Case Studies

Anonymized. Specific. Real.

Names are withheld because the systems are operationally sensitive. Industry, team size, the actual workflow we replaced, and the numbers that moved are not.

Engagements

4 shown

Workflow systems that took manual work off operators.

Integration work that ended cross-system data drift.

Agent-driven workflows in production.

AI training that landed past the early adopters.

Smallest

~50 people

Largest

~120 people

First system live

4–8 weeks

Case 01 B2B services · ~50 people Internal system + agents Stack · Swarm ↗

From three hours of account setup to a workflow that runs itself.

Every new account triggered three hours of cross-tool data entry across CRM, billing, and the onboarding tracker. Mistakes surfaced two weeks later as billing disputes and missed SLAs.

What we built

A workflow system with agent-driven steps for the routine path — data reconciliation between CRM and billing, contract document drafting, and exception routing. A reviewer console for the edge cases the team actually needs to see. Built on laravel-swarm and the team's existing stack.

What changed

Per-account ops time dropped ~85%. Billing disputes traceable to onboarding errors dropped to near zero. Leadership got a single source of truth on every new account — without adding headcount.

Ops time / account

~85% lower

Billing disputes

Near zero

First system live

~6 weeks

New hires

None

Case 02 Multi-product SaaS · ~80 people Integration + data layer

Stopped the weekly reconciliation meeting from existing.

Customer, billing, and product analytics systems disagreed on basic facts — active users, MRR, plan tier. Every leadership review opened with thirty minutes of "whose number is right." Finance was rebuilding the source of truth by hand each month.

What we built

A reliable sync layer between the three systems, with clear operational definitions of every metric and a failure-handling path that surfaced drift the day it happened instead of three weeks later. Reporting moved off spreadsheets onto a foundation finance and ops both trusted.

What changed

Monthly close shortened by roughly a week. The reconciliation meeting was retired. Leadership stopped debating the dashboard and started acting on it.

Monthly close

~1 week faster

Recurring meetings

Retired

Drift detection

Same-day

Engagement

~8 weeks

Case 03 Operations-heavy company · ~120 people Legacy platform rebuild

Turned a fragile admin tool into a system leadership could lean on.

The internal platform had been built quickly five years earlier and never refactored. Every change required a senior engineer. Operations were running workarounds for half a dozen known bugs. Growth was being shaped by what the system could survive, not by what the business needed.

What we built

Refactored the core workflows, tightened the data model, and rebuilt the admin layer the team relies on day to day. Replaced the "ask an engineer" pattern with a reviewer console that operators can run themselves. No new platform. No rip-and-replace.

What changed

Engineer escalations from operations dropped ~70%. Feature lead time on the platform improved noticeably. The team stopped scheduling around the system's limits.

Eng escalations

~70% lower

Feature lead time

Down materially

Known-bug workarounds

Retired

Engagement

~12 weeks

Case 04 Multi-location storage operator · ~XX people AI training program

AI in the operating cadence of a team that does not write code.

Operations-heavy business running multiple physical locations. Leadership saw AI everywhere except inside their own company. The people running the actual work — site managers, customer service, finance, ops leadership — had no starting point. Tooling without onramps.

What we did

A "Getting Started with AI" session for the whole company — common vocabulary, what AI is and is not, where it earns its keep in their kind of work. Then department-by-department workshops shaped around the workflows each team actually owns: site operations, customer communication, finance, leadership reporting. Each workshop ended with one real workflow the team ran the next morning.

What changed

AI moved from a leadership theme to a daily operating tool inside a non-technical workforce. Each department walked out with three to five named workflows in regular use. Leadership got an answer to "are we actually using AI" beyond pilot reports — a list of named workflows owned by the teams that run the business.

Active usage

From zero to daily

Per-team workflows

3–5 shipped

Departments covered

All operating teams

Engineering involvement

None required

If yours looks like one of these

Bring us the workflow. We will tell you what a fix looks like.

The pattern is consistent across all four cases above: a real bottleneck, a small system around it, agents where the work needs judgment, and an exit point the team can run.

What to bring

The workflow that is costing you, the team that runs it, the systems involved, and the number that should move. That is enough to scope.

Scope a project See what we build