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Home / Portfolio / Claude Code Coding Speed Lift
CASE STUDY

How Brilworks Doubled Coding Speed at Two Clients with Claude Code

Brilworks's anonymized case study on rolling out Claude Code at two product engineering teams: ~2x coding-speed lift, the four-step playbook, and where the agent does and doesn't compound.

Category: Engineering Enablement / B2B SaaS
Talk to Brilworks About Claude CodeBuild Something Similar
~2x
Productivity gain on agent-eligible tasks (both clients)
2
Clients across different industries (hospitality SaaS + smart-home)
3
Stage rollout: audit, agent design, enablement
How Brilworks Doubled Coding Speed at Two Clients with Claude Code
Overview

The Challenge & Vision

Two of our clients adopted Claude Code with Brilworks's help in the last two quarters. One was a hospitality / restaurant-tech SaaS we'll call HostCo; the other was a smart-home and connected-device platform we'll call HomeCo (real names withheld under NDA). Different industries, different stacks, but the same engineering reality: senior-engineer leverage was getting eaten by integration boilerplate, deferred refactors, and — at HomeCo — cross-runtime tickets touching firmware, backend, and mobile in a single change. Both teams had tried ad-hoc AI assistants; the results were uneven. They wanted leverage, not novelty. By the end of each engagement, both teams reported a roughly 2x productivity improvement on agent-eligible tasks.

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Where senior engineering time was going

Across both engagements, the same patterns kept eating throughput. Different industries, different stacks — same shape of bottleneck:

  • Customer-specific integration scaffolding — payment gateways, POS connectors, loyalty platforms, marketing-automation hooks at HostCo: important per deal but boilerplate per ticket.
  • Long-deferred refactors blocked behind capacity constraints — at both clients, large refactors slipped quarter after quarter because nobody had a free week to take them on.
  • Test coverage gaps in older modules at HostCo — every change in those modules ran slower than it should have because of missing safety nets.
  • Cross-runtime tickets at HomeCo — telemetry fields, alert types, permissions touching firmware, cloud, and mobile in a single change, with domain knowledge concentrated in a few senior engineers.
  • Hardware compatibility constraints at HomeCo — legacy modules accumulated faster than in pure-cloud SaaS because rewrites were expensive.
  • No standard at either team for which tasks were worth handing to an AI assistant and which were not.
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Solution

What We Built

  • ✓

    Audit and surface selection — mapped each repo to where Claude Code would compound the fastest (integration scaffolding, test generation, well-scoped refactors), deliberately excluding surfaces where the agent would create more review load than it saved.

  • ✓

    Agent design and tooling — codified team conventions (naming, error handling, test patterns, commit messages) into Claude Code instructions; wired in lint, type-check, and test runners so the agent self-verified before opening a PR.

  • ✓

    Enablement, not handoff — paired with senior engineers across two sprints, drafting prompts together, refining the instruction file, and tightening review heuristics. The team owned the agent at the end of the engagement.

  • ✓

    Multi-runtime tuning at HomeCo — instruction files per runtime (firmware, cloud, mobile) rather than per repo, with explicit go/no-go criteria for staging firmware and mobile work behind cloud-side wins.

  • ✓

    Cross-runtime ticket pattern at HomeCo — the most leveraged use of the agent turned out to be tickets where one engineer drove a change through firmware, cloud, and mobile with the agent handling the cross-cutting boilerplate.

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