AI integration services for the systems you already run.
Our AI integration services exist because the hard part is almost never the model. It is the retrieval layer over data that changes hourly, the auth and rate limits on a system built in 2011, the fallback when the provider is down, and the boundary question of who owns the truth. That work is ordinary systems engineering, and we have been doing it since 2005.
Every AI feature that survives contact with a real company turns out to be an integration project. The model is a dependency you configure; the work is everywhere else. What does the model get to see, and how fresh is it. What may it change, and through what interface. What happens when the provider returns a 500 during your peak hour. Which system is the authority when two of them disagree — because they will.
This is the part of AI work that maps directly onto what DTN has always done. SAP-backed pricing, stock and fulfilment logic across MASCOT Workwear's 20+ markets. TTI across 18 regional sites with a PIM feeding 10,000+ products. 13 country sites for Goodiebox with multi-currency and multiple payment providers. None of those involved a model, and all of them were the same problem: making systems that were not designed for each other agree, reliably, at volume.
On the retrieval side, our RAG development services are built for data with a lifespan. Product catalogues, price lists, stock, help content and policy documents change constantly, and a vector index built once and never invalidated is a machine for confidently telling customers last month's price. So we treat freshness as a first-class requirement: incremental indexing tied to your change events, hybrid lexical and vector retrieval because exact SKU and part-number matching is a lexical problem, and a hard rule that live facts such as price and stock are fetched at answer time rather than recalled from an embedding.
The other half is the outbound direction — models given controlled write access to your systems through typed, validated, reversible interfaces. That is the same tool-surface discipline described under agent development, and it applies whether the caller is an agent or a single feature.
The work that sits between a model and a system that matters.
Retrieval over live data
Hybrid lexical and vector search over catalogues, help content and records, with incremental indexing tied to your change events. Exact SKU matching stays lexical, because embeddings are bad at part numbers.
Live facts fetched, never remembered
Price, stock and order state read at answer time from the system of record. Anything else eventually tells a customer something that was true last week.
ERP, PIM and helpdesk boundaries
SAP, Akeneo, Zendesk, Gorgias, Intercom and bespoke back-office systems. Who owns each fact, what is cached, and what happens when one side is unavailable — decided explicitly.
Controlled write access
Typed, validated, idempotent and reversible interfaces for anything a model may change, with approval gates on destructive operations.
Degradation that is designed
Provider timeouts, rate limits and outages handled with fallbacks, caching and a defined reduced-capability mode. An AI feature that takes checkout down with it was integrated carelessly.
Model-agnostic by construction
Claude, GPT, Qwen or a local Ollama model behind one interface, so you can move on price, latency or data residency without rewriting the feature. Avoiding lock-in is a design decision, not a licence negotiation.
How an integration is delivered.
Map the systems and the truth
Which system owns which fact, how fresh each one is, and what the real API and auth surfaces allow. Usually the most valuable artefact of the whole project.
Build retrieval and measure it
Indexing, ranking and freshness, scored against a golden set of your own queries before anything is customer-facing.
Wire the write path
Typed tools, validation, idempotency and approval gates, tested against the failure cases rather than the happy one.
Load, fail and hand over
Behaviour under rate limits, timeouts and provider outages verified deliberately, then runbooks and decisions documented in your repository.
What we commit to on every integration.
The integration evidence on this page is deliberately pre-AI — SAP, PIM and multi-market commerce work from published case studies. It is the honest evidence for this specific claim, and it is the part of AI delivery that most often fails.
The stack.
Common questions.
What do RAG development services actually involve?
Chunking and indexing your content, hybrid lexical plus vector retrieval, ranking, freshness handling, and an evaluation set that tells you whether a change to any of those helped. For commerce data the freshness and exact-match parts are usually harder than the retrieval itself — a catalogue that changes hourly breaks the naive version of this immediately.
Can you integrate AI with our ERP?
Yes, and the boundary design is the real work. Where the ERP owns price, stock and fulfilment, the model gets read access to a projection and a narrow set of gated write tools. We run SAP-backed logic across MASCOT's 20+ markets and TTI's 18 regional sites — see ERP integration.
Will this lock us into one model provider?
Not if it is built properly. Providers sit behind an interface, prompts and evals are version-controlled as your assets, and switching is a configuration change plus an eval re-run. We keep local models via Ollama and Qwen viable as a fallback path specifically so the option stays real.
What happens when the model provider goes down?
Whatever you decided in advance — cached answers, a smaller local model, a deterministic fallback, or an honest unavailable message. What must not happen is the AI feature taking a checkout or a support queue with it, and that is a design decision made before launch, not an incident review afterwards.
Can the model run on our own infrastructure?
Where data residency or cost requires it, yes — open models via Ollama on your own hardware or cloud, with the quality trade-off stated honestly rather than glossed over. For many retrieval and classification tasks a small local model is genuinely sufficient; for hard reasoning it usually is not, and we will say which yours is.
Where to next.
Tell us which systems have to agree.
Name the systems, who owns which fact, and how fresh the data has to be. That conversation tells us more about feasibility than any discussion of models.