From demo to production. AI that grows your business.
DataBridgeCrux turns AI pilots into reliable systems your business can depend on.
AI is only as reliable as the systems supporting it.
Models are the visible part. What decides whether they hold up in production is everything beneath them: the data they read, the permissions they respect, the tests that watch them, and the team that runs them. That is where we spend most of our time.
See what we build with →Data you can trust
Clean, modelled and fresh sources with clear owners, so every answer starts from the right numbers.
Access that holds
Permissions travel with the data into every index, prompt and agent, so nothing leaks by accident.
Quality you can prove
Test sets and regression checks that show when a change makes the system worse, before your users do.
Systems your team can run
Monitoring, cost ceilings, runbooks and handover, so it keeps working long after launch day.
Most organizations are not short on AI ideas.
Most teams can already name a dozen promising use cases. What holds them back is everything those ideas depend on: data that can be trusted, clear rules on who can see what, a way to measure whether the output is any good, and a path from pilot to production. Without that foundation, good ideas stall long before they reach the people they were meant to help.
Data nobody fully trusts
Pilots that never ship
Quality no one measures
Costs that keep creeping
Fix the foundation.
Then ship the system.
Four areas of work designed to move from data problems to production systems without unnecessary complexity. Each one stands on its own, and each one is built so the next can rely on it — from the first audit to the system your team runs every day.
AI Readiness Assessment
Two weeks. We audit your data, systems, and use cases, then hand you a prioritized roadmap with honest estimates. You own it either way.
How this works →Generative AI Solutions
RAG systems, agents, and LLM applications built with access control, evaluation, and cost ceilings from day one.
How this works →Data Platform Engineering
Warehouses, lakehouses, pipelines, and migrations. The unglamorous work that determines whether everything above it functions.
How this works →Analytics & BI
Dashboards people actually open, built on a semantic layer that stops three teams reporting three different revenue numbers.
How this works →
What we
build with
Four frameworks we maintain across every engagement: ingestion, orchestration, agent workers, and the evaluation layer that proves the rest of it works. You get the source code, and you keep running it long after we leave.
Where the foundation turns into outcomes.
From the first audit to systems teams rely on every day, each build is shaped around the data underneath and the people who will run it.
Enterprise Search
Document Intelligence
Operations Agents
We publish how we build.
Most consultancies describe their methodology in adjectives. We publish ours as engineering patterns — the actual architectures we use, what they cost, what they’re bad at, and when you shouldn’t use them.
It’s a strange thing to give away. We do it because the clients who read a pattern and call us already know what they’re buying, and those engagements go better.
Browse the pattern library →Permission-aware retrieval over enterprise documents
Enterprise document retrieval where access permissions remain part of the retrieval system.
Read pattern →LLM evaluation harness for production systems
Evaluation patterns for measuring production AI quality and catching regression.
Read pattern →Change data capture from operational systems to lakehouse
A proven pattern for moving operational changes into modern data platforms.
Read pattern →
The thinking behind
the build.
Recent work and engineering patterns across the areas where we build. Each one comes from a real engagement: the architecture we chose, what it cost to run, where it struggled, and what we would change next time. Read a few before we talk and you will know exactly how we think.
Cost-tiered model routing
Route AI requests across different model tiers based on complexity, cost, and workload requirements.
Read pattern →Human-in-the-loop approval gates for agent workflows
Keep people inside agent workflows where approval, oversight, and decision control matter.
Read pattern →Semantic layer as a single source of metric truth
A governed approach to keeping business metrics consistent across teams and reporting systems.
Read pattern →Parallel-run migration for warehouse replacement
Validate old and new systems side by side before switching production workloads.
Read pattern →Data quality monitoring with routed ownership
Make data quality failures visible and route them to the people responsible for resolving them.
Read pattern →
Practical engineering,
from day one.
The goal is not to build something impressive for a demo. The goal is to build something your team can operate: documented, tested, and owned by the people who will run it long after the engagement ends. Three principles shape every project we take on.
We start with the data, not the model.
Every failed AI project we’ve been called in to rescue had the same root cause. The model was fine. The data feeding it wasn’t governed, wasn’t fresh, or wasn’t what anyone thought it was.
We build to hand over.
Documentation, runbooks, and a knowledge transfer plan are in scope from the first week. If your team can’t run it without us in six months, we’ve failed regardless of what the dashboard says.
We tell you when not to use AI.
A material share of the use cases clients bring us are better solved with a rules engine, a fixed report, or a conversation with three people. We’ll say so.
Tell us what’s stuck.
Most of our engagements start with a 30-minute call where we listen more than we pitch. If we’re not the right fit, we’ll say so and point you somewhere better.