AI & Data
Applied AI that is grounded, measurable and safe to put in front of real users.
The difference between an impressive AI demo and a dependable AI system is engineering: grounding in trustworthy data, evaluation before release, monitoring in production and clear points where people stay in control.
Reference view
IllustrativeWhere this helps
We build AI-enabled applications and analytics with that discipline from day one — and we are candid about where AI is not the right tool for the job.
There is pressure to “add AI”, but no clear, measurable use case.
A promising prototype is not reliable enough for production.
Accuracy, privacy and accountability concerns are blocking adoption.
Valuable data exists but is not turned into decisions.
Capabilities
01
Applications that use AI where it measurably improves outcomes, integrated with your existing systems.
02
Automating document-heavy and repetitive knowledge work, with clear hand-offs to people.
03
Pipelines, models and dashboards that turn operational data into timely, explainable insight.
04
Language-model features designed around defined tasks, constrained outputs and predictable cost.
05
Grounding model responses in approved, current sources — with citations users can check.
06
Review, approval and escalation flows that keep qualified people accountable for consequential decisions.
07
Evaluation sets, quality benchmarks and production monitoring for accuracy, drift and cost.
08
Data-access controls and defenses against prompt injection, data leakage and misuse.
How we approach it
01
We define the task, the success measure and the acceptable error rate before choosing a model.
02
Responses are anchored to approved sources and tested against realistic cases before any release.
03
Where stakes are high, recommendations are reviewed and approved by people — and every decision is logged.
What you receive
Every engagement is scoped to your situation. These are the outputs this work typically produces.
In practice
01/Pattern
Related: AI & Data, Cybersecurity
02/Pattern
Related: FinTech & Blockchain, Software Engineering
Keep exploring
For consequential decisions, autonomy is the wrong default. Here is how to design AI systems where qualified people review, approve and remain accountable.
Start a conversation
Tell us what you’re building, fixing or exploring. We’ll help you find a sensible first step — whether that’s a focused review, a prototype or a full engagement.