Agents with defined tools
The agent can call only the functions you give it: look up an order, update a record, open a ticket. Nothing outside that boundary.
AI engineering · Europe and USA
AI integration for systems already in production.
We connect language models, document retrieval and AI automation to the systems your clients already run: ERP, commerce platforms, internal applications and event flows. We do not build models. We build the integration, control and observability layer that makes a model usable in production.
The service
What AI integration services actually involve.
Most enterprise AI work is not about the model. It is about everything around it: where the data comes from, how it is reduced to useful context, what happens when the model answers badly, what a call costs, and how you show afterwards what was decided.
That is the work we have been doing on systems integration for years. An AI service is another external system to connect, with mapping, authentication, retries, cost limits and monitoring. The difference is that it answers non-deterministically, so the controls matter more, not less. That layer is what AI integration services should actually be buying you.
- Data retrievalWe connect the model to real sources: catalogues, orders, documents, internal knowledge bases.
- Behaviour controlVersioned prompts, structured outputs, validation, and fallback paths when a response is not usable.
- Cost and latencyCaching, per-task model selection, and spend limits monitored like any other resource.
AI agent development
An AI agent development service grounded in your systems.
A useful enterprise agent is not a conversational demo. It is a process that reads real data, takes a limited number of verifiable actions, and stops when it is not confident.
Retrieval and context (RAG)
Document indexing, semantic search and context assembly, so answers come from your content rather than the model’s memory.
Human in the loop
Steps that change data or speak to a customer can require explicit approval before they run.
Traceability
Every call, input, output and cost is logged, so unexpected behaviour can be reconstructed rather than guessed at.
Build or buy
Where custom AI development beats a platform.
We will say this early: for many use cases an off-the-shelf platform is the right answer, and we will tell you so.
Build custom when
- The logic depends on proprietary data that lives in your systems, not in a SaaS tool.
- The workflow has to sit inside an existing ERP, commerce platform or internal process.
- Data residency or confidentiality rules out sending the content to a third-party platform.
- Volume makes a platform’s per-seat cost worse than a direct integration.
Use a platform when
- The use case is generic and well covered by an existing tool.
- You need to validate an assumption before funding any development.
- There is no dataset or internal process yet for the AI to work against.
Cost
What AI agent development costs.
AI integration cost has two components that behave differently. The engineering is one-off and can be estimated like any other development work. Model consumption is recurring, variable, and grows with usage — it is the line that surprises teams who did not measure it first.
We start by establishing cost per operation at realistic volume before writing production code, so you know upfront whether the use case holds up economically. Where it needs to come down, we reduce it with caching, smaller models for simple steps, and usage limits.
- One-off engineeringIntegration, controls and observability, estimated by scope like any normal project.
- Recurring consumptionCost per model call, measured at real volume before development starts.
- Spend controlCaching, per-task model choice and limits monitored in production.
Evaluating a supplier
How to compare AI agent development companies.
If you are comparing suppliers, these are the questions that separate a solid integration from a demo. They apply to us too.
Ask what happens when the model is wrong
A serious answer describes validation, fallbacks and escalation. A weak answer describes a better prompt.
Ask for cost per operation
Anyone who has not measured consumption at real volume has not put anything into production yet.
Ask how your data is reached
Indexing, permissions and data residency are architecture decisions, not details.
Ask what gets logged
Without logs of calls, inputs and outputs you cannot verify or improve behaviour.
Governance · EU
Generative AI integration services built to be justified.
Many of our partners serve European clients subject to GDPR and, increasingly, the AI Act. We design each AI integration to be documentable: which data reaches the model, which does not, where it is processed, what is logged, and which steps require a human decision. These are architecture choices, and they have to be made at the start.
See how we workWhat we have delivered
An LLM workflow inside a live content migration.
The hard part of enterprise AI is not the model — it is the integration, and that is work we have been doing for years across ERP systems, commerce platforms and event-driven flows. We have delivered an LLM-driven workflow inside a content migration, generating and reorganising the fields a new CMS required and avoiding a manual rebuild. The same engineering applies when the service on the other end of the API is a language model.
FAQ
Questions before we start.
What does an AI agent development company actually deliver?
A working integration, not a demo: defined tools the agent may call, retrieval over your own content, human approval on steps that change data, logging, and a measured cost per operation. If a supplier cannot describe those five things, they are selling a prototype.
Do you build AI models from scratch?
No, and be wary of anyone proposing that for an enterprise project. We use existing models through their APIs and build the layer that makes them usable: data access, behaviour control, error handling, cost and traceability.
What does AI agent development cost?
Two lines: one-off engineering, which is estimated by scope, and recurring model consumption. We measure cost per operation at realistic volume before development, so you know whether the use case holds up before funding it.
Will our data be used to train the models?
Not under the configurations we use. Major providers exclude API data from training on business plans, and we verify it for whichever provider you choose. Where the requirement is stricter, we look at models hosted in a controlled environment.
Can you work on the systems we already have?
That is the normal case for us. We work across existing ERP systems, commerce platforms, custom applications and event flows, and connect AI services through the same integrations we have been building for years.
What happens when the model returns a wrong answer?
You design for it upfront. Structured, validated outputs, deterministic fallbacks, human approval on steps that change data, and full logs so you can reconstruct what happened.
What experience do you bring to an AI project?
Two things. Delivered LLM work — an AI-driven workflow inside a content migration, described in the linked case study — and years of building the integration layer every enterprise AI project depends on: connecting systems, moving data reliably, handling failure, and keeping cost and behaviour observable. Most production AI work is systems engineering, and that is the part we are strongest at.
Let’s discuss the next engagement
What does your next client project require?
Share the required roles, technology stack, timeline and preferred engagement model. We will assess the fit and available capacity.