01
Unclear Business Case for AI
Without a quantified investment case, the build-versus-buy question cannot be answered honestly — and funding decisions drift on enthusiasm instead of evidence.
Executive AI Advisory
AI Make-or-Buy Decision Support
Choose the right delivery model before committing budget, teams, and strategic control. We evaluate internal development, external solutions, and hybrid approaches against your technical, commercial, operational, and strategic requirements.
Decision
Build · Buy · Hybrid
Assessment
12 dimensions
Engagement
Four phases
Cost Horizon
Five-year TCO
The Decision
One of the most consequential technology decisions a leadership team makes.
Whether to build an AI capability in-house or buy it from the market commits budget, talent, and time for years — and it usually has to be made while vendors, analysts, and internal advocates all pull in different directions.
02
Scalability & Production Readiness
Many AI tools impress in a proof of concept and fail in real deployment — undone by data quality, infrastructure, latency, or performance at scale.
03
Vendor Claims vs. Reality
Providers overpromise and underdeliver. Separating genuine capability from marketing takes technical depth most buyers do not have in the room.
04
Security & Compliance Risks
External providers must meet your obligations — GDPR, the EU AI Act, industry security standards, and, increasingly, European sovereignty requirements.
05
Lack of Internal AI Expertise
Evaluating vendors and overseeing internal development both demand engineering judgment. When it is missing, the decision defaults to the best salesperson.
Questions Answered
The questions this engagement answers.
Before committing to a delivery model, these are the questions a leadership team must be able to answer with evidence.
01
Is the capability strategically differentiating?
02
Does a credible market solution already exist?
03
How much internal ownership is required?
04
Who should retain the data, models, and intellectual property?
05
Can the solution be integrated into existing systems?
06
What are the realistic internal development costs?
07
What are the long-term vendor and licensing risks?
08
What happens when the system must scale or change?
09
What must run on-device, on-premise, or in the cloud?
10
Which capabilities must remain inside the company?
The Outcome
Ten questions. One defensible delivery-model decision.
What We Do
A requirements-based assessment of build, buy, and hybrid.
We assess each option against your operational, technical, and commercial requirements. Internal development, external solutions, and combined approaches are evaluated on the same twelve dimensions — from strategic relevance and functional fit to total cost of ownership, data and IP control, and long-term flexibility.
The result is not a preference or a vendor pitch: it is a delivery-model recommendation your board can interrogate, with the evidence and trade-offs attached.
Assessment Dimensions
Strategic relevance
Functional fit
Technical feasibility
Internal capabilities
Development effort
Integration effort
Total cost of ownership
Data and IP control
Security and sovereignty
Vendor dependency
Time to operation
Long-term flexibility
How the Engagement Works
From contested question to defensible decision
A structured process in four phases, each closing with a concrete output.
1
Requirements and Context
We start on your side of the table:
Understand your business
Your objectives, constraints, and the strategic direction the decision must serve.
Clarify your requirements
Precise operational, technical, and commercial requirements, and what a successful outcome must deliver.
Assess your in-house capabilities
An honest evaluation of your team, infrastructure, and readiness to build and operate AI.
2
Market and Option Scan
We systematically evaluate the available paths:
AI vendors and products
Researching, shortlisting, and screening solution providers on technical capability, integration effort, track record, and strategic fit — with claims tested, not taken on trust.
Development partners
Assessing specialized partners who could build with you or for you, held to the same standard.
3
Requirements-Based Assessment
We assess each option against your operational, technical, and commercial requirements:
Compare options
Feasibility and total cost of internal development versus external solutions, compared honestly and side by side.
Validate technical aspects
Technical depth, scalability, security, and integration verified hands-on, so the decision survives contact with production.
4
Recommendation and Decision Support
We carry the decision to the boardroom:
Define ROI expectations
A quantified investment case covering immediate value and long-term strategic implications.
Select the delivery model
A clear recommendation — build in-house, buy from the market, or deliberately combine both — with the rationale and safeguards to defend it.
1
Requirements and Context
We start on your side of the table:
Understand your business
Your objectives, constraints, and the strategic direction the decision must serve.
Clarify your requirements
Precise operational, technical, and commercial requirements, and what a successful outcome must deliver.
Assess your in-house capabilities
An honest evaluation of your team, infrastructure, and readiness to build and operate AI.
2
Market and Option Scan
We systematically evaluate the available paths:
AI vendors and products
Researching, shortlisting, and screening solution providers on technical capability, integration effort, track record, and strategic fit — with claims tested, not taken on trust.
Development partners
Assessing specialized partners who could build with you or for you, held to the same standard.
3
Requirements-Based Assessment
We assess each option against your operational, technical, and commercial requirements:
Compare options
Feasibility and total cost of internal development versus external solutions, compared honestly and side by side.
Validate technical aspects
Technical depth, scalability, security, and integration verified hands-on, so the decision survives contact with production.
