Can you integrate AI into my existing system without a rewrite?

Yes. AI Retrofitting is our core speciality. We use n8n workflow automation and LangChain to connect your existing SQL databases, ERP systems, CRMs, or legacy tools with modern LLMs without modifying your core system. The AI layer sits alongside your existing infrastructure, reading data through secure API connections and delivering document analysis, automated reporting and smart search without requiring a rewrite of what already works.

Which AI models do you work with?

We work with the leading AI models: OpenAI, Anthropic Claude, Google Gemini, and open-source models (Mistral, Llama) for data-sensitive or air-gapped deployments. Model selection is driven by your use case, data volume, latency needs, and budget. For most business applications we use a stronger model for reasoning tasks and smaller models for high-volume classification. All integrations are model-agnostic by design, so you can switch providers without rebuilding the pipeline.

How is our proprietary business data protected?

Your data is not used to train public models: we use API access, where OpenAI and Anthropic do not train on your data by default. With a RAG architecture, your documents sit in a private vector database hosted in the EU, and the model only sees the passages needed for each answer. For regulated industries (healthcare, finance, legal), we deploy via Azure OpenAI or AWS Bedrock with EU data residency and audit logging. Data encryption at rest (AES-256) and in transit (TLS) is standard on every project.

How long does an AI integration take?

Timeline depends on scope. Simple integrations, like adding an AI chatbot to an existing website or automating document classification, move fast. Complex projects involving legacy ERP connections, multi-channel deployment, and custom RAG pipelines with LangChain and Pinecone take longer and run in phases. Every project begins with a short discovery phase to map data flows and define success metrics, which is what confirms your real timeline before development begins.

Often you do not, and we will say so. AI earns its place when a task repeats, involves a lot of text or data, and consumes human time on judgement that is mostly mechanical: triaging enquiries, pulling data out of documents, drafting first replies, searching your own knowledge base. If your actual problem is data being retyped from one system into another, a plain integration solves that more cheaply and more reliably. Before estimating, we ask which task you want taken off people and how many hours a month it costs you today.

Will AI replace our employees?

Not in the projects we build. What AI genuinely takes over is the first step: a suggested reply, a document draft, sorting inbound mail, extracting data from attachments. A person still confirms, corrects and owns the result, because a system nobody checks fails quietly and expensively. The realistic outcome is the same team getting through more, not the team disappearing.

What if the model provider raises prices or retires the model we use?

That is why we do not bind the integration tightly to one provider. Model calls go through a layer that can be switched to another model without rewriting the rest of the system, and your data, prompts and logic stay with you rather than with the provider. Models get retired and prices change; that is the normal state of this market. Writing the application around a single model looks simpler today, but the migration a year from now will not be.

Does our data get used to train models?

Not under the configurations we use. Business API access at the major providers excludes training on your data by default, and we configure and verify that rather than assume it. We also send only what the task needs: if the model has to classify an enquiry type, it does not need the customer's full record. For sensitive data there is also the option of a model running on infrastructure you control, at higher cost and lower answer quality, and that is a decision you make with the full picture.

How do we know the AI is giving correct answers?

By measuring it, not by trusting it. Before launch we assemble a set of real examples with known correct answers and test every prompt or model change against it, because without that, fixing one thing quietly breaks another. In production, edge cases are logged and reviewed, and critical steps require human confirmation before anything leaves the building. A system with none of those three is not production-ready, however well it performs in a demo.

Can we start with a small pilot?

Yes, and we usually recommend it. One clearly bounded task with a measurable outcome answers the real question: does this actually save time, and is the quality good enough for your standard. A pilot that fails is cheap information; a pilot that works gives you a number to justify the next step. What we do not recommend is rolling AI into five processes at once, because then you cannot tell which part worked.

Do we need our own model, or is an existing one enough?

For almost every business case an existing model plus your data is enough. Training your own requires large volumes of labelled data, continuous maintenance, and a cost that rarely pays back. Retrieving from your documents at query time gives the model your knowledge without training, and it updates the moment you edit the document. A custom model makes sense in narrow technical domains with a lot of proprietary data, and we will tell you when that is the case.

What is the EU AI Act and does it apply to us?

The EU AI Act is European regulation that classifies AI systems by risk and attaches obligations to each tier. Most business uses discussed here, such as support, enquiry triage or document processing, fall into the lower-risk tier where the main obligation is transparency: the user must know they are interacting with an AI system. Heavier obligations attach to uses affecting employment, creditworthiness, education or access to services. We establish the category before development because it determines what the system has to log and disclose, and for legal confirmation of your specific case we will point you to a lawyer.

