Chapter 01 Weeks 1-4
Build
Engineers ship a backend from an empty repo with a CLI-based coding agent as their primary tool. They practice service design, external APIs, structured model output, persistence, and layered architecture.
Codo Academy
/
For Engineering Teams
AI-assisted backend engineering cohort.
A private 12-week program for senior backend teams that need to adopt AI coding without weakening review standards, production ownership, or leadership visibility.
- + Standardize how engineers use AI in real delivery work
- + Create shared review expectations for generated code
- + Train one pilot team before rolling the workflow across the org
- Program
- Private 12-week cohort
- Pilot team
- 8-12 senior backend engineers
- Leadership outcome
- Shared AI delivery standards
Why this exists
Two failure modes are showing up across senior engineering teams.
Failure mode 01
Rejection.
Engineers refuse to ship code they didn't author. AI tools sit unused. The team works the same way it did five years ago, on a problem that has changed underneath them.
Cycle times stay flat. Senior engineers stay reviewing every diff line by line.
Failure mode 02
Rubber-stamping.
Engineers merge whatever the model produces. There are no contracts catching schema drift and no system tests catching the regressions. Production starts paging at 2am for reasons no one on the team can trace.
Review queues grow. On-call gets noisy. Quality drifts across the codebase.
Why this matters now
AI usage is already happening inside engineering teams. The risk isn't whether engineers use it. The risk is that they use it unevenly, without shared standards, and without leadership visibility into where it helps or hurts delivery.
That creates variance in quality, risk, and throughput at exactly the moment leaders need consistency. Codo Academy gives one team a shared, production-grade workflow before that drift becomes structural.
What leadership gets
A pilot team, a shared operating model, and a decision point.
The cohort is not a tools tour. It is a controlled way to make one backend team adopt AI-assisted delivery with standards your leadership can inspect.
- 01
One team operating from a shared AI delivery playbook.
Planning, generation, review, testing, diagnosis, and hardening stop being individual habits and become a visible team workflow.
- 02
Clearer standards for accepting generated code.
Engineers practice the checks that make AI output reviewable: contracts, schema validation, tests, dependency rules, and operational signals.
- 03
A practical read on where AI helps or adds risk.
Leadership sees AI usage under realistic delivery pressure, not in isolated demos or scattered personal experiments.
- 04
A repeatable pilot model for expansion.
The first cohort leaves behind standards, a reference repo, and a working pattern other backend teams can inspect and adapt.
What engineers practice
Capabilities, not certificates.
Engineers leave with a working reference system, an eval harness, a guardrails layer they can port into your codebase, and the habits to use AI inside production delivery.
- 01
Reviewable generated code.
Engineers learn where to put contracts, schema validation, tests, dependency rules, and observability so generated code can meet existing production expectations.
- 02
A repeatable AI delivery loop.
Plan with the model, generate, run system tests, read CI feedback, and iterate. The work moves from scattered prompts to one team workflow.
- 03
Production pressure in a real environment.
Engineers harden a backend under realistic constraints: persistence, queues, observability, rate limits, graceful shutdown, load tests, and failure recovery.
- 04
Incident diagnosis with model support.
When the system breaks, engineers practice using logs, traces, metrics, and model-assisted investigation to find the cause instead of guessing from output by eye.
How it works
Format and twelve-week arc.
We teach in TypeScript and AWS because the environment is concrete and observable. The transferable layer (architecture, contracts, evals, testing, incident diagnosis, AI-assisted delivery) maps directly to Java, Python, Go, and .NET.
- Duration
- 12 weeks · 8 sprints · 12 live sessions
- Audience
- Senior backend engineers (3+ years) shipping production systems
- Cohort
- 8-12 senior engineers, private to one company
- Delivery
- Hybrid (remote + on-site) or fully remote
- Infrastructure
- AWS workspaces with deploys, load tests, observability, and CI
- 1-on-1s
- Architecture sessions with Codo engineers every two weeks
Chapter 02 Weeks 5-8
Architect
Engineers add cache, queue, and worker primitives, deploy to AWS, and run load tests that expose the system's limits. AI becomes part of the diagnosis loop, not just the code generator.
Chapter 03 Weeks 9-12
Harden
Engineers replace simulations with production-grade dependencies, add observability and rate limits, practice graceful shutdown, and defend the architecture under failure scenarios.
Who this is for
Built for senior backend teams. Specific by design.
Best for
- + Senior backend teams starting with one dedicated cohort of 8-12 engineers
- + Engineering leadership willing to protect 4-6 hours/week of engineer time
- + Teams with meaningful backend ownership and production responsibility
- + Companies setting AI engineering standards across services
Not a fit
- − Junior-heavy teams that need fundamentals-first training
- − Teams without sponsorship to protect time and enforce standards
- − Teams looking for a one-off AI tools talk
- − Teams shipping their first production service
Who runs it
Practitioners. Not trainers.
Five years embedded with engineering teams, shipping systems that have to keep working after the launch. Architecture, CI, observability: the parts that decide whether code is safe in production. Generated code needs more of that work, not less. Codo Academy is how we hand our engineering loop over to your team.
Common questions
What every engineering leader asks first.
Why not build this internally?
Most teams can write an AI policy. Fewer can turn it into shared engineering behavior under delivery pressure. The cohort gives one team facilitation, production-grade exercises, and a fixed deadline for adopting the standard.
Is this about a specific AI tool?
No. We teach the delivery loop: planning, generation, review, testing, diagnosis, and hardening. The model and vendor setup can match your internal policy.
How do you measure success?
We define success with the engineering sponsor before kickoff. Typical indicators include adoption consistency, shared review standards for generated code, AI used inside real delivery work, and less ambiguity about where AI is safe and useful.
How much management overhead does this require?
Low. We ask for one engineering sponsor, access to the relevant team workflows, and a short checkpoint cadence. The program is designed to improve execution without creating a parallel management process.
What are the security boundaries?
The cohort runs in a dedicated training repo under your org, not your production codebase. We use your approved model setup, isolate AWS workspaces per engineer, decommission them at close, and leave you with the repo, reference implementation, and test harness.
What budget range should we expect?
Pricing depends on team size and scope. Most buyers start with a single-team pilot so they can evaluate fit before broader rollout. We share specific numbers on the fit call.
Fit call
A 20-minute call with Omer or Doron.
We will assess fit, show how the cohort works, and tell you plainly whether this is worth piloting for your team.
Globally remote · Tel Aviv hub · Private cohorts available · Procurement-friendly · NDA-ready