LLM features · RAG · Document AI
AI integration company for products and internal systems
In shortWeDevelop is a Romanian software company that integrates AI into existing products and internal systems: document and invoice extraction, retrieval-based search over private data, and agents that work through your own APIs. It suits companies in Romania and abroad that have a specific, repetitive task with enough volume to measure, rather than a general wish to use AI.
At a glance
- Provider
- WeDevelop, a software company from Romania
- Clients
- Romania, the EU, the UK and North America
- Languages
- English and Romanian
- Invoicing
- EUR, GBP or RON
- Pricing
- Quoted after scoping, from your brief
- Contact
- office@wedevelop.ro
01Is this for you
When an AI integration is worth paying for
AI earns its cost on narrow, repetitive work that a person can check. It is usually worth scoping when several of these describe you.
- People re-type data from PDFs, scans or emails into an ERP, a DMS or an accounting system.
- Staff lose time searching manuals, contracts, tickets or a catalogue for answers that already exist in writing.
- The same product or order questions arrive every day, and a person can catch a wrong answer in time.
- A task runs across several steps and systems, and those systems have an API or can be given one.
- You can say how the task is measured today and what accuracy would make automating it acceptable.
02What you get
What an AI integration engagement covers
Most projects begin with one of these cases, inside the stack you already run.
Document and invoice extraction
Fields from PDFs, scans and emails become structured records in your ERP or accounting system. Anything below the confidence threshold waits in a review queue.
Search and answers over private data
Retrieval-augmented generation over documents, tickets or product data, using pgvector or Qdrant, with each answer linked to its source and your access rights respected.
Agents connected to your systems
Assistants that read from and act in an ERP, CRM or helpdesk through APIs, with permissions, an audit trail and a fixed limit on what they may change.
Support assistants
Customer-facing assistants grounded in your own content, which pass the conversation to a person when they are unsure or the question is outside their remit.
Classification and content at volume
Ticket routing, lead scoring, product descriptions and catalogue translation, where the volume is high and a sample can be reviewed.
Evaluation and guardrails
An evaluation set written before the feature, output validation, prompt regression tests and production monitoring, so accuracy is a number rather than an impression.
03Which option
No AI, a ready-made tool, a commercial API or a self-hosted model
The first decision is whether the task needs a model at all. The second is where the data may go, and that usually settles the rest.
| Option | Fits when | Trade-off |
|---|---|---|
| Rules, a better form or a plain integration | The task is deterministic and the inputs are predictable. | It will not cope with free text or messy documents, but it costs less to run and is easier to trust. |
| Ready-made AI tool | The need is generic, the data may leave your perimeter and nothing has to be written back into your systems. | You get little control over accuracy, data handling and how deeply it connects to your processes. |
| Commercial model through an enterprise API (Claude, GPT) | You want strong accuracy early and the API tier excludes your inputs from training. | Cost grows with usage and your data passes through a third party, so what you send has to be kept to a minimum. |
| Open model on EU infrastructure | The documents are sensitive or the data has to stay inside your perimeter. | You pay for inference capacity and for running it, and accuracy has to be proven on your own evaluation set. |
Many systems combine these: rules for the easy cases, a cheaper model tier for most of the volume and a stronger one for the hard cases. If an option without AI solves the task, that is what we will recommend.
04Cost
What decides the cost of an AI integration
There is no price list, because the model is rarely the expensive part. The build is quoted after scoping, and the cost of running the feature is estimated separately.
| Factor | Why it matters |
|---|---|
| Data quality | Clean digital documents need far less work than mixed scans or layouts that change from one supplier to the next. |
| Systems to write into | Reading is simpler than acting. Every ERP, CRM or helpdesk action needs permissions, logging and failure handling. |
| Accuracy required | Evaluation and guardrails grow with the stakes, so a task with human review costs less than one where each error is expensive. |
| Where the model runs | Self-hosted or EU-hosted inference adds infrastructure to build and operate, compared with an enterprise API. |
| Volume | Tokens or inference hours per operation, multiplied by expected volume, set the monthly running cost. Caching and cheaper model tiers bring it down. |
| Review workflow | A queue for uncertain results is a small application in its own right, with its own users, rules and reporting. |
The running-cost estimate arrives with the build quote, so you can check that the feature still makes sense at full volume before paying for it.
05How to start
From a task description to a measured rollout
Describe the task
Tell us what people do by hand today, how often, in which systems, and what a wrong result would cost.
Scoping on sample data
We look at a sample, anonymised if needed, agree what good looks like and decide whether AI is the right tool at all.
Evaluation set, then prototype
Expected results are written down first, so the prototype is measured against them rather than demonstrated once.
Rollout behind a feature flag
The feature ships in your current stack, is compared with a baseline and is widened only when the numbers support it.
06For an accurate quote
What to include in your request
A useful first answer depends on the task and the data. You do not need a specification.
- The task, and how many documents, tickets or requests it involves each month.
- A few sample documents or records, anonymised if needed.
- The systems the AI has to read from and write to.
- Any confidentiality or data-residency rules, including those from contracts or your sector.
- Who will review the results, and what error rate you could live with.
07Questions
Questions buyers ask about AI integration
How much does it cost to integrate AI into our software?
The cost is quoted after scoping and depends mostly on data quality, the number of systems involved, the accuracy you need and where the model runs. Expect two figures from us: the build, and a monthly running cost modelled on your volume.
How long does an AI integration project take?
There is no honest figure before seeing the task and a data sample, because the timeline depends on document quality, access to your systems and how quickly you can agree what counts as correct. We keep the first release narrow so that results can be measured early.
Can you add AI to our existing application or ERP?
Yes, provided there is a way in: an API, a database or a file exchange that can be wrapped in an API. The feature goes into your current stack rather than a separate tool, so people keep working in the screens they already use.
Will our data be used to train someone else's model?
No, not in the configurations we deploy, because commercial models are used through enterprise API tiers that exclude inputs from training. Where documents are too sensitive for a third party at all, open models run on EU infrastructure instead.
What happens when the AI gets something wrong?
Wrong outputs are expected and designed for: answers carry their sources, outputs are validated against schemas and uncertain cases go to a person. Anything financial or legal stays a proposal until someone approves it, so a bad result stops in the queue rather than in your accounts.
Do GDPR and the EU AI Act apply to an AI feature?
They often can, so we treat both as design constraints rather than promising an outcome. The work covers documenting data flows, limiting what reaches a model, logging automated decisions and classifying the system against the AI Act risk tiers, while the legal assessment stays with your adviser.
When should we not use AI?
Skip it when the task can be solved deterministically or when nobody is able to check the result. A rules engine, a clearer form or a proper integration is cheaper to run and easier to trust, and in those cases that is the advice you will get from us.
Request a quote
Describe what you need. The enquiry goes to our team, and we reply by email with the next step.