BUILD · Delivery Challenge

Engineering Velocity

The gap between your team's effort and your delivery output is not a motivation problem - it is a process and tooling problem.

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The Problem

What this looks like

•Your engineers are not slow. They are working in a system that makes them slow. Every change requires navigating ten years of accumulated decisions that nobody fully understands any more. Test coverage is low enough that people are afraid to refactor. Deployment is a manual process with a checklist that somebody might forget to run. The backlog grows faster than it shrinks.

•You have tried adding people. It helped briefly, and then the same patterns returned. More engineers in a slow process produce more work in progress - not more shipped software.

•The teams delivering fast in 2026 have not grown faster than you. They have changed how they work: AI-assisted development, automated testing, short feedback loops from commit to production. The gap between those teams and yours widens every quarter. Not because they are smarter - because they restructured before you did.

What it costs you

•"We will fix the process after this release" is the sentence that has been said at every sprint planning for three years.

•The people best placed to fix the process are the ones most buried in the current one.

•SDLC change is not a single tool purchase - no one vendor solves it.

•Leadership sees the slowness as a resourcing problem, so the answer is always headcount. The real constraint is structural.

The real risk

The delivery gap compounds each quarter unless process and tooling are restructured.

Our Approach

Our approach is sequenced by pillar:

THINK

We start with a rapid assessment of where your delivery is actually losing speed. Not assumptions - data from your repository, your deployment logs, your team. We produce a clear diagnosis: where the friction is, what is causing it, and what to change first. This assessment takes one to two weeks and changes what you invest in next.

BUILD

We embed AI-native development practices in your team. Not a training course - working alongside your engineers on your actual codebase. The foundations engagement covers the core change: AI-assisted development, automated testing, CI/CD hardening. The full transformation engagement extends this across the organisation, standardising practices and making them self-sustaining.

TEACH

After the structural work is done, your engineers own the new way of working independently. The AI Engineering Programme trains them in AI-native development: using LLMs effectively in their workflow, building and evaluating agents, maintaining AI-assisted codebases. When we leave, the velocity stays.

Expected Outcomes

Rapid assessment output, 1-2 weeks

Primary delivery bottlenecks identified with data

Engagement deliverable - Fognini Tech

4-8 weeks from engineering foundations start

Engineering team using AI-assisted development consistently

Engagement deliverable - Fognini Tech

Benchmark

Software developers using GitHub Copilot completed tasks 56% faster than those not using it

Cihon et al., Cornell University arXiv:2302.06590, Feb 2023 →

Benchmark

AI high performers redesign workflows rather than adding tools to existing ones - 55% vs 20% of peers

McKinsey State of AI 2025, Nov 2025 →

Your path forward

Start with one concrete service that matches your situation, then review how we sequence the rest.

Start here

Rapid SDLC/Delivery Assessment

Find exactly where your engineering is losing speed - and get a clear, prioritised plan to fix it.

From CHF 4’000 (Basic)

Audience: Engineering leads and CTOs who suspect delivery bottlenecks but want an independent view before committing to a solution.

Outcome: A focused assessment of how your engineering team currently delivers software. Reviews your SDLC, tooling, team practices, and delivery metrics to identify the highest-impact bottlenecks. Suited to engineering teams that know something is slowing them down but have not independently diagnosed the cause.

Delivers

SDLC assessment report
Delivery maturity scorecard
Prioritised improvement recommendations

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Core

AI-Native Engineering - Foundations

Your small engineering team using AI tools systematically - not ad hoc. Configured, trained, and able to maintain the setup independently when we leave.

From CHF 6’000 setup

3-5 days

Audience: Small product teams and early-stage scale-ups with 2-5 engineers on a single codebase who want to adopt AI tooling without disrupting delivery.

Outcome: Assesses your current tooling and workflow, configures a custom AI development environment for your stack, and trains your engineers to use it. Suited to teams of 2-5 with one active codebase who write good code but are not yet using AI tooling systematically. You leave the engagement with working tools, documented processes, and a team that can maintain and extend the setup independently.

Delivers

Current-state assessment report
AI toolchain configuration
Custom agent setup
Prompt templates and workflow documentation
Engineer training sessions

Scale

AI-Native Engineering - Full Transformation

AI-native practices adopted across your growing team through real delivery - not just workshops. More output, same headcount, without stopping product work.

From CHF 10’000 setup

5-10 days

Audience: Growing scale-ups with 5-15 engineers across multiple workstreams who have delivery velocity problems and want to adopt AI-native practices without stopping product work.

Outcome: Everything in the Foundations tier, plus expanded agent coverage for testing, documentation, and code review, with structured training across a larger team. Suited to teams of 5-15 with multiple workstreams who have delivery velocity problems. The engagement runs long enough for your team to adopt the new approach through real delivery - adoption happens on the job, not in a training room.

Delivers

Current-state assessment report
AI toolchain configuration
Custom agent setup (code generation, testing, documentation, review, CI/CD)
Prompt templates and workflow documentation
Engineer training programme

Sustain

AI Engineering Programme

An engineering team that can design, build, and operate AI-native systems end to end - not just use AI tools as a shortcut.

From CHF 5’500 fixed

3 days

Audience: Software engineers, architects, and engineering leads who will design, build, or maintain AI-native systems.

Outcome: Builds deep technical AI capability for engineers and architects. Covers how LLMs work, prompt engineering at production level, retrieval-augmented generation, agent architectures, and the operational requirements of running AI systems in production. Participants leave able to make architectural decisions about AI systems, not just use AI coding assistants. Requires AI Foundations as a prerequisite.

Delivers

LLM and prompt engineering reference guide
RAG architecture patterns
Agent design framework
Production AI deployment checklist

See pricing for this challenge

Prices are indicative. We scope every engagement before quoting.

The gap between your team's effort and your delivery output is not a motivation problem - it is a process and tooling problem.

The teams delivering fast in 2026 have changed how they work: AI-assisted development, automated testing, short feedback loops from commit to production.

Book a Free 30-Min Discovery Call Take the AI Readiness Self-Check

Engineering Velocity

The Problem

What this looks like

What it costs you

The real risk

Our Approach

THINK

BUILD

TEACH

Expected Outcomes

Your path forward

Rapid SDLC/Delivery Assessment

AI-Native Engineering - Foundations

AI-Native Engineering - Full Transformation

AI Engineering Programme

The gap between your team's effort and your delivery output is not a motivation problem - it is a process and tooling problem.

Frequently Asked Questions

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