The Delivery Model
Dedicated pods. More AI. Fewer humans. Better outcomes.
Most service providers staff 8–12 humans and sprinkle in AI tools. We staff 7–8 AI agents and 2–3 human wizards who lead, govern, and own the outcome. The humans are in charge. The AI agents are the workforce. The result is a pod that delivers at a speed and cost structure traditional teams can't match.
The result: 60–70% lower cost, 3–5x faster delivery, and quality that improves with every engagement.
The model everyone else is running — and why it doesn't scale
Traditional delivery puts humans at the center and AI at the margins. We put AI agents at the center and humans at the governance layer. The difference isn't incremental — it's structural.
Traditional: AI-Assisted Human Teams
Typical 12–18 month enterprise platform program, blended onshore/offshore
Same pyramid. Same cost structure. AI makes it 10–30% faster at best.
Tvameva: Human-Led Pod with AI Workforce
Same scope, same platform complexity — fundamentally different economics
Different architecture. Different economics. Measurably better outcomes.
Side-by-side: same scope, different model
Inside the AI Pod
Each pod is a team of specialized AI agents — purpose-built for their role, operating continuously, and improving with every engagement. A small human governance team reviews critical decisions and owns client outcomes.
AI Agent Layer
The primary workforce — 7 specialized agents
Product Strategy Agent
Backlog prioritization, sprint planning, release forecasting, risk detection, dependency mapping
Continuously analyzes client outcomes, market signals, and delivery velocity to optimize the roadmap
Solution Architecture Agent
Platform design, integration architecture, design validation, technical documentation
Evaluates architecture decisions against patterns, generates integration specs, validates designs before human review
Engineering Agent
Code generation, implementation, automated testing, CI/CD pipeline management, deployment
Writes production code, generates test suites, handles migrations, manages build pipelines — the primary development layer
QA & Validation Agent
Automated regression testing, performance testing, accessibility audits, security scanning
Runs continuous validation against every change — smoke tests, Web Vitals, Lighthouse scores, cross-browser compatibility
Program Management Agent
Delivery tracking, status synthesis, milestone monitoring, escalation detection, automated reporting
Generates real-time delivery dashboards, flags risks before they become blockers, produces stakeholder updates
Content & Publishing Agent
Content creation, CMS publishing, brand consistency enforcement, guardrail compliance
Drafts solution content, publishes to Drupal CMS, validates against brand voice and content guardrails
Security & Compliance Agent
Continuous vulnerability scanning, dependency auditing, OWASP compliance, secrets detection, supply chain security, penetration testing automation
Monitors every code change for security vulnerabilities, audits dependencies against CVE databases, enforces security policies, scans for exposed secrets, and validates compliance posture — continuously, not quarterly
Customer Success Lead
Client relationship, outcome alignment, strategic guidance, escalation resolution, expansion planning
Governs agent outputs, approves critical decisions, owns client communication and trust
Customer Success Engineer
Architecture sign-off, complex integration decisions, production deployment approval, quality governance
Reviews agent-generated code and architecture for edge cases, security, and strategic alignment
The numbers behind the model
Not projections. Not benchmarks from analyst reports. These are the economics we see in production — across our own operations and our client engagements.
Economic value unlockable by AI agents by 2030
McKinsey
Of companies already running AI agents in production
G2 Enterprise AI Report
Median cost-per-unit reduction with mature agent workflows
G2 Data
The accelerator arsenal
Our AI pods don't start from scratch. They operate with field-tested frameworks, pre-built patterns, and production-proven tooling that compound across engagements. Every deployment makes the next one faster.
Platform Connectors
Pre-built integrations between enterprise platforms — Acquia-Algolia, GCP-Salesforce, Threekit-CPQ. Eliminates 4–8 weeks of custom build per connector.
Agentic Pipeline Templates
Reusable multi-agent workflow patterns for common delivery scenarios — content publishing, data pipeline orchestration, proposal generation.
