Enterprise AI.

AI that runs on your ERP data and survives production.

We build AI systems that run on your own data: decision support over ERP data, sourced question answering across documents and contracts (RAG), agents that automate processes, and LLM features embedded in your existing software.

What this service covers

Setup and infrastructure

On-premise LLM deployment
Cloud LLM deployment
Model serving and capacity planning
Access, logging and authorisation layer

Knowledge retrieval

RAG system setup
Enterprise document indexing
Question answering over ERP data
Cited answers and verification

Model adaptation

Fine-tuning
Prompt and context engineering
Model evaluation and selection

Agents and automation

AI agent development
Process automation (documents, approvals, reporting)

Decision support

Decision support systems on ERP data
Feasibility and pilot studies

What are enterprise AI solutions?

The most valuable use lies in the structured data an organisation already owns. Order, production, costing and procurement data sits in the ERP in an orderly form, so the model does not have to guess; it reads the number from the system and interprets it. This is why quotation preparation, request classification, procurement comparison and report interpretation return results fastest.

Most AI projects die at the demo stage. The cause is not model choice but never having met production reality: real data is dirty, real users ask unexpected questions, and real cost shows up on the first invoice. We build the work under production constraints from the start.

With Retrieval-Augmented Generation (RAG) we build question answering over your own documents, contracts and knowledge base. The model does not invent information from outside; it takes its answer from your records and shows which one it relied on.

AI agents carry out multi-step workflows rather than returning a single answer: classifying a request, pulling data from the relevant system, applying the rule, and writing the result. In agent architecture the real question is not the model's intelligence but what happens when it fails. We design for that from the start.

Instead of direct access we work within authorisation and security boundaries: only as much data as the user and the process need enters circulation. For features embedded into your existing product we use a provider-independent abstraction layer, so your code does not change when the model does.

Who is enterprise AI right for?

Organisations with an established ERP and orderly data that want decision support from it
Operations teams with manual, rule-based and repetitive processes
Organisations holding a large document archive they cannot reach into
Teams that ran an AI pilot but never reached production
Software companies adding AI features to their product

How does an AI project run?

01
Discovery and feasibility
We examine your problem, your process and your data. The output of this stage is not a proposal but an honest answer: can this be solved with AI, if so with which approach, and if not, why.
02
Pilot
A narrow version that nevertheless works end to end. Tested with your real data and real users. Success criteria are defined numerically from the start.
03
Production
Scalable architecture, authorisation, monitoring, cost control, security hardening and rollout. In AI systems the real work begins here.
04
Improvement
Accuracy and cost improvement driven by usage data and logs. AI systems are not built and abandoned; they are measured and tuned.

What do you get from an AI project?

A working system: source code and infrastructure definitions included, entirely yours
An evaluation set: automated tests that measure the system's accuracy
Authorisation and data flow map: who reaches which data, within which boundary
Cost model: per-use model spend and infrastructure
Architecture documentation and handover training

Frequently asked questions about enterprise AI

What determines how long an AI project takes?: Data quality determines the timeline more than model choice does. If your data is orderly and accessible the first working version arrives quickly; if it is scattered, data preparation becomes the longest stage. Once the scope is clear, we share the project schedule and its duration.
Should I choose RAG or fine-tuning?: In the large majority of cases, RAG. Fine-tuning does not teach a model new information; it teaches behaviour and form. If your information changes often, if you need to cite sources, or if your data volume is large, RAG is the right answer. We add fine-tuning when a specific language, tone or output format is required.
If we give our ERP data to AI, will it leave our systems?: That is your decision and we shape the architecture around it. There are three options: a cloud model under enterprise API terms (data is not used for training), cloud hosted within the European region, or an open model running on your own servers. For data under data-protection regulation we often use the third. Which data goes where is settled in writing at the start.
What happens when the AI answers incorrectly?: We build the system accounting for it. The source of an answer is shown so the user can verify it; cases below the confidence threshold are handed to a human; accuracy is measured with regular evaluation tests. The error margin does not reach zero, but it becomes manageable and measurable.
Which model do you use?: We do not tie ourselves to one model. The architecture is model-independent: the provider layer is abstracted and the model becomes a configuration value. When a better or cheaper model appears, a single line changes rather than the code.
What does an AI project cost?: There are two components: build and run. The build is a project price set once the scope is clear. The running cost is per-use model spend plus infrastructure, produced as a concrete monthly estimate from expected volume.

