Context-Driven Engineering
The AI delivery operating model for enterprise software practices — Engineering, Product, BA, QA, Release, and Design.
Your engineering team has Copilot. Your code gets generated. But is your AI working with your process, or against it?
What about everyone else in the delivery lifecycle? CDE connects every role — Product, BA, QA, Release, Engineering, Design — into one AI-readable system. Requirements, tickets, code, tests, environments, logs, and decisions: connected, governed, and pulling the whole practice along together.
What tools like Copilot, Cursor, and Devin don't do
Code generators are good at what they do. But they run without your organizational context. That gap is where CDE lives.
| Gap | What it costs you |
|---|---|
| They don't know your requirements | AI writes code that passes tests but misses the spec |
| They don't know your ticket history | Same investigation repeated across sprints — wasted days |
| They don't know past decisions | AI proposes approaches your team already tried and rejected |
| They don't know your environments | Deployments that "work on my machine" but break in UAT |
| They don't enforce governance | Uncontrolled AI generation with no quality gates, no audit trail |
| They can't reason across repos | Changes in Service A silently break Service B |
CDE fills every one of these gaps.
The Four Pillars of CDE
1. Context
What does the AI need to know?
CDE synchronizes your requirements, tickets, code, tests, environments, production logs, and decisions into a coherent, AI-queryable system. Every agent works from full project context — not guesswork.
2. Knowledge
What can the AI do?
Purpose-built capability agents handle planning, implementation, bug fixing, code review, security analysis, testing, deployments, log analysis, and more. Each is specialized, project-aware, and aware of your conventions.
3. Verify
Is it correct?
Requirements-driven testing. Acceptance criteria verification. Production evidence. A feature is not done until it is proven — not just "tests pass."
4. Govern
Should the AI do this?
Roadmap-first creation. Ownership boundaries. Human approval at meaningful risk points. Full audit trail. AI that does what it should — and only what it should.
What CDE can do for your team
CDE ships with governed capability agents spanning every phase of your SDLC. Each is customized to your stack, conventions, and toolchain during onboarding.
Planning & Requirements
- Requirements query and gap analysis against Confluence
- Efficient Jira ticket queries — counts, summaries, full detail on demand
- User story generation with acceptance criteria
- Sprint breakdown and dependency mapping
- Cross-requirement traceability to code
Design & Architecture
- Architecture decision record (ADR) authoring
- Structural impact analysis before changes
- Cross-repo dependency discovery and documentation
- STRIDE threat modelling before implementation
- Pattern proposals with rationale and trade-offs
Implementation
- Feature implementation across all layers (data → service → UI)
- Test-driven development with Red-Green-Refactor enforcement
- Bug investigation, root cause analysis, and fix — end to end
- Refactoring with behaviour preservation and test validation
- Code generation tuned to your stack, conventions, and patterns
Quality & Testing
- Automated code review against project conventions and OWASP
- Acceptance criteria verification against requirements
- E2E test authoring and execution (Playwright)
- Supply chain security assessment (SBOM, SLSA)
- Quality gate enforcement before merge
Security
- OWASP Top 10 scan on every endpoint and form handler
- Secrets detection in commits and config
- Injection, XSS, and authentication vulnerability analysis
- Security review delegated automatically on auth changes
- Adversarial challenge mode — AI argues against its own proposals
Operations & Observability
- Production log analysis via Grafana / Loki (LogQL)
- Root cause analysis document generation from live logs
- Incident timeline reconstruction from metrics and traces
- Runbook authoring from operational patterns
- AIOps — predictive detection from Prometheus metrics
How CDE compares
Your team likely already uses one of these tools. CDE doesn't replace them. It's the operating-model layer that makes every tool more effective.