4
Recommendation and Decision Support
We carry the decision to the boardroom:
Define ROI expectations
A quantified investment case covering immediate value and long-term strategic implications.
Select the delivery model
A clear recommendation — build in-house, buy from the market, or deliberately combine both — with the rationale and safeguards to defend it.
Main Process
Parallel Activities
Process Flow
Deliverables
Concrete outputs, not guidance.
01
Build-Buy-Partner Decision Matrix
Every option scored against your requirements, side by side — with the trade-offs made explicit.
02
Internal Capability Assessment
An honest evaluation of your team, infrastructure, and readiness to build and operate AI.
03
Market & Solution Scan
The credible vendors, products, and delivery partners for your use case — screened, not listed.
04
Five-Year TCO Scenarios
Total cost of ownership for build, buy, and hybrid paths, modeled over five years on comparable assumptions.
05
Risk & Dependency Analysis
Vendor, licensing, technology, and lock-in risks for each path, with mitigations.
06
Recommended Delivery Model
A clear recommendation — build, buy, or a deliberate combination — with the rationale to defend it.
07
Architecture & Contractual Safeguards
The required architecture boundaries and contract terms that protect data, IP, and exit options.
08
Management Decision Presentation
The decision prepared for the boardroom: evidence, options, recommendation, and next steps.
Applied AI Since 2016
Judgment earned in production, not in slide decks
We have watched technologies, vendors, and architectures rise and fall for a decade. We have negotiated partnerships, run evaluations, and put AI systems into production ourselves. That experience is what lets us tell you — quickly and honestly — what will work in your environment and what will not.
AI Deployments – On-Premise & Cloud
We have designed and operated AI systems across the full deployment spectrum — secure on-premise infrastructure, cloud-native platforms, and hybrid and edge environments where latency, control, and sovereignty constraints decide the architecture. We guide you to the setup your requirements actually demand.
Pioneers Since the Early CNN Era
Our deep learning work began alongside breakthrough moments like AlexNet and Inception v2, and we have applied convolutional neural networks in production since 2016 — before most companies had started experimenting. That long baseline is how we distinguish durable capability from passing hype.
Leading the Charge in Generative AI
We build and test at the edge of generative AI ourselves, so our advice rests on first-hand evidence rather than vendor briefings. When we assess a GenAI claim, we have usually already tried the underlying approach — and we tell you plainly what it can and cannot do.
AI Deployments – On-Premise & Cloud
We have designed and operated AI systems across the full deployment spectrum — secure on-premise infrastructure, cloud-native platforms, and hybrid and edge environments where latency, control, and sovereignty constraints decide the architecture. We guide you to the setup your requirements actually demand.
AI Deployments – On-Premise & Cloud
Pioneers Since the Early CNN Era
Leading the Charge in Generative AI
The most expensive AI decision is the one made on vendor slides. Requirements-based due diligence costs a fraction of a failed implementation.
FAQ
Purchasing questions we hear on this service.
When in the process should we involve you?
Before budget, teams, or contracts are committed. The engagement is designed for the window in which build, buy, and hybrid are still genuinely open options — that is when a requirements-based assessment changes the outcome rather than justifying one.
Does the answer have to be a clear build or a clear buy?
No. Many strong outcomes are deliberate combinations — for example, buying a platform while keeping differentiating models, data, and IP in-house. The decision matrix defines build-buy-partner boundaries at the capability level, and the recommended delivery model includes the architecture and contractual safeguards that make the combination workable.
How do you compare internal development costs with vendor pricing fairly?
On the same assumptions and the same horizon. We model five-year total cost of ownership for every path — including internal staffing, infrastructure, integration, licensing, maintenance, and exit costs — so a subscription price is never compared against an incomplete internal estimate.
We already have vendor proposals on the table. Is it too late?
No. We assess the proposals against your operational, technical, and commercial requirements, test the claims behind them, and compare them against the internal-development baseline. If deeper provider validation is needed, the engagement connects directly to our AI Provider Evaluation & Technical Due Diligence service.
Related Services
Complementary services to enhance your AI journey.
Evaluation
AI Provider Evaluation & Technical Due Diligence
Determine whether a provider, product, or technical solution can deliver under real operating conditions.
Architecture
AI Product & Systems Architecture
Translate strategic intent into clear product requirements, system boundaries, and technical decisions.
Strategy
AI Strategy Consulting
Set the AI direction, priorities, and investment logic your delivery-model decisions must serve.
Review Your Make-or-Buy Decision
Bring us the decision before you commit budget, teams, and strategic control.
We will assess every option against your requirements and give you a recommendation we are willing to defend in your boardroom.
Review Your Make-or-Buy Decision nAIxt Technologies GmbH
Am Forst 2
82166 Gräfelfing, Germany
+49 89 54196515
info@naixt-technologies.de