Forward Deployed AI Engineering for Claude Code, Codex, and Your Real Repository
Zenveus embeds a senior engineer into your delivery workflow to configure AI coding systems, implement live backlog work, install quality and security guardrails, and leave your team with a measured operating model.
What is a forward deployed AI engineer?
A forward deployed AI engineer is a senior engineer who works inside a client’s existing team, codebase, and delivery system. Unlike a strategy-only consultant, the engineer configures tools such as Claude Code and Codex, implements workflows in the real repository, adds quality and governance controls, and transfers the operating system to the internal team.
Who is the 30-day FDE engagement for?
- Software teams already using Claude Code, Codex, Cursor, or Copilot
- Engineering leaders who cannot yet prove AI delivery ROI
- Teams with an active production repository and real backlog work
- Organizations that need source-code, data, and permission guardrails
- Developers losing time to inconsistent prompting and review rework
- Leaders who want implementation and handover, not another strategy deck
This is for teams ready to change the delivery system
- An active engineering team owns a production repository and real backlog
- Claude Code, Codex, Cursor, or Copilot usage already exists but varies by developer
- Quality, security, cost, or permission controls need to become repeatable
- Leadership wants implementation, measurement, and an internal handover
Choose a smaller first step when…
- There is no production repository, delivery team, or representative backlog yet
- The need is limited to buying licenses, introductory training, or a strategy deck
- No internal owner can adopt the workflows and controls after day 30
- The team cannot provide access to representative work or agree a baseline
The coding agent is rarely the bottleneck. The surrounding system is.
Context gaps
Agents guess because architecture rules and examples are not repo-native.
Wide-scope changes
Fast generation creates regressions that slow review and release.
Weak validation
Generated output reaches pull requests without representative tests.
Permission risk
Secrets, data boundaries, and tool access vary between developers.
Tool sprawl
Claude, Codex, Cursor, and Copilot lack a shared operating model.
No baseline
Usage and token counts are reported while delivery outcomes remain unclear.
The AI engineering operating system your team keeps
Repository context
Architecture rules, repo instructions, and golden examples.
Repeatable workflows
Planning, implementation, testing, review, and debugging.
Quality gates
Tests, static analysis, evaluations, and rollback expectations.
Governance
Permissions, secrets, data boundaries, model access, and cost.
Approved integrations
Source control, tickets, documentation, and telemetry.
Capability transfer
Team adoption, dashboards, reusable playbooks, and ownership.
Concrete assets delivered inside your engineering system
Repository package
Instructions, architecture rules, and approved examples committed with the code.
Workflow playbooks
Planning, implementation, debugging, testing, review, and documentation workflows.
Evaluation suite
Representative tasks and automated quality gates connected to delivery.
Governance policy
Model, permission, secrets, data-handling, auditability, and cost controls.
Team handover
Pairing, onboarding material, operating playbooks, and a named internal owner.
Day-30 readout
Week-one baseline, day-30 scorecard, retained configurations, and next-scope decision.
How Claude Code, Codex, and the surrounding stack fit together
Claude Code
Repository-aware coding agent for planning, implementation, debugging, and tool use.
OpenAI Codex
Cloud and local coding agent workflows for parallel implementation, review, and validation.
GitHub Copilot
Editor and pull-request assistance configured around team conventions and review policy.
Cursor
Agentic IDE workflows constrained by repository rules, tests, and review boundaries.
Model Context Protocol
A shared tool layer connecting agents to approved documentation, tickets, data, and systems.
OpenAI API
Model, tool-calling, evaluation, observability, and cost controls for product integrations.
Anthropic API
Claude API patterns for tool use, prompt caching, streaming, routing, and production limits.
GitHub
Repository policy, branch controls, pull-request workflows, ownership, and auditability.
CI/CD
Automated validation that keeps higher code output from bypassing production safeguards.
Playwright
Representative end-to-end evaluations for real user paths, regressions, and release confidence.
How the forward deployed engagement works
Step 1
Technical fit call and access plan
Step 2
Repository audit and delivery baseline
Step 3
Workflow, context, security, and model configuration
Step 4
Live backlog implementation, pairing, and weekly readouts
Step 5
Day-30 measurement, handover, and optional next scope
Live Codebase AI Audit
Live, senior-led review of one active codebase.
- Audit the codebase live on the call
- Surface delivery, quality, and security gaps
- Prioritize the highest-impact AI workflows
- Leave with a sequenced implementation plan
30-Day AI FDE
Fixed 30-day implementation inside your repository.
