Engineering-Led, Not Tool-Led
We don't sell a tool subscription. We redesign your engineering practices, workflows, and culture around AI — tools are chosen to serve the architecture, not the other way around.
AI-Native Engineering Transformation
Your engineers should be directing AI — not writing boilerplate.
NCompas redesigns how your engineering teams build software — AI-augmented development, autonomous coding agents, intelligent CI/CD, and AI-native testing that compound velocity without multiplying headcount.
55%faster task completion for developers using AI coding assistants — the largest single productivity leap in software engineering history (GitHub, 2024)
87%of developers report that AI tools improve code quality when integrated into the review and test cycle — not just generation (Stack Overflow Dev Survey)
3×more frequent deployments at organisations that have fully integrated AI into their SDLC, not just bolted a copilot onto the existing workflow
$1.5Tin developer productivity unlocked globally by 2030 through AI-native engineering practices — the software industry's biggest structural shift (McKinsey, 2024)
Why NCompas
Engineering transformation that actually ships.
Most AI engineering programmes stall at pilot. We design for full adoption — with velocity metrics that prove ROI before the engagement ends.
Codebase-Aware AI Setup
GitHub Copilot out of the box doesn't know your codebase. We configure fine-tuned context, internal pattern libraries, and company-specific rules so AI suggestions are relevant and safe from day one.
Security-First Approach
Every AI-generated line of code passes through automated security review before it merges. We build the guardrails before we accelerate the output — not after the first vulnerability makes headlines.
Measurable Velocity from Week One
We baseline your current engineering throughput on day one and track velocity metrics weekly. You see exactly how much faster your team is shipping — not a consultant's estimate, production data.
Developer Adoption as a KPI
AI tools that developers abandon after two weeks have zero ROI. We design adoption programs, measure daily active use, run developer retrospectives, and iterate until the tools are embedded in the daily habit.
Enterprise-Grade Governance
IP protection, code licence auditing, AI output review gates, and data residency compliance. We design the policy and governance framework alongside the tooling — so legal, security, and IP teams don't block the programme.
Six transformation pillars
This isn't "add Copilot." It's how your entire engineering org works differently.
GitHub Copilot is where most organisations start — and stop. AI-native engineering transformation reaches every layer of the SDLC, from requirements to production monitoring.
Agentic Coding & Autonomous Workflows
Beyond autocomplete — autonomous AI agents that write entire modules, generate test suites, refactor legacy code at scale, migrate frameworks, and respond to production incidents with code fixes. We design the agentic engineering workflows that let your developers supervise AI, not write every line.
Autonomous feature scaffoldingAI-driven legacy migrationTest suite generation agentsIncident response code patchesAutomated dependency upgrades
Routine coding tasks completed autonomously — developers focus on architecture and product decisions
AI-Augmented Developer Experience
GitHub Copilot is the beginning, not the destination. We design an AI-augmented DX stack — context-aware code completion, inline documentation, intelligent refactoring, and AI-generated pull request summaries — tuned to your codebase, your patterns, and your team's velocity targets.
GitHub Copilot Enterprise tuningCodebase-aware context injectionAI PR summarisationInline documentation generationAI-assisted refactoring workflows
Average 40–55% reduction in time-to-complete per coding task
AI-Native SDLC Transformation
Redesigning your software development lifecycle around AI — from AI-assisted requirements refinement and sprint planning to automated code review, intelligent defect prediction, and AI-generated release notes. Every stage of the SDLC gets smarter.
AI requirements analysisAutomated code review botsDefect prediction modelsAI sprint velocity forecastingAutomated release documentation
Teams ship 2–3× more features per sprint without adding headcount
Agentic Coding & Autonomous Workflows
Beyond autocomplete — autonomous AI agents that write entire modules, generate test suites, refactor legacy code at scale, migrate frameworks, and respond to production incidents with code fixes. We design the agentic engineering workflows that let your developers supervise AI, not write every line.
Autonomous feature scaffoldingAI-driven legacy migrationTest suite generation agentsIncident response code patchesAutomated dependency upgrades
Routine coding tasks completed autonomously — developers focus on architecture and product decisions
AI-Powered Testing & Quality
AI that generates test cases from requirements, identifies coverage gaps, predicts flaky tests, and writes regression suites faster than your QA team can review them. Test coverage becomes a function of AI output, not engineering bandwidth.
AI test case generationCoverage gap detectionFlaky test predictionVisual regression AIMutation testing automation
Test coverage improvements of 35–50% without increasing QA headcount
Platform Engineering & AI-Augmented CI/CD
Internal developer platforms powered by AI — self-service scaffolding, intelligent pipeline optimisation, predictive build failure detection, and AI-driven infrastructure provisioning. Your CI/CD pipeline gets smarter with every build.
