Custom AI integration
AI that lives inside your ERP, CRM and in-house systems — not in a separate browser tab.
Custom AI integration for enterprise systems
The hard part is never the model. It is the plumbing between the model and the systems your company actually runs on.
A chat window in the corner of your website solves nothing if order status lives in the ERP, customer history sits in the CRM, invoices are in the accounting system and the actual business rules are encoded in an internal tool someone wrote in 2014. Custom AI integration closes exactly that gap: we connect the model to the place where your data and processes already live, with the permissions, the audit trail and the error handling that a production system requires.
We are a development team, not a slide factory. Develops Hungary has been building enterprise software from Budapest since 2018, and most of our AI work is not greenfield: it means touching live, load-bearing systems without anything stopping. This page is deliberately more technical than a typical service page — if you are evaluating vendors, you should be able to tell from the text whether we know what we are doing.
Why an off-the-shelf SaaS AI tool is not enough
Boxed AI tools are genuinely good at generic work: drafting text, summarising meetings, helping developers write code. But your competitive advantage is not generic. It is in the quoting logic, the exception handling, the framework contracts and the twenty years of history sitting in your database — and a vendor who has never seen your data model cannot encode any of that.
Data islands
A useful answer usually needs three systems at once: stock from the ERP, the customer's contract from the CRM, and the open ticket from the support tool. A SaaS tool sees one of them, at best.
Your process is the exception
Every company has rules that exist nowhere in the vendor's product roadmap. Custom integration lets the model follow your rules instead of an average one.
Permissions and data residency
Uploading contracts or HR files into a third-party tool is a governance decision, not an IT detail. A custom layer keeps the data inside your permission model and your chosen region.
Per-seat licences add up
Above a few dozen users, the annual licence spend on a generic tool often exceeds the one-off cost of building the integration you actually need.
What we integrate with: ERP, CRM and legacy systems
We start from whatever you already run. In practice the integration surface is one of four things: a documented API, a database we can read from a replica, a message queue, or a scheduled file exchange. All four are workable — they just change the effort estimate.
ERP and finance
SAP (OData, BAPI/RFC), Microsoft Dynamics 365 and Business Central over the Dataverse Web API, and Hungarian accounting and invoicing stacks — NAV Online Invoice, Számlázz.hu, Billingo and the usual desktop bookkeeping systems. Typical use cases: invoice and delivery note extraction, supplier data matching, automated posting suggestions.
CRM and sales
Salesforce, HubSpot, Dynamics 365 Sales, Pipedrive or your own CRM. CRM AI automation usually means call and e-mail summarisation written back to the record, automatic lead qualification against your real ICP, quote drafts built from historic deals, and churn signals surfaced before the renewal conversation.
Legacy and in-house
MSSQL and Oracle back ends, SOAP services, AS/400 exports, SharePoint and document management systems, ticketing tools, and internal applications with no documentation left. Where there is no API, we build a thin, read-only interface layer first — safer than letting an AI process write into a system nobody fully owns any more.
integrate
Technical approach: APIs, security, on-premise vs. cloud
We build an integration layer between your systems and the model rather than wiring the model directly into a database. That layer owns authentication, rate limiting, retries, idempotent jobs and the audit log. If a model provider changes its API — or you decide to switch providers entirely — one component changes, not five systems.
For anything that answers questions from your own documents we use permission-aware retrieval: the vector index stores the same access control metadata as the source system, and the query is filtered by the logged-in user's rights before the model ever sees a chunk. This is the single most common thing missing from quick internal AI pilots, and the reason many of them never make it past the security review.
Model choice follows the data, not the hype. Where the content is not sensitive, a hosted API in an EU region with a data processing agreement and training switched off is the fastest and cheapest route. Where data cannot leave the network — healthcare, defence, certain financial workloads — we serve open-weight models on your own hardware or in a private cloud, and we are upfront about the trade-off: somewhat lower model quality, higher infrastructure cost, full control. Everything we ship comes with prompt and response logging, an evaluation set that catches regressions when a model version changes, and a human approval step wherever an action has financial or legal consequences.
The project lifecycle, step by step
Five phases, each with a decision point where you can stop. Nobody should sign a six-month AI project before seeing the first working result on their own data.
1. Discovery workshop (1-2 days)
We map your systems, data sources, interfaces and permission model with your IT team, then pick a use case with a measurable goal. Output: a system map, a risk list and a fixed-price offer.
