Case Studies

What Effektiv delivers.

Four engagements. Each carries the hard numbers we would write into an outcome contract — cost, weeks, AI-authorship percentage, outcome metric. Each shows how EffektAI powered the delivery.

$1.1M

vs $4.2M Big-4 quote

197

live integrations · 0.001% error rate

14 wks

AS/400 to cloud · no rollback

Software Build

SaaS · Resources sector

16-product SaaS platform

$1.1M

delivered

vs $4.2M

Big-4 estimate

73%

AI-written

16

products shipped

Big-4 estimate was $4.2M over three years. Effektiv delivered $1.1M over 18 months. 73% AI-written across 16 products on a shared foundation. The eval rig ran on every pull request — five gates, no exceptions. The full platform, eval rig source code, and authorship trace database transferred to the client on the final day.

How EffektAI powered this

EffektAI's Diagnose Engine mapped the client's existing data architecture before a line of new code was written. The Authorship Trace System logged every AI-generated line. The Eval Rig enforced spec coverage, security scan, and test coverage gates across all 16 products simultaneously.

CASE STUDY VIDEO · COMING Q3 2026

"Client testimonial coming soon. These engagements are anonymised by sector and scale — the numbers, constraints, and technical decisions are real."

SaaS · Resources sector

Built on EffektAI

Every engagement above ran on EffektAI.

EffektAI is not a methodology that lives in a deck. It is a running platform that was active on every pull request, every data migration batch, and every integration deployment in the four engagements above.

When each engagement closed, the platform transferred to the client — eval rig source code, authorship trace database, agent configuration, and runbooks. The numbers above are not marketing claims. They are the outputs of a platform that keeps a verifiable record of every decision it made.

Learn about EffektAI →

Diagnose Engine

Found 23 undocumented pricing rules in the AS/400 codebase. Found 23 unmapped fields in the NetSuite migration. In both cases, the documentation said they didn't exist.

Eval Rig Framework

Ran five gates on every pull request across all four engagements. Zero gate-failing code reached production. The rig configuration transferred to each client at exit.

Agent Mesh

Managed 197 concurrent integration channels in the supply chain engagement. All money-touching writes passed a human gate. Error rate: 4.1% to 0.001%.

Authorship Trace

Every line of AI-generated code in the 16-product SaaS platform is logged with its origin, specification reference, and reviewing engineer. The trace database transferred at exit.

Outcome-priced from day one

Be the next case study.

Show us a scope, a quote you've received, or a project that stalled. We price on outcomes — 40–60% lower than a comparable day-rate engagement — with the EffektAI platform and AU governance behind every line of code.

Benchmark your last project See engagement models →