AI Consulting

We build AI integrations into your product or the software you already run: assistants that answer from your own data, document extraction and workflow automation. The model is the easy part; the system around it decides whether it works: your data, the guardrails, the handoff to a person. We build it in TypeScript with LangChain.js, the Vercel AI SDK and n8n, connect it to your ERP, CRM or databases, and test it against real questions before launch. A person approves the risky steps and every run has a cost limit. Your data never trains a public model. Request an AI feasibility check and we'll tell you honestly whether AI moves the needle for you.

Request an AI feasibility check

5.0· 29 Google reviewsSenior EU engineersFull code ownershipGDPR-compliant by default

AI Integration

We add AI to your product or the software you already run (ERP, CRM, your own databases), without rebuilding it.

What Is AI Retrofitting for Legacy Software?

AI retrofitting is the process of adding AI capabilities (natural language search, document summarisation, automated classification) to existing business software without replacing the underlying system. Most established companies operate on legacy platforms that contain years of valuable data locked behind outdated interfaces. Retrofitting unlocks that data.

We build middleware layers using TypeScript, LangChain.js, and REST APIs that sit between your current software and modern AI models. Your team accesses new capabilities through their existing dashboard: no retraining, no workflow disruption. Finding an answer becomes a question in plain language instead of a search through folders.

For example, a logistics company running a 12-year-old ERP can gain automated shipment status summaries, exception alerts, and natural language reporting through our middleware layer. Staff adopt it quickly because the interface lives inside the tool they already use.

When the system underneath still does its job, retrofitting is usually far cheaper than a rebuild, and we tell you when it isn't. For businesses that cannot afford 6–12 months of system replacement, it is the pragmatic path to AI-powered operations.

How much does it cost to connect AI to your CRM, ERP and email?

Four things set the price: how many systems we connect and whether they have an API, the state of their data, how many steps need a person's approval, and the request volume. There is a one-off build cost and a running cost for the AI model and hosting. You get the amount in writing, as a fixed price per phase.

What moves the build price:

Number and type of systems. CRMs such as HubSpot or Pipedrive have documented APIs. An older ERP without an API needs exports, imports or database access, which is more work.
Data quality. Duplicates, incomplete records and different names for the same thing have to be sorted out before AI works on them. Sometimes that is the biggest part of the job.
Approval steps. Every step where a person approves (sending to a customer, posting, a change in the ERP) needs a review screen and a record of who approved what.
Test set. Before launch we build tests from your real messages and documents. The more cases, and the more varied, the more preparation.
Personal data and GDPR. A data processing agreement and rules on who can see what are part of the scope from day one.

Running costs: AI model usage depends on the number and length of requests and the model chosen; hosting depends on where the workflow runs. Every request has a cost limit, so more traffic never becomes a surprise on the invoice. An optional monthly plan covers monitoring, fixes and re-testing after model changes.

How you get a number: describe the systems and the process in the enquiry form, and the engineer who will build it replies in writing with a scope assessment. Starting prices by project type are on the pricing page.

How Does AI Orchestration Handle Complex Tasks?

AI orchestration is the coordination of multiple AI models, tools, and data sources to execute complex, multi-step business processes autonomously. Unlike single-prompt AI interactions, orchestrated agents plan a sequence of actions, execute each step, evaluate results, and adjust course, similar to how an experienced employee works through a procedure.

We build orchestration pipelines using LangChain agents with tool-calling capabilities. A single trigger can initiate a chain: extract data from an uploaded PDF, validate it against your database, generate a summary, update a CRM record, and send a notification, with a person approving the steps that carry risk.

Practical orchestration examples:

Invoice processing: Extract line items from scanned invoices (OCR + LLM), match against purchase orders in the ERP, flag discrepancies, and route approved invoices for payment
Contract review: Parse legal documents, extract key clauses (termination, liability, renewal), compare against standard terms, and highlight deviations for legal review
Customer onboarding: Collect documents, verify identity data, create accounts across multiple systems, and trigger welcome sequences

Each pipeline includes error handling, retry logic, and human-in-the-loop checkpoints for high-stakes decisions.

Practical AI Use Cases by Business Function

AI integration solves specific operational bottlenecks by automating high-volume, repetitive cognitive tasks that currently consume skilled employee time. The highest-ROI applications share a common pattern: structured input, predictable logic, and measurable output. These are the use cases we deploy most frequently.

