Enterprise AI · from audit to production

AI solutions for measurable business goals.

We identify where AI is justified, validate the assumption through a working pilot, then integrate and measure the production solution. Audit, private knowledge base, AI assistant and agent - with named business and technology accountability.

Use-case and ROI-based prioritisation
Private, permission-aware data handling - data stays in your own environment
PoC, implementation, monitoring and further development from one team

Request an AI fit consultation View the AI Audit

Brief professional discussion · use case, data and risk · no obligation

Trusted by · AI projects

100+Projects delivered

13+Industries

5.0 · ClutchIndependent reviews

Telebus + taxi registry

Payment platform websites

E-commerce

Enterprise systems

E-commerce

Roadside assistance app

Healthtech · EHR

City-guide mobile app

Many companies already use AI: few know where it truly pays off.

Familiar situations

The question is not whether your company uses AI, it is whether it delivers measurable business results.

Problem · 01

„We don’t know where to start with AI adoption"

Plenty of ideas, but nobody knows which will pay off quickly and which won’t.

Solution

AI Readiness Audit + Opportunity Map, we map which of your processes hold real business potential.

Problem · 02

„We use AI, but we don’t measure the results"

Employees already use ChatGPT / Copilot, leadership expects efficiency gains, but there is no data.

Solution

AI Efficiency Assessment: time savings, error rates, and output quality measured with KPIs.

Problem · 03

„Our developers paste customer data into ChatGPT"

The dev team uses AI tools, but unchecked; the codebase and customer data may end up in public models.

Solution

Private RAG + zero-data-retention enterprise AI tooling: data stays in your own tenant.

Problem · 04

„Our legacy systems are slowing us down"

5-10-year-old systems, key-person dependency, missing documentation, hard to change.

Solution

AI-assisted legacy rewrite: code analysis, documentation, and modularization in an agent-based workflow.

Problem · 05

„We have a lot of internal knowledge, but it’s hard to search"

Thousands of documents, policies, contracts, support tickets: search is slow, reuse is hit-or-miss.

Solution

Private RAG + enterprise AI assistant: structured, verifiable, permission-aware answers on internal data.

Which one sounds familiar? A 30-minute call tells you what pays off.

30-minute call

Which situation sounds familiar? We have a measurable answer for each.

Familiar situations

5 common situations ·
click to switch the solution

The situation: pick oneThe AP4 solution for your pick

For this situation: „Our developers paste customer data into ChatGPT"

The solution

Private RAG + zero-data-retention enterprise AI tooling: data stays in your own tenant.

Private RAGZero-data-retentionOwn tenantEU AI ActLet’s discuss it in 30 minutes1-page proposal
within 2 business days

Definition · in 1 paragraph

Definition

What is enterprise AI consulting?

The goal of enterprise AI consulting is to help a company identify the AI use cases that carry real value, assess the data, security and operational prerequisites, and get from pilot to production in a controlled way. Beyond the decision material, AP4 also builds the prototype and the production solution - the strategy, the prototype and the production-grade code all come from the same senior team.

In short

AP4 Digital’s Budapest-based team provides enterprise AI consulting and implementation: private RAG systems, AI agents and assistants, built with zero-data-retention AI tooling, EU AI Act and GDPR-compliant processes, from strategy to production-grade code by a single team.

Service:Enterprise AI consultingFocus:Private RAG · agents · assistantsCompliance:EU AI Act · GDPRRegion:HU · EU

Two distinct things: AI-native delivery and an AI solution built for you

How we deliver

AI-native delivery

AP4's own product-development method: AI supports analysis, design, engineering, testing and documentation with named professional oversight.

What you buy

Enterprise AI service

A solution designed for your process, your data and your accountability structure - this is the business solution you buy.

For both, we define data processing, human controls and acceptance criteria separately.

Which use cases do we work with?

internal knowledge bases and search
document processing
customer-service assistants
decision support
content and data verification
AI agents embedded in workflows

We recommend a use case only when all four are in place: an identifiable user · a data source · a business metric · an accountable process owner.

Joint prioritisation of value and risk

Alongside expected time or cost savings, we assess the following:

the cost of errors
human controls
data quality
model dependency
security
compliance
operability

A good pilot is not an impressive demo but a measurable decision on whether to proceed.

Data processing and supplier terms

Data processing depends on the provider, region and contractual configuration selected for the project. At the start, we define what data may enter an AI tool, which retention and training settings apply, and when a private or self-hosted environment is required.

The data processing agreement and supplier documentation set out the guarantees provided.

AI Act and accountability

We classify the solution based on its actual use, affected parties, data and risk.

This page does not provide legal advice. For higher-risk or regulated use cases, we involve accountable legal, privacy and security professionals and design documented human oversight.

AI consulting: from assessment to a production system.

Service path · assessment → validation → implementation

We help you identify the AI use cases that carry real value, assess the data, security and operational prerequisites, and get from pilot to production in a controlled way. Beyond the decision material, we also build the prototype and the production solution.

Start here

1. Entry point

Assessment and prioritisation - AI Audit

A structured executive and technology assessment: where the real business value is, with what data and risk conditions. The output is a prioritised use case list, ROI estimate and roadmap.

