2 weeks · fixed fee
AI Production Readiness Review
Know what to build before you fund it. An evaluation baseline on your data, a target architecture, a run-cost model and a go/no-go plan.
Data engineering AI can rely on
An AI system is only as reliable as the data behind it. We design and run pipelines, warehouses and real-time analytics with quality checks and governance built in.
What is AI-ready data?
Most AI failures are data failures
AI-ready data
Real-time pipelines
Knowledge graphs
Data quality and governance
AI-ready data is complete, current, available under the right permissions and checked for quality before a model or agent uses it. In most companies it is spread across systems, owned by different teams and never tested.
We build the data path end to end: ingestion, modelling, quality checks, lineage and access control. For B2B.IQ we scaled an analytics platform to more than 300 million transactions a day, with queries 30x faster.
01
Pipelines and ingestion
Batch and streaming pipelines from your products, SaaS tools and databases, with retries, monitoring and alerting.
02
Warehouses and lakehouses
Data models designed for the questions people and AI systems ask, at a predictable cost.
03
Retrieval data: vectors and knowledge graphs
Document parsing, chunking and embeddings for RAG, and knowledge graphs built by entity extraction and resolution, kept in sync with the systems they come from.
04
Quality and governance
Data contracts, tests, lineage and access rules, so you know what fed each answer.
How we work:
Analytics too slow for the business
Queries that take minutes and dashboards nobody trusts.
Data spread across tools
CRM, product, billing and support data that never meet.
RAG answers that go stale
Indexes that drift out of sync with the documents behind them.
No single source of truth
Three teams, three numbers for the same metric.
Costs that keep growing
Warehouse and pipeline bills with no owner.
Compliance questions
Personal data, retention and audit requirements for AI use.
Want to know whether your data is ready for AI?
Read how B2B.IQ scaled its analytics platform to 300M+ transactions a day.
How we engage
Start small, with a fixed scope
6–9 weeks · team of 4
Pilot-to-Production Sprint
One workflow, live in production. Built or rebuilt, connected to your tools, with human approvals where the stakes are high and monitoring against the baseline.
Monthly · from 3 months
Forward-deployed AI engineers
Senior AI and data engineers who join your team, work in your tools and ship on your roadmap.
Part-time · monthly
Fractional AI CTO
Senior leadership for your AI roadmap: model and vendor choices, team design, architecture reviews and board reporting.
What clients say
Teams that kept working with us
From my experience, it is hard to find a team who have both excellent technical and commercial skills. Busy Rebel has never let us down, so we continue to work with them. The most important criteria we have as a team is creating & maintaining high standards; Busy Rebel has this in spades.
I've had the pleasure of working with a few software houses in the past, but none of them compares to the team at Busy Rebel. Their sense of mission and purpose is unparalleled, and their in-house skills are excellent. I always know that I can rely on them to get the job done, and done well, even if we are working remotely.
Thanks to the contributions of Busy Rebel, our organization as a massive worldwide distributor has passed all phases of digital transformation, gained a competitive advantage, and somewhere has revolutionized our industry. Busy Rebel is extremely productive digital partner, and internal stakeholders are particularly impressed with the vendor's dedication to success and attention to detail.
Tell us what you’re trying to ship
Data engineering for AI: FAQ
What does AI-ready data mean?
Data that is complete, current, available under the right permissions and tested for quality before a model or agent uses it, with lineage so you can trace an answer back to its source.
Do we need a data platform before we start with AI?
Not a complete one. We build the part of the data path the first use case needs and extend it with each new one, so data work pays off in production instead of in a long platform project.
Do we need a knowledge graph for AI?
Only if your questions depend on relationships between customers, contracts, products or events. A useful graph needs entities extracted from your data, duplicates resolved to one canonical record and updates as the sources change, so it is a data engineering job as much as a modelling one. We start from the questions and an evaluation set, then choose vectors, a graph or both. See how they relate on our AI knowledge graph.
Which tools do you use?
We work with the major clouds and the common warehouse, lakehouse and streaming tools, and we start from what you already run before adding anything new.
Can you work with our existing data team?
Yes. We often join in-house teams: we add senior engineers, set standards and hand over.