Automated Data Entry and Document Processing: Invoices, receipts, and forms are converted into structured database entries using OCR and LLM extraction. On standardised documents accuracy is high enough that manual entry becomes a spot check instead of a daily job.
Sentiment Analysis and Ticket Prioritisation: Customer emails, support tickets, and reviews are automatically scored for urgency and sentiment. Critical complaints surface immediately instead of sitting in a queue. High-priority issues reach the right person faster.
AI Features Inside Your Product: Recommendations, smart search, summaries and drafting built into your SaaS, so users get an answer instead of a filter panel. For PGAdmit, our AI recommendation engine lifted search usage 40% and signups 25%.
Content Generation at Scale: Product descriptions, meta tags, email copy, and report summaries generated from structured data inputs. Human review remains in the loop, but first-draft generation removes most of the writing time.
Knowledge Base Search: RAG-powered internal search across company documents, past projects, and regulatory databases. Employees ask in plain language and get an answer from your own documents, with the source shown. The assistant on this site is a live example: it answers only from what we have published.

How We Protect Your Data in AI Systems

Data sovereignty in AI means your business data stays in systems you control, the model only sees what it needs to answer a question, and nothing is used to train public models. For EU-based companies, this is both a competitive advantage and part of what GDPR expects: privacy by design and appropriate security under Articles 25 and 32.

We implement a retrieval-augmented generation (RAG) architecture where your documents are embedded into a private vector database (pgvector on Supabase, or Pinecone) hosted in EU data centres. The AI model receives only the relevant document chunks needed to answer each query; your full dataset is never sent to any external API.

Key data protection measures:

Model isolation: We use API access to the models (OpenAI, Anthropic), where your data is not used for training by default. When data has to stay in the EU, we use EU-hosted models (Claude on AWS Bedrock in Frankfurt, Azure OpenAI in the EU).
Encryption: Data encrypted at rest (AES-256) and in transit (TLS 1.3) at every layer
Access controls: Role-based permissions determine who can query, modify, or delete knowledge base content
Audit logging: Every AI interaction is logged with timestamp, user identity, and data accessed

For industries with heightened regulatory requirements (healthcare, legal, financial services), we deploy in a dedicated EU cloud environment.

How Do You Know the AI Gives Correct Answers?

Before launch, we build a test set from your real questions and documents and check that the AI answers correctly, cites the right source, or says it doesn't know. Assistants show where each answer came from, so your team can verify it. Where a wrong answer would be costly, a person approves before anything reaches a customer. Every request has a cost limit, so a spike in usage never turns into a surprise invoice. After every model update, the same tests run again before the change goes live.

What Happens After Launch?

An AI feature is not finished at launch: models change, your documents change, and users ask questions nobody planned for. After launch, we can look after it on a monthly plan: we review real conversations and failed answers, update the knowledge base, rerun the tests after model updates, watch the cost per request and fix what breaks. New features are separate projects with their own fixed price, so the monthly plan stays predictable.

AI Consulting: Start with a Technical Assessment

AI consulting starts with a structured assessment of your existing software infrastructure, data assets, and operational workflows, to identify the highest-ROI opportunities for AI integration. It replaces guesswork with a prioritised, costed roadmap that your team can execute immediately.

The consultation covers three phases:

Infrastructure audit: We map your current tech stack (databases, APIs, ERPs, CRMs) and assess integration readiness. We identify data quality issues that would undermine AI accuracy if left unaddressed.
Opportunity scoring: Each potential AI use case is scored on implementation complexity, expected time savings, and revenue impact. We prioritise based on the ratio of business value to engineering effort.
Technical roadmap: You receive a phased implementation plan with cost estimates and expected ROI. Phase 1 targets are selected for fastest time-to-value. The roadmap itself is what confirms your real timeline, not a guess upfront.

We operate from Croatia within the EU regulatory framework, ensuring every recommendation accounts for GDPR, data residency, and industry-specific compliance. The output is a document your team can execute with us or independently: no vendor lock-in.

Which Industries See the Fastest AI ROI?

AI integration delivers the fastest measurable returns when applied to high-volume, repetitive cognitive processes, tasks where skilled employees spend hours on work that follows predictable patterns. These are the industries where AI tends to pay back fastest.

Tourism and Hospitality: Automated guest communication (booking confirmations, check-in instructions, local recommendations, review responses) takes most routine questions off the front desk. AI-powered revenue management adjusts pricing dynamically based on occupancy patterns, competitor rates, and seasonal demand signals.

Real Estate and Property Management: AI-driven CRM enrichment scores inbound leads automatically, prioritising agent time on the highest-intent buyers. Document extraction pulls key terms from purchase agreements and lease contracts for instant review, cutting administrative processing time.

Professional Services (Legal, Accounting, Consulting): RAG-powered assistants search internal knowledge bases, past case files, and regulatory documents instantly. Junior staff find the relevant rule or precedent without waiting for a senior colleague, and every answer shows its source so it can be checked. Less time goes into searching, more into billable work.

