From noise to signal.
Production AI, from people who know your business.

Your domain. Your data. AI in production.

RAG systems that turn company knowledge into an answer with its source in view, GenAI flows that turn unstructured text into data, agentic pipelines that speed up the development line, and flows that were stuck in a prototype taken into production. Fifteen years of production systems experience, brought to the AI side.

Talk to our AI team → Where we come in
25+platforms running in production
15+ yrsin production systems
8sectors held responsibility in
Where we come in

Four problems,
four distinct AI architectures.

Tell us your situation →
01 Problem GenAI / RAG

Company knowledge is scattered, and finding an answer burns hours

Contracts, procedures, product documentation and past correspondence sit in different systems. New joiners take months to catch up, and the support team answers questions that have already been answered before.

Our solution

Enterprise search and assistant

We build a retrieval layer that runs on the organisation’s own documents. Every answer is linked back to the paragraph it rests on, and the data never leaves your environment.

  • BeforeHours in documents to find an answer
  • AfterAn answer in seconds, with its source visible
02 Problem GenAI

Unstructured text is turned into data by hand

Free-text supplier output, PDF invoices, emails and form content are keyed into the system by people. As volume grows, cost and error rate grow with it.

Our solution

From text to structure

We convert free text into a defined schema. Output passes schema validation, and records that fall below the confidence threshold are not accepted automatically — they are routed to a person.

  • BeforeEvery record entered by hand
  • AfterMost of it automatic, only the exceptions reach a person
03 Problem Agentic AI

The development cycle slows under repetitive work

Code review, writing tests, release notes, dependency updates and recurring fixes take a serious share of the team’s time. As the team grows, so does that load.

Our solution

An automated development pipeline

We place agents into the development pipeline that work within defined boundaries. Every step passes validation, decisions leave a trace, and the points that require human approval are marked explicitly.

  • BeforeRepetitive work eating engineering time
  • AfterThe team focused on product decisions, the routine left to the pipeline
04 Problem LLM production engineering

The AI experiment works, but it cannot go to production

The demo convinces, the production decision does not come: output quality cannot be measured, cost cannot be forecast, and nobody knows what happens when the model or the provider changes.

Our solution

Taking flows into production

We make multi-step LLM flows measurable: evaluation sets for output, cost and latency monitoring, provider-independent abstraction and a path back when something does not work.

  • BeforeA demo, but no production decision
  • AfterA service that is measured, monitored and reversible
Results in production

Where we took AI to production,
and what changed for the client.

All work →
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 →
%20lower cloud cost, same workload
Travel Distribution Platform

Multi-supplier inventory came together in one model, and the same volume is now served at 20% lower cloud cost.

Obilet’s bus, air and rail inventory was fed by suppliers that each had their own data model and their own failure behaviour, so every new integration put the search and booking flow at risk of regression. We normalised the inventory into a single internal model, solved station matching through geographic proximity and GIS, isolated supplier-side failures and reworked resource usage on AWS. The platform now runs independently of any one supplier, holds predictable performance through peak season, and serves the same workload at roughly 20% lower cloud cost.

Read the case study →
How we work

From diagnosis to continuity,
measurable at every stage.

Start with a diagnosis →
01 1–2 weeks

Diagnose

We examine the process, the data and the constraints, and identify where AI will return the most. If no suitable place emerges, we say that plainly too.

02 2–4 weeks

Prove

A working prototype on your real data, with measurement. The decision to continue rests on a success criterion agreed in advance, not on a hunch.

03 6–12 weeks

Production

Going live together with security, authorisation, monitoring, evaluation sets and documentation. Everything we build belongs to you.

04 ongoing

Continuity

Model and flow updates, quality and cost tracking, and extending scope as new needs appear.

If the diagnose phase concludes that AI will not pay off here, we say so; selling work that will not function is expensive for both sides.

Frequently asked

The five questions
we hear most.

AI & data engineering →
How long does an AI project take?

Diagnose and prove together usually take 4–6 weeks; taking it to production needs another 6–12 weeks depending on scope. We give you a firm timeline not as a guess but at the end of the diagnose phase, once we have seen the data and the integration surface.

What kind of data do you need to start?

Examples that show what a correct result looks like. A large dataset is not required; a small, well-documented set of cases is usually enough to begin. During the diagnose phase we define the inputs, the expected outputs and the acceptance criterion together.

Does our data leave our environment?

Not if you do not want it to. We can set the whole thing up inside your own cloud account; which data goes where, what is sent to which provider and how long it is retained are all defined in writing up front.

Will it integrate with our existing systems?

That is exactly where measurable benefit arrives fastest. We build AI components not as a separate island but as a layer that connects to the systems your data already lives in.

How is cost determined?

By scope, by depth of integration and by what production readiness requires. We give a fixed quote after the diagnose phase. We also make the running cost of model usage predictable from the start; a surprise bill is a failure on our side too.

Let’s work together

Let’s find where AI actually pays off in your business.

Tell us which process you want to speed up and what data you have; in the diagnose phase we will define a measurable target together.

Start a project → See our work