Jeremy Allen
Chief Technology Officer, Contra
EXECUTIVE BUSINESS CASE
Accelerating Contra's Engineering Velocity with AI-Native Development
Oborna embeds AI directly into the development workflow to help Contra's engineering team ship faster across your TypeScript-first, GraphQL-centric platform. We recommend a phased rollout with measurable productivity gains expected within 90 days.
64%
of the Fortune 500
50,000+
organizations using Oborna
100M+
lines of enterprise code processed daily
Strategic Problem Framing
Engineering throughput needs to increase 25–40% to support Contra's next phase of growth. Current developer velocity is constrained by four compounding bottlenecks, each one measurable and each one addressable.
30–40% OF DEV TIME LOST
Boilerplate & cross-service overhead
Engineers spend an estimated 30–40% of their time on boilerplate, schema propagation, and cross-service consistency across Contra's TypeScript monorepo. That's time that doesn't ship features.
25–40% LONGER REVIEW CYCLES
Type safety maintenance at scale
Maintaining zero type drift across the full stack (Drizzle → Zod → GraphQL → React) extends code review cycles by an estimated 25–40%, consuming senior engineer bandwidth on validation instead of architecture.
3–6 MONTHS TO FULL PRODUCTIVITY
New hire ramp time
New engineers navigating Contra's interconnected monorepo (GraphQL subscriptions, AMQP workers, multi-portal auth) take 3–6 months before contributing meaningfully. Every new hire represents months of under-capacity.
~2 HRS/DEV/DAY IN LOST VELOCITY
Competitive velocity gap
Industry benchmarks show AI-augmented teams reclaim ~2 hours per developer per day. Every day Contra operates without these tools, competitors with AI-native workflows widen their shipping advantage.
The Result: Every quarter of inaction means another cycle of delayed releases, another cohort of new hires ramping slowly, and another set of competitors shipping with AI-augmented velocity. At Contra's current engineering scale, even a 15% throughput gap costs hundreds of engineering hours per quarter. Those hours go to boilerplate and rework instead of marketplace growth, payments expansion, and the features that drive retention. The longer the gap persists, the more expensive it becomes to close.
Recommended Approach
We recommend a phased rollout of Oborna Enterprise tailored to Contra's engineering organization. Each phase builds on the previous, creating a clear line from pilot results to team-wide adoption.
PHASE 1
Pilot Team Deployment
Weeks 1–4
Deploy Oborna to a core platform team working across Contra's TypeScript/GraphQL stack. Configure SSO, privacy mode, and model governance. Establish baseline metrics for PR volume, cycle time, and developer satisfaction. Oborna's codebase-aware AI will immediately begin providing contextual suggestions across your monorepo.
PHASE 2
Measured Expansion
Weeks 5–10
Extend access to frontend, backend, and infrastructure teams. Implement .obornarules to enforce Contra's coding standards, including type safety patterns, GraphQL schema conventions, and testing requirements. Activate background agents for automated PR workflows and Zod/Drizzle schema propagation.
PHASE 3
Organization-Wide Rollout
Weeks 11–16
Full deployment across Contra's engineering organization with SCIM provisioning. Enable Bugbot for automated security and quality scans on all PRs. Establish analytics dashboards for ongoing ROI tracking and spend governance across all teams.
Target Outcomes
Based on enterprise adoption benchmarks and independent productivity studies, calibrated for Contra's TypeScript-heavy, full-stack environment, we project the following measurable improvements within the first 90 days of full deployment.
Metric
Current State
Target State
PR Volume (per developer/week)
4–5 PRs
10–15 PRs (+100%)
Time on Boilerplate & Schema Propagation
30–40% of dev time
10–15% of dev time (60–75% reduction)
New Hire Ramp Time
3–6 months to full productivity
2–3 months (codebase-aware AI accelerates onboarding)
Developer Satisfaction (Tooling)
Baseline (no AI-native tooling)
93% preference rate in head-to-head evaluations
Required Investment
Contra's Commitments
- Designate an internal champion and a pilot team working on the core marketplace or payments stack for the initial 4-week evaluation
- Provide IT support for SSO/SAML integration, SCIM provisioning, and network configuration aligned with Contra's Kubernetes/Argo deployment environment
- Commit to a structured evaluation framework: baseline metrics before rollout and bi-weekly check-ins during pilot
Oborna's Commitments
- Dedicated enterprise onboarding with premium support, deployment guidance, and .obornarules configuration tuned to Contra's TypeScript/GraphQL conventions
- Full SOC 2 Type II compliance, zero data retention with AI providers, AES-256 encryption at rest, and TLS in transit, ensuring Contra's proprietary code and user data are never exposed
- Analytics dashboards, AI code tracking, and Oborna Blame for ongoing governance, spend management, and ROI measurement across all teams
Recommendation
Contra's TypeScript-first platform serves professionals across 58 countries. Scaling it further without proportional headcount growth requires AI-augmented tooling.
We recommend a phased pilot with a single core team, measured against clear baselines. Expect a 25–40% increase in PR throughput and ~2 hours saved per developer per day. Every quarter without action widens the gap.
We recommend a phased pilot with a single core team, measured against clear baselines. Expect a 25–40% increase in PR throughput and ~2 hours saved per developer per day. Every quarter without action widens the gap.
Prepared for Contra. Contact Alex Chen for more information.
