You share the context
Use case, current state, constraints, team, data, and what would make the project valuable.
Your external team for AI engineering
We help SaaS teams and software consultancies define, validate, and ship reliable, measurable, and profitable AI projects.
- Clear viability
- Reliability checks
- Real integration
- Cost control
We reply in less than 48h
TenBeltz
We work with two kinds of software teams
SaaS companies
Teams that want to introduce AI or fix a feature that already reached production with poor reliability, high cost, or weak operating criteria.
- • New AI roadmap
- • Broken AI feature
- • Need for reliability checks
Software consultancies
Teams that need sharper technical criteria to scope, sell, and deliver AI projects for their own clients without improvising architecture or requirements.
- • Pre-sales support
- • Project planning
- • Delivery foundation
Problem
Most AI projects fail before production or when they finally get there
The issue is rarely adding a model. The issue is knowing what to build, what data is needed, and how to operate it without hurting product, margin, or trust.
01
No clear viability criteria for the use case
02
Built without enough data or evaluation coverage
03
Costs explode when traffic grows
04
Reliability breaks under real usage
05
No monitoring, safeguards, or failure signals
06
Projects sold without asking clients for what is needed
Value
We step in where engineering judgment is actually needed
TenBeltz is not an AI agency and not a generic consultancy. We define the case, structure the system, and help the team carry it into production with explicit delivery criteria.
We do not sell demos
We do not sell hours without context
We design AI systems that are:
01 Decidable 02 Measurable 03 Observable 04 Maintainable
Ways of working
Four ways to work
Each mode fits a different project stage. This is the short version; the services page has the full detail.
Entry
AI Gap Analysis
Diagnosis to decide whether it is worth building and leave with a clear roadmap to deliver it.
- • Technical clarity
- • Project roadmap
Foundation
AI Project Foundations
Technical foundation for consultancies to scope, sell, and start AI projects with better criteria.
- • Reusable criteria
- • Pre-sales support
MVP
Agent MVP
First working version of an agent or AI system, with a clear way to validate quality.
- • Functional MVP
- • Validation criteria
Delivery
Production Delivery
Full implementation through production, integrated with your product and ready to operate.
- • Product integration
- • Operable delivery
Next step
What happens after you write
The first conversation is not a sales call disguised as diagnosis. We use it to understand whether there is a real technical fit.
We assess fit
If TenBeltz is not the right partner, we say it clearly before proposing anything.
We recommend a path
Gap Analysis, Foundations, Agent MVP, Production Delivery, or no project for now.
You decide with clarity
You get a concrete next step instead of a vague proposal or a list of hours.
Deliverables
What clients actually walk away with
08 / artifacts
01 · Core
Clear viability decision
A go / no-go answer grounded in engineering, not intuition.
02
Recommended architecture
A system shape aligned with constraints, reliability, and margin.
03
Technology choices
Practical choices for providers, frameworks, and orchestration.
04
Data and dataset requirements
What is missing, what quality is needed, and how to structure it.
05
Reliability test set
A practical set of examples and criteria to measure whether the AI works well.
06
Observability plan
Signals, metrics, and alerts to monitor the system in production.
07
Risk map and safeguards
Failure modes and operating limits identified upfront.
08 · Output
Technical roadmap
Sequenced priorities so delivery stays clear and implementation does not drift.
Process
From fuzzy idea to operable system
We keep the process simple: understand the context, design the system, validate with real criteria, and either ship it or support the internal team.
01
Context
We understand business constraints, product reality, and the specific use case.
step
02
System design
We define architecture, data needs, reliability checks, safeguards, and delivery path.
step
03
Validation
We ground decisions in measurable criteria instead of intuition or demo theater.
step
04
Production or handover
We either take it through delivery or hand over a clear path for your internal team to finish with confidence.
step
Fit
Who this is for
Good fit
-
You are a SaaS or consultancy with real software delivery behind the AI ambition.
-
You need technical judgment, not a flashy prototype.
-
You care about reliability, observability, integration, and cost.
-
You want a technical team that can challenge scope before implementation starts.
Not a fit
-
You want a cheap prototype as fast as possible.
-
You want to add AI without a clear use case or owner.
-
You want to start building without defining success criteria first.
If the project matters, it is worth defining it properly from the start
Share the context and we will tell you whether there is a strong fit and which way of working makes sense.
Tell us your project
Share the context and we will assess whether there is a strong fit for TenBeltz.