- Work directly inside the production codebase
- Configure Claude Code, Codex, and team workflows
- Install quality, security, and measurement controls
- Pair the team and hand over retained playbooks
AI Leadership POD
Embedded AI leadership and delivery without a full-time CAIO hire.
- Fractional Chief AI Officer direction
- Senior AI engineering delivery capacity
- Governance, measurement, and portfolio roadmap
- Flexible POD scope as priorities evolve
Claude Code and Codex are capable tools. The FDE engagement supplies the repository context, controls, adoption system, and measurement layer required to use them consistently across a real team.
- Repository context
- Repeatable team workflows
- Quality and security gates
- Live backlog implementation
- Cost and model governance
- Balanced delivery baseline
- Team handover
- Usually left to each developer
- Varies by user
- Depends on existing setup
- Not included
- License and token controls only
- Usage metrics
- Documentation from vendor
- Mapped to your architecture
- Implemented and paired in your repo
- Configured and validated
- Included in the 30-day scope
- Policies, budgets, and auditability
- Delivery, quality, review, cost, adoption
- Configurations and playbooks stay with you
- When a tool-only rollout can make sense
- 1. Individual exploration
- 2. Low-risk prototypes
- 3. Teams with mature internal AI enablement
- 4. No requirement to demonstrate operational change
- When an FDE is the right choice
- 1. A production repository and active backlog
- 2. Multiple developers need consistent workflows
- 3. Security, data, or compliance constraints
- 4. Leadership needs a defensible baseline and handover
We do not promise a universal productivity percentage. We define the baseline in week one, measure the same system at day 30, and report gains and tradeoffs together.
- Delivery flow
- Quality
- Review flow
- Cost
- Adoption
- Ownership
- Decision
- Lead time and PR cycle time
- Defects, hotfixes, and rollbacks
- Latency, rounds, and context search
- AI cost per merged outcome
- Repeatable workflow usage
- Who maintains the system today
- Continue, adjust, or stop
- Same measures after implemented workflows
- Speed reported with failure rates
- Same review measures after pairing
- Useful output and failed-run waste
- Usefulness and standardized adoption
- Named internal owner plus retained assets
- Written executive readout
- What the baseline protects against
- 1. Reporting generated code as productivity
- 2. Speed gains that increase regressions
- 3. Adoption claims based only on license usage
- 4. Cost savings without delivery context
- What the readout gives leadership
- 1. Before-and-after operational measures
- 2. Quality and cost tradeoffs
- 3. Retained repository configurations
- 4. A clear next-scope decision
Delivered systems behind the 30-day operating model
1 shared foundation
Multi-tenant platform across isolated tenants, domains, and themes.
8 named AWS services
Delivery, data, security, communications, and infrastructure as code.
Under 60 seconds
Natural-language questions converted into live governed dashboards.
AI-assisted operations
Claude workflows supported migration, page generation, and reusable components.
Governed enterprise AI
Bedrock Agents, Guardrails, RBAC, and identity controls stayed in the customer environment.
Repeatable deployment
Docker, AWS, pytest, mypy, and automated infrastructure replaced manual rollout steps.
Delivered scope from an anonymized production platform transformation
Starting point
Repeated tenant setup, manual migrations, and developer-dependent content work.
Foundation
One shared multi-tenant platform became the reusable operating base.
Production stack
Eight named AWS services covered delivery, data, security, and communications.
Automated flows
Three flows covered migration, AI-assisted generation, and Playwright validation.
Operating impact
Launch, migration, content operations, SEO, and infrastructure improved.
Outcome
One-off delivery work became a repeatable production operating model.
In 30 minutes, we will identify whether the constraint is tooling, workflow, controls, adoption, or measurement. If the 30-day engagement is too large, we will say so and recommend the engineering baseline or a narrower audit.
How is an FDE different from an AI consultant?
A strategy consultant may assess and recommend. The FDE implements the agreed system in the real repository, pairs with the team, measures it, and transfers ownership.
Do we need to replace Claude Code, Codex, Cursor, or Copilot?
No. We evaluate the tools already in use and configure the mix that fits your repository, policies, representative tasks, and cost constraints.
What does the 30-day engagement include?
The scope includes a baseline, repository context, selected live workflows, quality and governance controls, team pairing, retained playbooks, and a day-30 readout.
How do you protect source code and internal data?
Access, permissions, secrets, data handling, model policy, sandboxing, and auditability are agreed before implementation and matched to your environment.
What does our team keep after day 30?
Your client-specific repository configuration, policies, agent skills, evaluation tasks, dashboards, findings, and operating playbooks remain with your team.
Is a second month required?
No. The core engagement is fixed at 30 days. Any follow-on implementation or enablement scope is optional and separately agreed.