AI-first engineering

CTO’s AI Evaluation Framework.

Book an Intro Call Common Questions

4 pillars

Pillars in the CTO AI evaluation framework

12 MVPs

Shipped in a single 6-hour hackathon

6 hours

Hackathon MVP delivery window

AI adoption is exploding. Gartner estimates enterprise AI adoption has grown by 270% in the past four years. Yet, more than 80% of AI projects never reach ROI.

Executive Summary

Summary

The biggest reason? Lack of evaluation frameworks.

This paper introduces a 4-pillar evaluation framework designed for CTOs and engineering leaders. It is not theory, it’s backed by real-world validation, including our own hackathon where 12 MVPs were shipped in just 6 hours.

Insights

Hackathon Case Insight: 12 MVPs in 6 Hours.

When we ran our Hackathon, the challenge was clear:

12 MVPs in 6 Hours

Were outputs consistent across edge cases?

Accuracy tests

Did apps hold under concurrent users?

Latency sanity checks

Were prompts optimized to avoid runaway tokens?

Cost reviews

Could a non-technical user test it without guidance?

Usability validation

Approach: Instead of “build first, evaluate later,” every sprint included mini E-vals

“build first, evaluate later,”

Lesson: Evaluation wasn’t a slowdown. It was the enabler of speed. By catching issues in real time, teams avoided rework and shipped faster.

Outcome

12 MVPs delivered demo-ready.

No critical failures during live demo.

Avg. iteration cycle <30 mins.

Clear visibility into which MVPs were “investable” vs. experimental.

The status quo

The Problem with AI Today.

67% of CIOs admit their AI pilots stall before scaling (PwC).

Projects focus on “what’s possible” instead of “what’s sustainable

Hype-Driven Development

Forrester estimates rework consumes 30-50% of AI budgets when evaluation is skipped.

Missed hallucinations or untested latency issues require full rebuilds.

Expensive Rework

IDC reports 20-40% of AI infra spend is avoidable with better cost modeling.

Token bloat, inefficient prompting, and under-optimized infra inflate bills.

Runaway Costs

One bad AI failure (e.g., inaccurate recommendations, compliance miss) can undermine trust across the org.

Credibility Risk

AI Today

Overview

The Four Pillars of AI Evaluation.

Evaluate hallucination rate, bias, and consistency

Test against ground-truth datasets, not just demo examples.

Example metrics: BLEU, ROUGE, accuracy on edge-case datasets.

Reliability & Accuracy

Measure real-world usage under concurrent load.

Benchmark average vs. p95 latency.

Stress test for input variety and concurrent requests.

Performance & Latency

Token usage audits (average tokens per request, cost per 1k users).

Infra scaling scenarios → what does it cost at 10x load?

Identify unnecessary calls or wasted retries.

Cost Efficiency

Tie outputs to measurable KPIs (conversion, retention, time saved).

Define “success thresholds” upfront: what business metric must move?

Avoid “demo success” that doesn’t translate to business ROI.

Business Alignment

AI Evaluation

Insights

Data-Backed Insights.

60% of AI projects stall at PoC due to lack of evaluation.

Gartner

Companies with AI evaluation frameworks see 3x faster scaling.

MIT Sloan

Firms with structured evaluation reduce AI costs by ~30% annually.

McKinsey 2024

12 MVPs in 6 hrs, 0 critical failures, because evaluation was embedded from day zero.

Logiciel Hackathon

Resources

Insights for tech leaders.

Whitepaper · PDF

AI Test Generation Evaluation Kit

Whitepaper · PDF

AI - Powered Product Development Playbook

Whitepaper · PDF

Your Highest-Intent Buyers Are Sitting in Your CRM Four Times

Let's build

Evaluate. Adapt. Accelerate. Your Path to AI-First Engineering Starts Here.

Talk through your roadmap with our engineering leads - implementation, governance, and security handled as one connected responsibility.

CTO’s AI Evaluation Framework.

See Logiciel in action.

Hackathon Case Insight: 12 MVPs in 6 Hours.

The Problem with AI Today.

The Four Pillars of AI Evaluation.

Data-Backed Insights.

Insights for tech leaders.

AI Test Generation Evaluation Kit

AI - Powered Product Development Playbook

Your Highest-Intent Buyers Are Sitting in Your CRM Four Times

Evaluate. Adapt. Accelerate. Your Path to AI-First Engineering Starts Here.