Our other services

ConsultingConsulting that sees the process on the floor and builds it in the system.IntegrationWe fix the place where your systems stop talking to each other.

Let's start

A short intro call is enough. We'll scope it together from there.

Get in touch

What this service covers

Setup and infrastructure

On-premise LLM deployment
Cloud LLM deployment
Model serving and capacity planning
Access, logging and authorisation layer

Knowledge retrieval

RAG system setup
Enterprise document indexing
Question answering over ERP data
Cited answers and verification

Model adaptation

Fine-tuning
Prompt and context engineering
Model evaluation and selection

Agents and automation

AI agent development
Process automation (documents, approvals, reporting)

Decision support

Decision support systems on ERP data
Feasibility and pilot studies

Related project: Deckent, our agent orchestration project

What are enterprise AI solutions?

Who is enterprise AI right for?

Organisations with an established ERP and orderly data that want decision support from it

Operations teams with manual, rule-based and repetitive processes

Organisations holding a large document archive they cannot reach into

Teams that ran an AI pilot but never reached production

Software companies adding AI features to their product

How does an AI project run?

Discovery and feasibility

We examine your problem, your process and your data. The output of this stage is not a proposal but an honest answer: can this be solved with AI, if so with which approach, and if not, why.

Pilot

A narrow version that nevertheless works end to end. Tested with your real data and real users. Success criteria are defined numerically from the start.

Production

Scalable architecture, authorisation, monitoring, cost control, security hardening and rollout. In AI systems the real work begins here.

Improvement

Accuracy and cost improvement driven by usage data and logs. AI systems are not built and abandoned; they are measured and tuned.

What do you get from an AI project?

A working system: source code and infrastructure definitions included, entirely yours

An evaluation set: automated tests that measure the system's accuracy

Authorisation and data flow map: who reaches which data, within which boundary

Cost model: per-use model spend and infrastructure

Architecture documentation and handover training

Frequently asked questions about enterprise AI

What determines how long an AI project takes?

Data quality determines the timeline more than model choice does. If your data is orderly and accessible the first working version arrives quickly; if it is scattered, data preparation becomes the longest stage. Once the scope is clear, we share the project schedule and its duration.

Should I choose RAG or fine-tuning?

In the large majority of cases, RAG. Fine-tuning does not teach a model new information; it teaches behaviour and form. If your information changes often, if you need to cite sources, or if your data volume is large, RAG is the right answer. We add fine-tuning when a specific language, tone or output format is required.

If we give our ERP data to AI, will it leave our systems?

That is your decision and we shape the architecture around it. There are three options: a cloud model under enterprise API terms (data is not used for training), cloud hosted within the European region, or an open model running on your own servers. For data under data-protection regulation we often use the third. Which data goes where is settled in writing at the start.

What happens when the AI answers incorrectly?

We build the system accounting for it. The source of an answer is shown so the user can verify it; cases below the confidence threshold are handed to a human; accuracy is measured with regular evaluation tests. The error margin does not reach zero, but it becomes manageable and measurable.

Which model do you use?

We do not tie ourselves to one model. The architecture is model-independent: the provider layer is abstracted and the model becomes a configuration value. When a better or cheaper model appears, a single line changes rather than the code.

What does an AI project cost?

There are two components: build and run. The build is a project price set once the scope is clear. The running cost is per-use model spend plus infrastructure, produced as a concrete monthly estimate from expected volume.