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Data & ML Infrastructure
AI systems are only as good as the data and infrastructure underneath them.
Includes:



Data platform modernization
Migrating fragmented data sources into a unified, AI-ready platform
Feature engineering
Building and maintaining the feature stores your models need to train well
MLOps pipelines
Automated training, evaluation, versioning, and deployment
Model monitoring
Drift detection and performance tracking so degrading models get caught early
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Typical Engagement
- 8 - 16 weeks to production
- Fixed-scope milestone contracts
- Dedicated senior engineer
- Weekly delivery checkpoints
- 30-day post-launch monitoring
Tech Stack
- OpenAI, Anthropic, Cohere
- AWS Bedrock, Azure OpenAI
- LangChain, LlamaIndex
- Pinecone, Weaviate, pgvector
- Weights & Biases, MLflow
What we do for clients
- Data platform modernization — Migrating fragmented data sources into a unified, AI-ready platform
- Feature engineering — Building and maintaining the feature stores your models need to train well
- MLOps pipelines — Automated training, evaluation, versioning, and deployment
- Model monitoring — Drift detection and performance tracking so degrading models get caught early
- Data quality & governance — Structured data audits before build, not surprises during it
- Cost & scaling architecture — Infrastructure sized for your actual workload
Where we fit best
We work best with teams that know what they want to build but whose data and infrastructure aren't ready to support it yet.
Case Study
How Bivoxo approaches real problems
FAQ
Common questions, candidly answered
Direct answers to what clients typically ask before a
discovery call - drawn from our first conversations
We already have a data warehouse — do we need to rebuild?
Usually not; we build on top of what you have
What's LLMOps and why does it matter?
LLM behaviour drifts without monitoring; we instrument it properly
What does MLOps infrastructure cost to run?
Depends on scale; we design for cost-efficiency from day one
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