Who is it for?

When there are several AI ideas but no priority - or you want a data and risk picture before a pilot.

Rapid: 5 working days · Full: 2–4 weeks

AI Audit details

2. Go / no-go decision

Validation - PoC on real data

We validate the selected use case on real data, against success criteria agreed up front. Production development starts only after proven value.

Who is it for?

When the candidate use case exists but accuracy and behaviour must be proven before a decision.

4–6 weeksfrom €9k

Let's scope the PoC

3. Production system

Implementation and operations

Private RAG, AI agents, integration - with evals, monitoring, cost control and human oversight. The pilot becomes a measured production system.

Who is it for?

When the validated solution needs to go to production safely and stay operated.

6–12 weekstypically €30–125k

Let's talk about your AI opportunity

Typical use cases

Company knowledge assistant

Private RAG over internal documents with source-cited answers - data stays in your own environment.

Customer support automation

Triage and draft replies across email, chat and voice, with human approval.

Reporting and decision-support agent

Multi-step, tool-using agents: query, analysis, report - in a controlled process.

Document processing

Structured extraction from contracts, invoices and forms, with verification and logging.

AI features in an existing product

AI capability built into your existing software - with evals, cost control and fallbacks.

Fixed-fee audit and PoC. Production: custom pricing with a written SLA.

Pricing · 3 packages · audit → PoC → production

The audit and the PoC are fixed-fee, with outcomes agreed up front. Production implementation is custom-priced (typically €30–125k), with a written SLA - the budget is locked after the assessment.

Auditfrom €3,800· 5 business days

You don’t know where to start yet. We assess AI readiness and prioritize use cases.

Process audit5–7 use case matrixROI estimate1-page proposalExecutive presentationRequest an auditFIXED FEE + outcome guarantee

Most clients start herePoC (Proof of Concept)from €8,800· 4–6 weeks

You know where you’d start, but you’re not sure it will work. We build a live prototype and find out.

Working prototypeOn real dataSuccess criteriaCost modelScaling decision pointPoC detailsFIXED FEE + outcome guarantee

Implementationcustom pricing· typically 8–16 weeks

You know what you want and need a working system. From PoC to production.

Private RAG / AgentIntegrations (SAP, M365, CRM, app)Production deployMonitoring + Evals3 months of supportLet’s talkT&M or fixed with SLA

Production implementation is typically €30–125k, we lock the exact budget after the audit, with a written SLA.Request a preliminary proposal in 2 business days

Process · 4 phases · 2–4 weeks

What does an AI Readiness Audit look like in practice?

Structured, documented, measurable. Every phase ends with a concrete deliverable, not a general impression, but something leadership can base a decision on.

week 1

Discovery

NDA, stakeholder interviews, data and system mapping, assessment of current AI tool usage.

interview notes
system architecture sketch
AI tooling inventory

weeks 2–3

Analysis

Identifying use cases, value × complexity × risk prioritization, ROI estimation.

prioritized use case list
ROI estimate
risk report

weeks 3–4

Proposal

Roadmap (30/60/90 days), pilot scope, technology stack recommendation, EU AI Act checklist.

1-page executive summary
roadmap
pilot scope
compliance checklist

week 4

Handover

Executive workshop, Q&A session, decision support. Optionally: continuing with pilot delivery.

executive workshop
decision support material
go/no-go criteria

−70% response time · 2× ticket close rate

Internal measurement · 3 months · client name under NDA

"The AI customer service assistant cut average response time by 70% in 3 months, and our junior team now closes 2x as many tickets."

Customer Operations Lead

enterprise client (NDA)

With us, advice becomes a working system. Not a PPT.

Why AP4 Digital · 7 differentiators

10+ years of software engineering

Not a slideware business. 100+ delivered projects across 13+ industries: what we recommend, we can also build.

AI-native way of working

Agent-based workflows, Claude Code, AI-assisted legacy analysis. 100% of the team uses them weekly.

Private RAG · data stays in your tenant

Zero-data-retention LLMs, on-prem or client-cloud deploys. Audit material, code, and business data never leave their owner.

EU AI Act-aware

Transparency obligations apply from 2 August 2026; high-risk rules follow from 2 December 2027 and 2 August 2028. Every audit maps the obligations relevant to you.

Fixed price, fixed deadline, written SLA

The proposal is 1 page, the quote is detailed, the SLA is in writing. No "time-and-materials" tricks.

A second opinion, too

If we are not the right partner, we will say so, and recommend someone who is. The 30-minute call is worth something either way.

Hungarian and English working languages

100% of the team speaks English. We also work for international clients (DACH, UK, Nordics), with time-zone overlap.

Dienes István - managing director, AP4 Digital

A message from the managing director

"In AI consulting we hand over a working system, not a PPT, or we don’t put ourselves forward. If I feel another team would be a better partner, I’ll tell you that too."

Dienes IstvánManaging director · AP4 Digital · Since 2013

A senior team that builds, not just presents.

