- Unstructured data is being corrected manually
- AI experiments stay in the prototype and never reach production
- The team uses AI tools inconsistently, with unpredictable results
01
AI
AI & Data Engineering
We put large language models to work inside the product and inside the engineering process — wherever they produce a benefit we can measure.
Scope
What we take on
We treat AI as something that has to earn its place, not as a feature to show off. First we define which task gets shorter or which manual step disappears entirely; only then do we take it to production.
Workstreams
- Messy data that is currently corrected by hand, cleaned up automatically
- Records stored in different formats matched to each other correctly
- AI flows that stall at the pilot stage taken into production
- A consistent, measurable way for the team to use AI tooling
When you need this
If these signs
look familiar.
Where we applied it
LLM-based normalisation and vector matching across Obilet inventory; LangChain / LangGraph flows on the NEXT AI platform.
Results in production
Which platforms
have we applied this on?
ServerlessLLM + vector search on AWS
Customer Intelligence (AI)
Scattered customer data became actionable insight on a serverless AWS architecture.
NEXT AI’s customer data was spread across several sources, and because every analysis required manual interpretation, capacity was tied directly to headcount. We built the collection and normalisation flows, the LLM-based classification and the vector-search segmentation on a serverless AWS architecture, taking on the solution architect and DevOps roles. The platform now turns raw data straight into insight teams can act on, while infrastructure cost scales in proportion to usage.
Platform →Related platforms
Which platforms
does this discipline come into?
Let’s work together
Let’s look at a need you have around ai & data engineering.
Tell us where you are today and which constraints matter most; we will define a clear starting point together.