Automated Test Suites
Pre-configured Playwright test batteries covering smoke, content validation, integration, performance (Web Vitals), and accessibility — deployed on every engagement.
Content Model Scaffolding
Drupal content type definitions, field mappings, JSON:API configuration, and seed scripts that stand up a headless CMS in hours, not weeks.
GCP Analytics Starter Kit
BigQuery schema templates, Composer DAG patterns, dbt model scaffolding, and Looker block library — 8–12 weeks of foundational build eliminated.
Institutional Memory System
Every engagement feeds the knowledge base — architecture decisions, content patterns, client feedback, competitive intelligence. The pod gets smarter with each deployment.
Security-First Delivery
Security isn't a phase. It's every phase.
When AI agents write code, review architecture, and deploy to production, security can't be an afterthought or a quarterly audit. Our Security & Compliance Agent operates continuously — scanning every commit, auditing every dependency, and enforcing security policies in real time. Humans review critical findings and govern the security posture.
Continuous Vulnerability Scanning
Every code change is scanned for OWASP Top 10 vulnerabilities, injection risks, XSS vectors, and insecure patterns — before it reaches a branch, not after it reaches production.
Supply Chain Security
Every dependency is audited against CVE databases in real time. No vulnerable package enters the build. License compliance is enforced automatically.
Secrets & Credential Protection
Automated detection of exposed API keys, tokens, passwords, and credentials across code, configs, and environment files. Hard-blocked from commits.
Security Policy Enforcement
Content guardrails prevent exposure of client names and sensitive data. CORS, CSP, and trusted host policies are validated on every deployment.
Penetration Testing Automation
Automated security testing against deployed endpoints — authentication bypass, privilege escalation, injection, and API abuse scenarios run on every release.
Compliance & Audit Readiness
Continuous compliance posture monitoring against SOC 2, GDPR, and CCPA requirements. Audit trail of every agent action, human approval, and production change.
The AI security advantage: Traditional security is periodic — quarterly pen tests, annual audits, reactive patching. Our Security Agent operates on every commit, every dependency update, every deployment. The attack surface is monitored continuously, not intermittently. When new CVEs are published, our agent evaluates exposure within minutes — not when a consultant gets to it next quarter.
Case Study: Practicing What We Preach
How we built tvameva.ai — with our own AI Pod
This website is not a marketing artifact built by an agency. It is a production application built and deployed by the same AI pod model we deliver to clients — and it serves as a live proof point of the approach.
The founders directed the work. AI agents handled everything else: solution content creation across 3 solution areas, front-end design and engineering, headless CMS integration, test automation, and cloud deployment.
Built, tested, and deployed — including 3 enriched solution area pages with 15+ sections each
Solution areas, differentiators, proof points, case studies, and pod roles — all CMS-driven via Drupal JSON:API
Smoke tests, page content validation, integration checks, and Web Vitals performance testing via Playwright
Directing strategy, reviewing outputs, and governing quality — while agents built, tested, and deployed
“The AI pod model isn't a pitch deck concept. It's how we operate. Every page you're reading was created by AI agents, reviewed by a human, and published through the same agentic pipeline we deploy for clients.”
Outcome-based pricing
When AI agents do the volume work, the cost structure changes fundamentally. We pass that efficiency to you — and tie our revenue to your results, not our inputs.
You pay for
- Pipeline velocity and proposal cycle time reduction
- Engagement uplift and conversion improvement
- Platform migration milestones met on schedule
- Dashboard adoption rate and time-to-insight reduction
- AI resolution rate and cost-per-ticket reduction
Not for
- ✕Agent or platform seat licenses
- ✕Hours logged by humans or AI
- ✕Headcount on a project roster
- ✕Document volume or export counts
- ✕Software subscription tiers
See the AI Pod model in action
We'll walk you through a live engagement — how agents handle the work, how humans govern quality, and what the economics look like for your use case.
Book a 30-Minute Demo