| Tool / Approach | Their strength | CDE's differentiator |
|---|---|---|
| GitHub Copilot Enterprise | Distribution, Microsoft brand, deep IDE integration | Copilot generates code from prompts. CDE is the delivery operating model that gives Copilot full project context, making it more effective — not a replacement. |
| Microsoft HVE Core | A polished agent marketplace with a permissive licence | HVE agents are task workers with no project memory. CDE adds requirements integration, cross-repo reasoning, institutional memory, and governance that HVE doesn't provide. |
| Cursor / Windsurf | A polished code-editing experience | IDE-locked tools. CDE is process-layer and toolchain-agnostic — it works alongside any IDE, including Cursor, and adds the organizational context those IDEs can't carry. |
| Devin / SWE-Agent | High autonomy — runs end-to-end without developer input | Unsupervised AI. Enterprise buyers purchase governance. CDE provides governed AI with human approval gates, audit trail, and requirement traceability. |
| Big 4 / IBM Consulting | Trust, scale, global delivery capacity | No product — every engagement is bespoke and slower. CDE gives our consulting a head start on methodology and produces reproducible outcomes across every client. |
| Atlassian Intelligence | Already embedded in the tools your teams use | Surface-level platform features. CDE is the operating model that uses Atlassian as a context source — requirements, tickets, and decisions feeding governed agents. |
| Atlassian Rovo | AI search and agents natively integrated with Confluence and Jira | Rovo indexes content but does not structure it for delivery workflows. CDE's Jira and Confluence integration goes further — efficient cross-requirement traceability to code and ticket workflow automations including automated RCA generation directly on raised incidents. CDE also works across non-Atlassian toolchains. |
CDE is a structured, opinionated AI delivery operating model. It plugs into the toolchain you already run, keeps your team's past decisions in reach, and enforces governance. The consulting makes sure it actually gets used.
How we deliver CDE
Discovery & Codebase Onboarding
We traverse your codebases, map your toolchain, and configure CDE to read your entire delivery context. Within days, AI can answer: "What are the requirements for feature X, where is it implemented, and is it tested?"
Agent Customization & Pilot
We customize CDE agents to your conventions, tech stack, and workflows. Then run a focused pilot on one codebase — proving value on real work in weeks, not months.
Multi-Codebase Rollout
We expand across your portfolio: additional repositories, cross-project impact analysis, operational intelligence from production logs, and team-wide enablement.
Continuous Evolution
Quarterly health checks, new agent development, skill refinement based on team discoveries, and ongoing support ensure the framework stays ahead of your needs.
Continuous Evolution & Sustainment
After rollout, some teams prefer to have us sustain the ongoing health and evolution of their CDE implementation. Sustainment is an optional, subscription-based advisory engagement— it keeps your AI delivery practice sharp sprint after sprint without requiring your team to develop deep CDE platform expertise in-house. The framework still runs on your infrastructure; this is not a multi-tenant SaaS.
Engagements are scoped per customer based on team size, codebase count, and desired SLA.
Ask about SustainmentQuarterly Health Checks
Structured review of agent accuracy and quality gate pass rates. Output: a prioritized improvement roadmap for the next quarter.
Agent Tuning & Optimization
Ongoing refinement of agent definitions, skill modules, and context configuration as your team conventions and codebase evolve.
Custom Skill Development
New skills authored for proprietary frameworks, new toolchain integrations, or domain-specific workflows discovered post-rollout.
Escalation Support
Named support contact for complex agent failures, codebase onboarding, or governance questions — with agreed response SLAs.
Governance reporting, drawn from your own data
Every CDE deployment includes governance reporting drawn from your real project data — agent quality trends and a full audit trail of what the AI did and why. Reports run deterministically: no manual entry, no model dependency.
Your tech lead sees the quality trends; your compliance team gets the audit trail. It comes from your environment, not a number printed on a marketing page.
Every rollout is a hands-on engagement
CDE is not a download. We install it on your infrastructure, prove it on your own work, and leave your team able to run it. Four steps, each delivering value on its own.
Discovery & Implementation
We audit your codebases, install CDE, connect your tools, and train your team — with a baseline measurement and a 12-month plan.
30-day evaluation
Run it on your own work for a month. At day 30 you commit or walk away — a real evaluation, no obligation to continue.
Expansion
Add codebases, connect more systems, and commission custom agents at your own pace.
Sustainment
Optional ongoing advisory: health checks, agent tuning, new skills, and named escalation support.
The framework, the process, and the skills to use them well
Most “AI training” is one of two things: prompt tricks that age out in a quarter, or generic AI literacy you can’t measure. Our curriculum is neither. It names the ten things you actually do — or fail to do — when working with an AI agent, scores each one on a five-point scale, and makes improvement visible.
It runs alongside the consulting engagement, not in a classroom. The coach narrates the skill being used while real work happens. Intake and exit scorecards make change visible. A follow-up assessment later in the year checks that the habits stuck.
Calibrating AI confidence
The AI sounds equally sure when it’s right and when it’s wrong. Treat confident answers as guesses until you’ve checked them.
Knowing when to push back
Spot the moment the AI is agreeing too eagerly, inventing an API, or quietly dropping a constraint — and redirect it instead of absorbing the bad output.
