Solution · 1 of 4

AI-Powered PDLC

Cut time to production in half. AI agents alongside your engineering teams — not replacing them. Knowledge stays permanently.

CIOAll verticals8–16 week deliveryOutcome-fee model

Avg time to production

16mo

Enterprise median before AbarVa

After AbarVa

8mo

50% reduction — Genome-validated

Consulting reduction

$18M

Avg annual per engagement

Knowledge retained

100%

Stays inside the org permanently

Three phases

1

Diagnose the delivery bottleneck

AbarVa maps every handoff, every meeting displacing building, every vendor dependency without internal capability. In 48 hours you know exactly where your delivery cycle is leaking and which interventions recover the most time.

SituationData Intelligence

2

Embed AI into the build cycle

Maestros embed inside your engineering squads. AI agents handle scaffolding, testing, documentation, and review — the parts that slow humans down. Engineers build what requires judgment. Output doubles.

StrategyVendor

3

Verify improvement · earn the fee

Baseline locked Day 0: cycle time, output per engineer, consulting spend. Monthly actuals tracked. Fee on verified improvement only. If time to production does not drop, we do not get paid.

Business CaseOutcomes

Genome patterns

72%

Vendor dependency without internal capability

Teams cannot verify or recover when the vendor fails

61%

Change management gap

Technology works — adoption fails. Engineers revert.

79%

No MLOps infrastructure

AI cannot reach production without deployment rails

Deliverables

Delivery bottleneck map — every handoff quantified

AI agent integration playbook — squad-level

Vendor selection scored against your stack

Baseline + monthly tracking + fee on verified cycle time reduction

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