Intelligent pipeline optimisationPredictive build failure alertsAI-assisted IaC generationSelf-service dev portalsAI-driven deployment risk scoring
CI/CD cycle times reduced by 40% with intelligent pipeline optimisation
AI Security & Code Governance
AI-generated code needs AI-native security review. We integrate automated vulnerability scanning, AI-driven SAST/DAST, licence compliance checking, and prompt injection audits into your CI/CD — so speed doesn't come at the cost of safety.
AI-assisted SAST integrationPrompt injection auditingLicence compliance automationAI-generated secrets detectionDependency risk scoring
Security defect detection rates 60% higher vs. manual review alone
Platforms & tools we deploy
AI CodingGitHub Copilot Enterprise
AI IDECursor / Windsurf
AI CodingClaude Code
AI EngineAzure OpenAI
CI/CDGitHub Actions
DevOpsAzure DevOps
Code QualitySonarQube + AI
SecuritySnyk
Agent FrameworkLangGraph / AutoGen
IaCTerraform + AI
Dev PortalBackstage
ObservabilityDatadog / Dynatrace
How we work
First velocity improvement in two weeks — not six months.
Six steps that deliver a measured velocity improvement before asking for commitment to full-scale transformation — because proof beats proposal every time.
01
Engineering Baseline
Measure current velocity: PR cycle time, deployment frequency, DORA metrics, test coverage, and time-on-boilerplate. Every recommendation is anchored to real numbers, not estimates.
02
Opportunity Mapping
Map the AI opportunity across your SDLC — where automation delivers highest ROI, where agentic coding is viable, where governance needs to be built before speed is unlocked.
03
Toolchain Design
Select, configure, and integrate the AI toolchain that fits your stack, your security posture, and your team structure. Codebase context injection, internal pattern libraries, review gates.
04
Pilot Team Sprint
Run a 2-week AI-native sprint with a representative engineering team. Measure velocity delta, gather adoption feedback, identify friction. Proves ROI before full rollout.
05
Full Engineering Rollout
Scaled deployment across all engineering teams with onboarding sessions, pair-programming with AI, governance training, and adoption tracking by team and individual.
06
Continuous Optimisation
Weekly velocity tracking, AI adoption rates, defect density trends, and deployment frequency. Monthly engineering retrospectives. Quarterly AI toolchain upgrades as the landscape evolves.
Results by engineering team
Every team is shipping faster — with the same headcount.
Four engineering team types, four AI-native transformations. The velocity numbers are from production sprints, not proofs of concept.
The Challenge
Senior developers spending 40% of time on boilerplate, documentation, and code review. Junior developers blocked waiting for review cycles. Feature velocity falling behind product roadmap.
What We Built
AI copilot tuned to codebase context, automated PR review with AI suggestions, junior developer pairing with AI as first-line reviewer, AI-generated documentation on merge. Senior developers redirected to architecture and complex problem-solving.
55%reduction in time-to-complete per feature
3 days → 4 hrsPR review cycle time
90%documentation coverage (was 20%)
Senior developers spending 40% of time on boilerplate, documentation, and code review. Junior developers blocked waiting for review cycles. Feature velocity falling behind product roadmap.
55%reduction in time-to-complete per feature
3 days → 4 hrsPR review cycle time
90%documentation coverage (was 20%)
4-week wait for new service scaffolding. CI/CD pipelines taking 35+ minutes. Infrastructure-as-code written manually from scratch for every environment.
4 wks → 20 minnew service scaffold time
58%reduction in CI/CD pipeline duration
70%less IaC written from scratch
68% test coverage with a backlog of 800+ untested scenarios. Every release requires manual regression runs taking 3 days. QA team outnumbered by feature output 4:1.
91%test coverage achieved in 60 days
3 days → 2 hrsregression cycle time
12,000+AI-generated test cases in first quarter
800,000 lines of COBOL and Java 8 monolith. 3-year estimated timeline for manual refactoring. Domain knowledge locked in the codebase with no documentation.
3 yrs → 14 moestimated migration timeline
100%module documentation generated before migration
40%of refactoring completed by autonomous agents
The AI engineering gap is already compounding.
55%
faster task completion for AI-assisted developers — the largest productivity leap in the history of software engineering (GitHub Octoverse, 2024).
3×
more frequent deployments at AI-native engineering organisations — the compounding advantage grows every quarter as AI capabilities expand.
40%
of new code at leading technology companies is now AI-generated and human-reviewed — the ratio is increasing by ~10% per year.
2027
the year most enterprise software teams will have a majority of routine coding done by AI agents, with humans in an architecture and review role (Gartner).
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Find out exactly how much velocity your engineers are leaving on the table.
Start with a free Engineering Velocity Assessment — we'll baseline your DORA metrics, identify your top three AI-native opportunities, and show you what a 2-week pilot sprint would look like before you commit to transformation.