THE TETRIFOX APPROACH
AI is an engineering problem, not a strategy problem.
The technology is mature enough. What's missing is disciplined delivery.
Companies don't fail at AI because the models aren't good enough — they fail because they skip the business case, underestimate the integration work, or never plan for production. Tetrifox applies the same business-first, embedded delivery model to AI that we use for all product engineering.
Business case first
Every engagement starts with one question: what business outcome does this AI application need to deliver? If we can't define the value, we don't build.
Disciplined delivery
Predictable timelines measured in weeks. A Mino embeds in your organization and delivers against milestones, not against a vague AI roadmap.
Measurable outcomes
Usage, cost per run, accuracy, latency — every application ships with monitoring built in. You know exactly what you're paying and what you're getting.
THE DELIVERY FRAMEWORK
From idea to production in six phases
Technical review
We assess your current state — data landscape, existing systems, team readiness, and infrastructure. Same methodology as our Technical Reviews, focused on AI readiness.
1 week
Business case and solution blueprint
Define the business outcome, map the solution architecture, identify the right AI approach, and lock down cost expectations. No prototype until the case is clear.
1–3 weeks
App inventory and sizing
Break the solution into discrete AI applications. Each is sized as small, medium, or large based on the number of data points, the systems it connects to, and complexity.
Included in Phase 2
Delivery to UAT
A Mino builds and integrates the application end-to-end. Development, testing against the business case — not just technical specs — and iteration based on user feedback.
1–9 weeks
Production rollout and hypercare
Go-live with monitoring, alerting, and a dedicated support window. We don't hand over and disappear — the team stays until production is stable.
1 week
Iterate on production data
Real usage reveals what the business case couldn't predict. We refine prompts, adjust thresholds, optimize cost, and extend functionality based on actual production data.
Ongoing
APPLICATION SIZING
Predictable scope, predictable timelines
Every AI application is sized before development begins. The sizing determines timeline, team composition, and cost — no surprises.
A data point is one piece of information the application reads or fills in, such as an invoice number, a date or an amount.
Small
1–2 weeks- Data points
- Up to 10 data points
- Connected systems
- 1 connected system
Single-purpose applications. Document classification, content generation from a template, structured data extraction from one source.
Medium
3–5 weeks- Data points
- 10–25 data points
- Connected systems
- 2–3 connected systems
Multi-step workflows. Cross-referencing data from multiple sources, generating reports with business logic, automated review processes.
Large
6–9 weeks- Data points
- 25+ data points
- Connected systems
- 4+ connected systems
Complex orchestration. Multi-agent systems, deep integration across your stack, applications that combine multiple AI capabilities into one workflow.
These timelines are indicative, based on our experience across similar projects. Actual scope and duration are defined together during the blueprint phase.
BUILT-IN MONITORING
Every application ships with a cost dashboard
Most teams build AI and hope for the best. We ship with observability from day one — because we've seen what happens when you don't.
Usage tracking
Who's using it, how often, and for what. Adoption data that tells you whether the application is actually solving the problem.
AI cost per run
Exact cost visibility per inference call. No surprises on the monthly bill — you know the unit economics before you scale.
Latency monitoring
Response time tracking across every component. When a model provider slows down, you see it before your users feel it.
Error rates and alerts
Automated alerting on failure spikes, quality degradation, and anomalies. Issues surface in minutes, not after a stakeholder complaint.
WHY TETRIFOX
Built for this, not bolted on
Senior engineers, faster decisions
Model selection, architecture trade-offs, build-vs-buy decisions — our senior team navigates these in days, not weeks. No learning on your dime.
We practice what we ship
Our own development process is AI-aided. We use the tools we recommend — so we know what works in practice, not just in demos.
Testing against the business case
A dedicated tester validates that the application is useful, not just functional. For AI, 'it works' and 'it solves the problem' are very different things.
EU data residency
All processing within Europe. For regulated industries and data-sensitive companies, this isn't a feature — it's a requirement. We lead with it.
FREQUENTLY ASKED QUESTIONS
What companies ask before they start
Talk straight to a founder.
30 minutes. No pitch, no pressure. Just space to explore what you’re building and where you want to go.
Wouter van Nierop
Founder & CEO at Tetrifox