FDEmax

Services

Delivered inside your environment, or not at all.

We do not sell standalone software. Every capability below is deployed and operated by forward deployed engineers inside your stack, with approval checkpoints, audit logs, and enterprise access controls.

01Legacy Modernization

Your oldest systems, made secure and fast.

The systems that run your business are often the ones nobody wants to touch. Our FDEs specialize in exactly those: upgrading frameworks and runtimes, closing security gaps, and cutting latency and build times, without a risky big-bang rewrite. Work lands as a stream of small, reviewed pull requests against the code you already run.

What this enables

  • Framework, runtime, and dependency upgrades with regression coverage
  • Performance work: profiling, query tuning, caching, build-time reduction
  • Strangler-pattern migrations instead of big-bang rewrites
  • Test scaffolding around untested code before anything changes

CIO outcomes

  • Measurably faster systems: p95 latency and build times, before and after
  • A shrinking list of known vulnerabilities in the legacy estate
  • Upgrades stop being a feared annual project and become routine

02Continuous Security Remediation

We do not just detect risk. We close it.

Scanners produce lists; someone still has to do the work. Our agents triage findings by real exploitability, generate candidate patches with regression validation, and open pull requests your team reviews. The embedded FDE owns the stubborn cases that need human judgment.

What this enables

  • Risk-based prioritization of exploitable vulnerabilities, not raw CVE counts
  • Automated patch generation with regression validation
  • Dependency and base-image updates as a continuous stream of PRs
  • Remediation integrated into your existing CI/CD, not a parallel process

CIO outcomes

  • Mean time to remediate drops from months to days
  • Audit-ready trail: every fix is a reviewed, merged pull request
  • Security debt trends down while feature work continues

03Autonomous FinOps Governance

Cost control becomes structural, not reactive.

Cloud overspend is an engineering problem wearing a finance costume. Our FDEs deploy and operate an AI-driven cost layer inside your accounts that watches spend patterns in near real time and enforces the policies you set, with corrective actions proposed as changes you approve.

What this enables

  • Cross-cloud anomaly detection in near real time
  • Policy-based cost controls with automated corrective actions
  • Rightsizing and cleanup proposals as reviewable infrastructure changes
  • Spend visibility mapped to teams and services, not just accounts

CIO outcomes

  • Overspend caught in hours, not on next month's invoice
  • Structural guardrails that prevent regressions after the cleanup
  • Engineering and finance argue from the same numbers

04Git-Native Agents

AI that works where your code lives.

Our agents do not live in a chat window. They live in your repositories and CI: reading the codebase, running the tests, and opening pull requests with clear descriptions. The PR is the unit of work, review is the control point, and your git history is the audit trail. This is the engine behind every other service we run.

What this enables

  • Agents scoped to your repos with least-privilege access
  • Every change ships as a PR with tests and a rationale
  • Human approval gates: nothing merges without review
  • Full audit trail in the tools you already use

CIO outcomes

  • AI throughput without AI chaos: velocity with a review gate
  • No new platform to buy, learn, or secure
  • Institutional knowledge accumulates in commits, not chat logs

Engagement Models

Start small. Scale on evidence.

Every engagement starts with discovery, because the documented process is rarely the real process. Delivery sprints follow only when the evidence says they should. No pricing games, no long minimums.

Step one

Discovery Sprint

30 days, one operation

A senior FDE embeds in week one and traces how the work actually happens. Discovery follows the pain: for an ops team that means workflows, handoffs, and exception paths; for an engineering team, the codebase, the pipeline, and the backlog. You end the sprint with a map of your operation, improvements ranked by expected value, and a roadmap you own whether or not we continue.

Start a Discovery Sprint

Step two

Delivery Sprints

Scoped sprint by sprint

When the map shows work worth doing, we ship it in focused sprints. Each one targets a single workflow from the roadmap, with success criteria agreed before it starts: modernization, security remediation, FinOps, or feature delivery. Scale up or down between sprints as the evidence dictates.

Discuss delivery sprints

Inside a delivery sprint

One workflow. One measurable outcome. Then you decide.

A delivery sprint is a few weeks of focused work, not an open-ended retainer. It runs the same loop every time, and every sprint ends at a decision point you control.

01

Scope

One workflow from the discovery roadmap, chosen for value. We agree on success criteria and boundaries before any code is written: what the system may do, what stays human, and what number has to move.

02

Build

The embedded FDE and git-native agents work inside your repos and systems. Every change arrives as a reviewable pull request with tests. Nothing lands without your approval.

03

Measure

The sprint ends with results reported against the criteria we set at the start: hours saved, error rates, cycle time. Real numbers, not a satisfaction survey.

04

Decide

You keep everything the sprint produced either way. The evidence decides whether the next sprint happens, which workflow it targets, and whether capacity scales up or down.

Sprints compound. The first one usually targets the workflow with the clearest value from the discovery map. Later sprints extend it, harden it, or move to the next workflow on the roadmap. There is no multi-quarter commitment hiding behind the first yes.

Ready to see it running in your stack?

Talk to an FDE