Team · senior · Budapest

We have worked together since 2013. 70% of the team is senior (8+ years), 100% speaks English, and every project includes someone who has shipped a live RAG / LLM system to production in the past 12 months.

"After the 30-minute call, we received the 1-page proposal within 2 business days. Concrete, with numbers, no generalities."

- CTO · B2B SaaS, ~80 people

10+

years of software engineering

100+

delivered projects

13+

industries

5.0★

Clutch · 11 reviews

AI opportunity map: 7 use cases worth starting with today

Free download · PDF · 12 pages

Downloadable PDF (12 pages): the most common AI use cases in enterprise environments, with estimated ROI, implementation difficulty, and data security considerations. Broken down by industry (banking, retail, manufacturing, healthcare, HR, customer support, legal).

Privacy: we only send this PDF, no bulk mailing lists. You can unsubscribe anytime.

AP4 Digital · 2026

Free PDF · 12 pages

AI Opportunity Map

7 use cases worth starting with today: ROI, difficulty, data security.

BankingRetailIndustryHealthHRSupportLegal

Start with a specific process, not with AI.

30 minutes · free · under NDA if needed

Who you’ll talk to

Dienes István - managing director, AP4 Digital

10+ years of software development · personally leads the discovery call

In the first consultation, we jointly select the smallest measurable next step with an acceptable level of risk.

NDA immediately, if the topic is sensitive
A preliminary written proposal within 2 business days: scope, time & budget range
A detailed, committed proposal after discovery
A second opinion if another team would be better

Emailhello@ap4.hu Phone+36 20 344 6913

16 questions

FAQ.

* frequently asked questions

Enterprise AI: what most people ask.

Still have a question? Email hello@ap4.hu, we reply within 2 business days.

Start with a specific business process, a known user and a measurable problem. For a single use case, the Rapid AI Audit is the right entry point; for broader organisational questions, choose AI Readiness.

A specific guarantee can be made only from documentation for the selected provider, region and contractual configuration. We record this at the start of the project together with the DPA and supplier terms.

When the process can be automated more cheaply and reliably with deterministic rules, suitable data is unavailable, value cannot be measured, or error risk cannot be managed through human controls.

Enterprise AI consulting is a service where an expert team identifies, prototypes and implements AI use cases as production systems in a corporate environment. The difference from classic consulting: the deliverable is a working system, not a strategy document.

An AI audit starts from €3,800, a PoC from €8,800, and a full production implementation typically lands between €30–125k depending on use-case complexity. AP4 Digital also works on fixed-price audits and PoCs, so the cost is predictable up front.

Private RAG (Retrieval-Augmented Generation) is an AI architecture where the model answers from the company’s own documents, and the data never leaves the company tenant. This preserves corporate data security policy and prevents data leakage to external providers.

An average AI project breaks into 3 main phases: audit (1 week), PoC (4–6 weeks), production implementation (8–16 weeks). The full cycle is typically 12–25 weeks, from audit to live system.

Yes. AP4 Digital uses a private RAG architecture: client data stays in your own tenant (Azure / AWS / GCP), we work under NDA, with an ISO27001-compatible process. We never send data to an external provider to train a model.

An AI consultant hands over strategy and a roadmap, but others write the code. An AI development agency delivers strategy AND a working system, with the same team. AP4 Digital is the latter, backed by 10+ years of software engineering.

AP4 Digital has delivered AI projects across 13+ industries for companies large and small, including: public transit (BKK), finance and payments (Simple), education (Corvinus), healthcare (Swiss Clinic), manufacturing (Alcoa), insurance (Europ Assistance), tourism (TMRW Hotels), automotive (UNIX Autó).

That’s what the audit phase is for (from €3,800, 5 working days): we review your business processes, rank 5–7 possible AI use cases in a matrix by ROI and feasibility, and email a 1-page executive proposal. If you’re not interested afterwards, that’s it, no obligation.

Book a 30-minute call on Calendly. We discuss the business context, your core process and AI maturity. Within 2 business days we email a 1-page proposal with 3 possible directions and an estimated scope. From there, you decide how to proceed.

Transparency obligations apply from 2 August 2026, and the rules for high-risk AI systems (e.g. HR decision support, credit scoring, healthcare, critical infrastructure) from 2 December 2027 and 2 August 2028 respectively, following the 2026 EU amendment. The obligations include a risk management system, data governance, technical documentation, logging and human oversight. AP4 Digital walks through the EU AI Act checklist in every audit, so you learn in time whether your planned system is affected.

Already in the analysis phase we rank use cases in a value × complexity × risk matrix, and every recommendation comes with an ROI estimate: hours saved, shorter turnaround, lower error rates. The PoC has pre-agreed success criteria and a cost model, so the scaling decision rests on numbers, not impressions.

The implementation package includes 3 months of support, with monitoring and evals. After that, we can continue operations and further development on a monthly retainer, with a written SLA. The full source code and documentation remain yours.

It depends on the use case. For large language models: Claude (Anthropic), GPT (OpenAI), Llama (Meta), Mistral. For embeddings: OpenAI ada-002, Cohere, local options. Selection criteria: accuracy, cost, data locality and latency.

All 30 questions in one place: full FAQ →