How we work with teams

Help your team adopt AI-assisted development in a way that increases delivery speed, reduces developer friction, and keeps humans firmly in control.

1
Understand your team
2
Analyze your pipeline
3
Implement safe patterns
4
Train your team
5
Validate in a live sprint
6
Transition to independence

Every team's path is different. Not every engagement includes every step.

Discovery

Free — conversation or automated assessment

This isn’t a sales pitch. It’s a working session to determine whether agentic development will actually help your team — and where.

  • Current development workflow review
  • Tool and process inventory
  • AI readiness and friction mapping
  • Fit assessment — honest, both ways

Agentic Development Pipeline Analysis & Design

Paid assessment

A structured audit of how code moves through your development pipeline — where friction lives, and where agentic coding can safely reduce cognitive load, review overhead, and delivery time. The goal is not just AI usage — it’s a repeatable engineering process your team can rely on.

  • Repository structure and conventions review
  • CI/CD and toolchain analysis
  • Developer workflow observation
  • Friction and automation opportunity mapping
  • Written deliverable: analysis report with prioritized recommendations

Custom Template & Pipeline Consulting

Implementation engagement

We build the scaffolding your team needs to use AI-assisted development safely and repeatably, with deterministic workflows and guardrails that prevent common AI failure modes.

  • Custom project templates and context engineering
  • Guardrail and permission design
  • Task structure and workflow scaffolding
  • Tool integration (IDE, CI/CD, version control)
  • Handoff documentation

Team Training

1–2 days, customized

Every training is built on the results of your pipeline analysis. No generic workshops. This is not theory. Training happens inside the workflows and repositories configured during the engagement.

  • Role-specific training (developers, QA, managers)
  • Hands-on exercises in your actual repos
  • Prompting and automation skill building
  • Common template usage and customization
  • Follow-up reference materials

Embedded Pilot Sprint

Premium engagement — 1–2 weeks

We work alongside your team inside a real sprint. We set up repo structure, context files, guardrails, and workflows — then we use them together. Your team finishes with working patterns they’ve already practiced.

  • Full environment setup on a live project
  • Side-by-side pairing with developers
  • Real-time workflow tuning
  • Pattern documentation as you go
  • Sprint retrospective with recommendations

Retained Advisory

Ongoing support

As new tools appear and models evolve, we help your team adapt without disrupting your workflow.

  • Regular check-ins (cadence set by your needs)
  • Async support for questions and blockers
  • Workflow refinement as tools evolve
  • New pattern and tool evaluation
  • Team scaling guidance

We're tool-agnostic. The concepts don't change.

We have our favorites, but every company has different needs, constraints, and existing investments. We work with whatever tools fit your team — Claude Code, Copilot, Cursor, ChatGPT, open-source models, MCP-based tooling, Azure DevOps, GitHub Actions, and more.

The underlying principles — context engineering, guardrails, structured workflows, human oversight — apply regardless of which AI tools you choose. We help you build patterns that survive the next tool change.

Recent engagementCase Study

From ad-hoc prompting to a team-wide AI development workflow

Full team
onboarded end-to-end
PR throughput
improved post-rollout
Bug count
dropped with guardrails
Client
Small SaaS and hardware engineering team
Challenge
Developers were experimenting with AI tools individually, mostly copy-pasting between chat interfaces and their IDE. There was no shared workflow, consistency, or way to evaluate what was working.
Approach
HelpIRL analyzed the team’s development pipeline, selected tools suited to their stack, and introduced shared templates and guardrails for AI-assisted development.
Implementation
AI tools integrated into the team’s workflow, common templates created, and one-on-one training provided for developers, QA, and managers.
Operational Change
The team moved from ad-hoc prompting to a repeatable AI-assisted development workflow used across the engineering group.
Results
PR throughput improved, bug counts dropped, and developers aligned on shared patterns that made code review faster and more predictable.
Adoption Curve
Velocity dipped briefly during training but improved as the team refined their shared templates and workflows.
Status
Team now operates with a structured AI development process and continues refining patterns internally.

Detailed metrics from this engagement are still being compiled. We're happy to discuss outcomes and lessons learned in more detail.

What we don't do

  • Sell AI as a solution to problems you don’t have
  • Promise automation of work that requires human judgment
  • Build systems that bypass human oversight
  • Deploy AI into production pipelines without guardrails
  • Recommend tools we haven’t used ourselves
  • Lock you into proprietary tools or frameworks
  • Require long-term contracts to get started

Have a workflow that could use less friction?