2. Proof of concept (2-4 weeks)
One narrow use case on real data, with a success criterion agreed in advance. If the PoC misses it, you have spent weeks instead of months — and you own the findings either way.
3. Integration (4-10 weeks)
Production interfaces, error handling, permissions, monitoring, deployment pipeline. We work against your staging environment and never write to live systems without a rollback path.
4. Testing and UAT
Key users test against real cases, the evaluation set runs automatically on every change, and we load-test the interfaces before go-live.
5. Handover and support
Documentation, admin training, source code handover, and an SLA if you want one. We also write down how the system should be re-evaluated when a new model generation arrives.
Custom development or off-the-shelf tool?
A short decision guide. If you land on the left-hand column, we will tell you to buy a licence instead of hiring us.
An off-the-shelf tool is enough when…
- the task is generic: writing, translation, meeting notes, coding assistance
- no sensitive personal or contractual data is involved
- you need something running this week, not this quarter
- one or two systems are involved and both have good native integrations
- you have fewer than a few dozen users, so licence cost stays small
Custom integration pays off when…
- the answer requires data from three or more systems at once
- your own business rules and exceptions decide what a good output looks like
- permissions, auditability or data residency are hard requirements
- the process runs hundreds or thousands of times a month
- the workflow itself is part of your competitive advantage
In practice the answer is often hybrid: a commercial model behind a custom integration layer. You get the model quality of a large provider and the process fit of bespoke software, without training anything from scratch.
Frequently asked questions
How long does a custom AI integration take?
A proof of concept on real data usually takes 2 to 4 weeks. A full production integration typically runs 2 to 4 months, depending on how many systems are involved and how accessible they are. When a legacy system has no usable API and we have to build an interface layer first, add roughly 3 to 6 weeks. We always agree on a written scope and milestones after discovery, so you are never paying into an open-ended project.
What does it cost?
Honest ballpark figures instead of a hidden price list: a scoped proof of concept typically lands between EUR 4,000 and EUR 10,000. A full integration into one or two enterprise systems is usually in the EUR 15,000 to EUR 60,000 range, driven mostly by the number of interfaces, the state of the data and the security requirements. On top of that come model usage costs (often EUR 50 to EUR 500 per month for internal tools) and an optional support retainer. After the discovery workshop you get a fixed-price offer, not an hourly guess.
Which technologies do you work with?
Python and TypeScript on the application side, Node.js and .NET where the host system requires it, PostgreSQL with pgvector or Qdrant for retrieval, Docker and Kubernetes for deployment, and Azure, AWS or European providers such as Hetzner for hosting. On the model side we use Azure OpenAI, OpenAI and Anthropic APIs, plus open-weight models (Llama, Mistral, Qwen) served with vLLM when data cannot leave your infrastructure. We integrate with SAP, Dynamics 365, Salesforce, HubSpot, SharePoint and custom in-house systems over REST, GraphQL, SOAP, OData or plain file exchange.
How do you protect our data, and what about GDPR and the EU AI Act?
Data protection is designed in, not bolted on. We work in EU regions, sign a data processing agreement, switch off training on your data at the provider level, minimise and pseudonymise what is sent to the model, and implement permission-aware retrieval so the assistant can only surface documents the logged-in user is already allowed to see. Every AI-assisted action is logged and auditable. On the EU AI Act: most internal productivity and process systems fall into the minimal or limited risk category, where transparency and documentation are enough — but HR screening, credit scoring and similar use cases are high risk and require human oversight, logging and conformity documentation. We classify your use case during discovery and tell you honestly which obligations apply.
What is the minimum project size?
Realistically, a custom integration starts to make sense from around EUR 4,000 to EUR 5,000 and a three to four week timebox. Below that, an off-the-shelf tool plus light configuration will serve you better, and we will say so instead of selling you a project. The system map consultation itself is free and carries no obligation — if the honest answer is that you do not need custom development yet, you will hear that in the first 45 minutes.
Related services
Integration is one part of the picture. If you are still deciding where AI belongs in your company, start with AI-Driven Process Development or book a Free AI Assessment.
A free 45-minute technical call with developers, not salespeople. Tell us which systems you run — ERP, CRM, accounting, in-house tools — and we will go through where a realistic AI entry point is, what it would take to integrate, and what it would roughly cost.