Naming the missing context
The AI doesn’t know your codebase quirks, last week’s decisions, or your customer’s environment. Tell it, instead of waiting for it to ask.
Discuss versus directive
Know when to think out loud with the AI and when to authorise it to act. Both extremes fail — endless dialogue, or executing too early.
Structured problem decomposition
Break a vague ask into a sequence of small, specific questions the AI can actually answer well.
Iteration cadence
Know when to refine the current attempt and when to scrap it and re-prompt from a different angle. Refining for too long is the most common failure.
Tool, context, and model choice
Pick the right model, the right context to attach, and the right rules for the task. Top-tier for everything wastes money. Mid-tier for everything wastes the leverage.
Recognising sunk cost
Throw out two hundred lines the AI just wrote the moment you realise the premise was wrong. Don’t defend the work because the AI already did it.
Verification discipline
Treat every AI answer as a guess until something independent confirms it — a compile, a test, the actual docs, a second source.
Emotional regulation
Stay steady when the AI mis-fires and stay critical when it nails one. The last interaction shouldn’t decide the next.
How each skill is scored — 1 to 5
Ten skills, five points each, fifty in total. A low score points to the full curriculum. A middling score points to focused coaching on the lowest three skills. A high score shifts the engagement toward capturing what your team already does well, so the rest of the practice can learn it.
Free ten-skill diagnostic
Score your team against the ten skills in about ten minutes. You get a per-skill rating, a band placement, and the three skills to address first. No sales call required.
Built for your organization
A whole software practice — engineering, product, BA, QA, release, design — with established codebases, formal SDLC processes, and existing tooling. AI that works with every role’s process, not around it.
- Established codebases that have been around for years
- Mixed technology stacks across multiple repos
- Existing tooling (Jira, Confluence, Jenkins/ADO)
- Pain from slow onboarding and undocumented decisions
- AI adoption without governance or audit trail
Industries: Financial services, government, insurance, healthcare IT, supply chain, regulated manufacturing.
Only the largest enterprises can afford to build a fully custom AI platform from scratch. Everyone else is left with a self-service tool that generates code without the context of your requirements, your decisions, or your systems.
CDE is built for that middle ground: a governed, ready-to-adopt option that gives AI your full project context — without a full in-house build.
Runs entirely on your infrastructure
CDE installs into the environment you already run. Your code, requirements, and decisions stay inside your network.
Your code never leaves your infrastructure. CDE runs on-premise. No source code, requirements, or internal data is transmitted to or stored by us.
Only the AI tools you authorize. Source, requirements, and decisions are sent only to the AI providers you approve — never anywhere else.
Governed by design. Human approval at meaningful risk points, quality gates before merge, and a full audit trail of what the AI did and why.
Where is CDE heading?
From developer augmentation today, to bounded delegation tomorrow, to full lifecycle orchestration as AI matures. See how the framework grows with you over time.
Frequently asked questions
How is this different from GitHub Copilot Enterprise?
Copilot generates code from prompts. CDE is the delivery operating model that gives AI full project context, enforces governance, and preserves institutional memory. The two are complements — CDE makes Copilot (and every other AI tool) more effective.
Do we need to change our existing tools?
No. CDE connects to your current toolchain — Jira, Confluence, Jenkins, Azure DevOps, GitHub, Bitbucket, Grafana — and uses what you already have.
How long until we see results?
The pilot delivers value on real work — bug fixes, code reviews, test generation, documentation — on your actual codebase. Engagement length is scoped per customer.
Does our code leave our infrastructure?
No. CDE runs on your infrastructure. Your code, requirements, and data never leave your environment. Regulated buyers can run it with zero outbound connectivity.
Can we customize the agents?
Yes. Every agent is customizable to your conventions, naming patterns, technology stack, and workflow. The consulting engagement tunes them to your team. Custom skills are a first-class part of the framework — your teams can author, version, and distribute their own under framework governance.
What governance reporting do we get?
CDE includes governance reporting that draws on your real project data — agent quality trends and a full audit trail. Reports run deterministically, with no manual data entry. Your tech lead gets quality trends; your compliance team gets the audit trail.
What is your SOC 2 posture?
A SOC 2 readiness program is in place covering Security, Availability, Confidentiality, and Processing Integrity. Your code, activity log, and tokens are deliberately outside our audit boundary because they stay on your infrastructure.
Ready to make your AI understand your business?
Book a discovery call. We'll map your current delivery process and show you where CDE creates leverage — no commitment, no pitch deck, just an honest conversation about whether this fits.