass don't ship.
Risk-based controls
High-stakes decisions (credit, healthcare, legal) get stricter approval workflows and deeper audits. Internal tools move faster with lighter gates. The platform adapts to context.
Complete auditability
Every prediction traces back to a specific model version, feature values, and training data. When regulators ask questions, you have answers in minutes, not weeks.
Continuous compliance
As regulations evolve (EU AI Act, algorithmic fairness rules, industry-specific requirements), the platform adapts with updated checks and documentation—without breaking existing workflows.
Explore All Capabilities
Strategy and Transformation
We help leaders build strategies that don’t sit in decks, but those that scale, adapt, and deliver measurable value.
Data Foundation
A modern data foundation gives you one source of truth for analytics, AI, and decision-making - engineered for reliability, speed, and scale.
Advanced Analytics & Insights
We build analytics platforms and production models so leaders make faster, confident decisions at scale.
AI / ML Innovation
From robust AI engineering to production-grade LLM solutions and ML platforms, Fornax turns experimentation into scalable impact.
Start with observability and the feature store. Instrument what's running today so you understand actual performance and costs. Then build a shared feature layer that new models can adopt immediately while legacy systems migrate gradually. Most organizations see ROI within the first three models that reuse engineered features—the time savings and consistency gains compound quickly.
Most successful ML organizations land on a hybrid: managed infrastructure for commoditized pieces (compute, storage, basic serving) plus custom tooling for competitive differentiators (feature engineering, domain-specific monitoring, specialized model types). We help you draw that line based on your ML maturity, team capabilities, and strategic priorities. The goal is minimum operational overhead with maximum control where it matters.
Layer your monitoring: track technical metrics (latency, error rates), prediction statistics (distribution shifts, confidence scores), and business outcomes (conversion rates, accuracy against ground truth). Set up automated retraining when drift crosses thresholds, but always validate before deploying. The best teams treat model maintenance as a continuous process, not a crisis response.
With proper infrastructure, a small team can reliably operate 20-50 models. The key is standardization: consistent deployment patterns, shared monitoring dashboards, automated retraining, and runbooks for common issues. Where teams struggle is managing bespoke infrastructure for every model. Platforms create leverage—each model becomes incrementally easier to support.
Separate the environments but connect the workflows. Data scientists need freedom to experiment with new approaches, libraries, and techniques. Production needs reliability and standardization. The bridge is a promotion process: models graduate from experimentation to staging to production as they pass gates for performance, cost, safety, and operational readiness. Fast iteration where it's safe, rigorous validation where it matters.
Partially. The core principles remain: versioning, monitoring, cost control, governance. But LLMs add new requirements: prompt management, RAG pipelines, longer context windows, token-based pricing, and specialized evaluation methods. Smart platforms abstract these differences where possible while exposing controls for LLM-specific concerns like grounding, hallucination detection, and cost-per-token optimization.
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