E-Commerce and Retail: Automated product description generation at scale, intelligent inventory reordering based on demand forecasting, and returns prediction models reduce operational overhead across the supply chain.

Healthcare and Clinics: Appointment scheduling automation, patient intake forms processed by AI extraction, and after-care follow-up sequences with staff reviewing anything clinical, all built with GDPR-compliant data handling for sensitive medical information.

Every engagement begins with a technical audit. We map your existing tools, identify the highest-ROI automation candidates, and deliver a phased roadmap before any development starts.

Custom AI Integration vs Off-the-Shelf Tools

Custom AI integration, custom GPTs and no-code AI platforms serve fundamentally different purposes. Custom integration delivers full data ownership, domain-specific accuracy, and direct connection to your existing systems. Off-the-shelf tools offer faster initial setup but impose vendor dependencies, limited customisation, and data sovereignty concerns, particularly relevant for EU businesses under GDPR.

Criterion	Custom AI (Neviox)	Custom GPTs (ChatGPT)	Zapier AI / Make AI
Data ownership & privacy	Your infrastructure	OpenAI infrastructure	Third-party
Domain knowledge customisation	RAG on your data	Limited	Minimal
Integration with business systems	Native (API, webhook)	Limited	No-code connectors
Model-level cost control	Full	OpenAI pricing plan	Vendor pricing plan
Multi-model support (GPT-4, Claude, Gemini)	Yes, hybrid	OpenAI only	Platform-dependent
Security & EU compliance	Architected in	Configurable	Limited
UI customisation	Full	Chat templates	None
Long-term scalability	Designed for growth	Vendor-dependent	Vendor-dependent

We also work remotely with clients in:

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“Thanks to Neviox Digital's work, Poliklinika Meter achieved a significantly improved digital presence, faster page loads, and a more intuitive user experience on their website. The team delivers reliably, communicates clearly, and responds quickly to needs. Neviox Digital provided expert guidance and tailored solutions throughout the project.”
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“Thanks to Neviox Digital's work, the client has seen a 40% increase in their custom search tool usage and a 25% increase in signups. The team delivers on time, communicates clearly, and responds quickly to needs. Neviox Digital has provided expert guidance throughout the engagement.”
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Frequently Asked Questions

Why Neviox Digital

AI you stay in control of
Before launch we test on your real examples, answers cite their source, a person approves the risky steps, and every run has a cost limit.
Product and automation from one team
The same team builds your website or software and the automation that connects to it, so responsibility never falls between two vendors.
Your code, your accounts
The code, the domain and every account are yours from day one, with no annual licence and no lock-in to us.

Send Us Your Site, Repo or Idea. Our Engineers Reply in Writing.

We reply as soon as we can, usually within a few days, and tell you what we would change first.

Request an audit

What we shipped, what broke and what we would do differently. No news roundups.

What Is AI Retrofitting for Legacy Software?

How much does it cost to connect AI to your CRM, ERP and email?

What moves the build price:

Number and type of systems. CRMs such as HubSpot or Pipedrive have documented APIs. An older ERP without an API needs exports, imports or database access, which is more work.
Data quality. Duplicates, incomplete records and different names for the same thing have to be sorted out before AI works on them. Sometimes that is the biggest part of the job.
Approval steps. Every step where a person approves (sending to a customer, posting, a change in the ERP) needs a review screen and a record of who approved what.
Test set. Before launch we build tests from your real messages and documents. The more cases, and the more varied, the more preparation.
Personal data and GDPR. A data processing agreement and rules on who can see what are part of the scope from day one.

How Does AI Orchestration Handle Complex Tasks?

Practical orchestration examples:

Invoice processing: Extract line items from scanned invoices (OCR + LLM), match against purchase orders in the ERP, flag discrepancies, and route approved invoices for payment
Contract review: Parse legal documents, extract key clauses (termination, liability, renewal), compare against standard terms, and highlight deviations for legal review
Customer onboarding: Collect documents, verify identity data, create accounts across multiple systems, and trigger welcome sequences

Each pipeline includes error handling, retry logic, and human-in-the-loop checkpoints for high-stakes decisions.

