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.
Familiar situations
Many companies already use AI — few know where it truly pays off.
The question is not whether your company uses AI — it is whether it delivers measurable business results, runs securely, and is applied in the right places.
Definition · in 1 paragraph
What is enterprise AI consulting?
Enterprise AI consulting (also known as AI consulting for companies, AI adoption consulting for businesses or corporate AI consulting) is a service where an expert team designs and implements agent-based workflows, private RAG (Retrieval-Augmented Generation) systems and AI assistants in an enterprise environment. Unlike classic consulting, the end result is not a strategy document but a working system — the strategy, the prototype, and the production-grade code all come from the same team.
Services · 6 engagement types
AI consulting services: audit, RAG, agents, production.
Not generic AI training. Business and technology decision support + production-grade implementation: where AI is worth using, what results to expect, what risks to plan for, and which first project to start with.
Pricing · 3 packages · audit → PoC → production
Fixed-fee audit and PoC. Production: custom pricing with a written SLA.
No "time-and-materials" tricks. The audit and the PoC are fixed-fee, with an outcome guarantee. Production implementation is custom-priced (typically €30–125k), with a written SLA.
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.
01
week 1
Discovery
NDA, stakeholder interviews, data and system mapping, assessment of current AI tool usage.
- interview notes
- system architecture sketch
- AI tooling inventory
02
weeks 2–3
Analysis
Identifying use cases, value × complexity × risk prioritization, ROI estimation.
- prioritized use case list
- ROI estimate
- risk report
03
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
04
week 4
Handover
Executive workshop, Q&A session, decision support. Optionally: continuing with pilot delivery.
- executive workshop
- decision support material
- go/no-go criteria
Comparison · 5 team types
AP4 Digital vs big AI consultancies, AI-only boutiques, and freelancers — which one to choose, and when?
Enterprise AI adoption has 4 typical vendor types: (1) big consultancies (Accenture, Deloitte, Capgemini, KPMG), (2) AI-only boutiques, (3) in-house attempts, (4) freelance consultants. Each has its place, but they fit different profiles. The table below helps you pick the right type for your task.
| Criteria | AP4 Digital 10+ years of engineering + AI-native | Big consultancy Accenture / Deloitte / KPMG | "AI-only" boutique prompts + LLM only | In-house attempt own team learning | Freelancer 1-person risk |
|---|---|---|---|---|---|
| Software engineering experience | 10+ years, 100+ projects | weak / via outsourcing | minimal, few live projects | varies, team-dependent | varies, 1 person |
| Concrete working system (not a deck) | yes — production-grade code | no — mostly strategy documents | prototype / demo only | built slowly, over months | yes, but 1-person risk |
| Private RAG / on-prem deploy | yes — the default setup | optional, expensive | rare, mostly on SaaS LLMs | new skill, long learning curve | varies by individual |
| Business ROI measurement (KPI framework) | yes — on every engagement | yes, at a high price | no — focus is on the tech | rarely — projects slip | hit-or-miss, up for negotiation |
| EU AI Act compliance awareness | yes — in every audit | yes — in-house legal department | surface-level — focused on prompts | new topic — just starting to learn | varies, no guarantee |
| Fixed price / fixed deadline | yes — written SLA | no — time & material | varies, often slips | no — internal priorities decide | varies, conflict-prone |
| Lead time (to audit) | 2–4 weeks | 8–16 weeks (RFP, legal, SOW) | 4–6 weeks | internal priorities decide — unpredictable | 2–4 weeks |
| Price range (audit) | €3.8–10k | 40–120 k € | 10–25 k € | hidden — opportunity cost | 4–8 k € |
| Bus factor (1-person risk) | team — multiple developers, documented | team — large organization | small team — 3–8 people | internal team — your own risk | 1 person — critical dependency |
Case study · private RAG
At a B2B SaaS company: 32 → 7 hours/week — support search time.
A private RAG built in 6 weeks, giving permission-filtered answers across 12,000 internal documents (policies, tickets, product knowledge). The data never leaves the client’s tenant.
143days
New product onboarding lead time
078%
Internal documentation coverage
327h/week
Support team search time
This project was led by
Péter — RAG architect, AP4 Digital
Have a similar project? Ask him directly.
−70% response time · 2× ticket close rate
"The AI customer service assistant cut average response time by 70% in 3 months — and our junior team now closes 2x as many tickets."
Team · senior · Budapest
A senior team that builds — not just presents.
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 48 hours. Concrete, with numbers — no generalities."
— CTO · B2B SaaS, ~80 people
10+
years of software engineering
100+
delivered projects
13+
industries
4.9★
Clutch · 24 reviews
References · 30 projects
Our featured references.
15 featured projects — the full 30-project portfolio is on the References page.
Free download · PDF · 12 pages
AI opportunity map — 7 use cases worth starting with today
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.
30 minutes · free · under NDA if needed
Book 30 minutes. A 1-page proposal 48 hours later.
Who you’ll talk to
Sándor — founder, AP4 Digital
10+ years of software development · personally leads the discovery call
A structured assessment of your current AI situation, your business goals, and which of our services fits (or who else would be the right partner).
- NDA immediately, if the topic is sensitive
- A 1-page written proposal within 48 hours
- A detailed proposal within a week
- A second opinion if another team would be better
Frequently asked questions