Practical AI Use Cases by Business Function

Automated Data Entry and Document Processing: Invoices, receipts, and forms are converted into structured database entries using OCR and LLM extraction. On standardised documents accuracy is high enough that manual entry becomes a spot check instead of a daily job.
Sentiment Analysis and Ticket Prioritisation: Customer emails, support tickets, and reviews are automatically scored for urgency and sentiment. Critical complaints surface immediately instead of sitting in a queue. High-priority issues reach the right person faster.
AI Features Inside Your Product: Recommendations, smart search, summaries and drafting built into your SaaS, so users get an answer instead of a filter panel. For PGAdmit, our AI recommendation engine lifted search usage 40% and signups 25%.
Content Generation at Scale: Product descriptions, meta tags, email copy, and report summaries generated from structured data inputs. Human review remains in the loop, but first-draft generation removes most of the writing time.
Knowledge Base Search: RAG-powered internal search across company documents, past projects, and regulatory databases. Employees ask in plain language and get an answer from your own documents, with the source shown. The assistant on this site is a live example: it answers only from what we have published.

How We Protect Your Data in AI Systems

Key data protection measures:

Model isolation: We use API access to the models (OpenAI, Anthropic), where your data is not used for training by default. When data has to stay in the EU, we use EU-hosted models (Claude on AWS Bedrock in Frankfurt, Azure OpenAI in the EU).
Encryption: Data encrypted at rest (AES-256) and in transit (TLS 1.3) at every layer
Access controls: Role-based permissions determine who can query, modify, or delete knowledge base content
Audit logging: Every AI interaction is logged with timestamp, user identity, and data accessed

For industries with heightened regulatory requirements (healthcare, legal, financial services), we deploy in a dedicated EU cloud environment.

How Do You Know the AI Gives Correct Answers?

What Happens After Launch?

AI Consulting: Start with a Technical Assessment

The consultation covers three phases:

Infrastructure audit: We map your current tech stack (databases, APIs, ERPs, CRMs) and assess integration readiness. We identify data quality issues that would undermine AI accuracy if left unaddressed.
Opportunity scoring: Each potential AI use case is scored on implementation complexity, expected time savings, and revenue impact. We prioritise based on the ratio of business value to engineering effort.
Technical roadmap: You receive a phased implementation plan with cost estimates and expected ROI. Phase 1 targets are selected for fastest time-to-value. The roadmap itself is what confirms your real timeline, not a guess upfront.

Which Industries See the Fastest AI ROI?

Every engagement begins with a technical audit. We map your existing tools, identify the highest-ROI automation candidates, and deliver a phased roadmap before any development starts.

Custom AI Integration vs Off-the-Shelf Tools

Criterion	Custom AI (Neviox)	Custom GPTs (ChatGPT)	Zapier AI / Make AI
Data ownership & privacy	Your infrastructure	OpenAI infrastructure	Third-party
Domain knowledge customisation	RAG on your data	Limited	Minimal
Integration with business systems	Native (API, webhook)	Limited	No-code connectors
Model-level cost control	Full	OpenAI pricing plan	Vendor pricing plan
Multi-model support (GPT-4, Claude, Gemini)	Yes, hybrid	OpenAI only	Platform-dependent
Security & EU compliance	Architected in	Configurable	Limited
UI customisation	Full	Chat templates	None
Long-term scalability	Designed for growth	Vendor-dependent	Vendor-dependent

What Is AI Retrofitting for Legacy Software?

How much does it cost to connect AI to your CRM, ERP and email?

How Does AI Orchestration Handle Complex Tasks?

Practical AI Use Cases by Business Function

How We Protect Your Data in AI Systems

How Do You Know the AI Gives Correct Answers?

What Happens After Launch?

AI Consulting: Start with a Technical Assessment

Which Industries See the Fastest AI ROI?

Custom AI Integration vs Off-the-Shelf Tools

We also work remotely with clients in:

Related Services

Latest Projects

What will your project cost?

Frequently Asked Questions

Can you integrate AI into my existing system without a rewrite?

Which AI models do you work with?

How is our proprietary business data protected?

How long does an AI integration take?

Do we even need AI?

Will AI replace our employees?

What if the model provider raises prices or retires the model we use?

Does our data get used to train models?

How do we know the AI is giving correct answers?

Can we start with a small pilot?

Do we need our own model, or is an existing one enough?

What is the EU AI Act and does it apply to us?

Why Neviox Digital

Send Us Your Site, Repo or Idea. Our Engineers Reply in Writing.

What Is AI Retrofitting for Legacy Software?

How much does it cost to connect AI to your CRM, ERP and email?

How Does AI Orchestration Handle Complex Tasks?

Practical AI Use Cases by Business Function

How We Protect Your Data in AI Systems

How Do You Know the AI Gives Correct Answers?

What Happens After Launch?

AI Consulting: Start with a Technical Assessment

Which Industries See the Fastest AI ROI?

Custom AI Integration vs Off-the-Shelf Tools

We also work remotely with clients in: