30-Day AI Agent MVP
Riverborn’s productized 4-week production-grade AI agent build with multi-agent orchestration capability and autonomy ladder positioning. Starts from $5,000 USD. Eight deliverable artifacts: production deployed AI agent, integration documentation, audit trail and observability infrastructure, code repository with deployment runbooks, KPI baseline measurement report, handoff documentation, multi-agent orchestration architecture documentation, and autonomy ladder positioning report.
Free 30-minute call. We confirm MVP scope, agent use case, and delivery timeline together.
- 4 Weeks Fixed Scope
- Starts from $5,000 USD
- 8 Named Deliverables
- 4-Phase Delivery with Weekly Milestone Checkpoints
30-Day AI Agent MVP is a productized 4-week production-grade AI agent build engagement. Riverborn delivers eight named deliverables at $5,000 starting. The engagement serves startups commissioning production-grade AI fast and growth-stage or mid-market companies post-Sprint scaling to multi-workflow integration, autonomy ladder progression, or multi-agent orchestration scope.
30-Day AI Agent MVP is one of Riverborn’s 5 productized offers: fixed scope, fixed timeline, fixed price. Productized engagement removes scope uncertainty, eliminates open-ended discovery cycles, and gives you deliverable depth on a known timeline at a known price.
Riverborn is an AI System Development Studio: 10+ AI ENGINEERS, 10+ shipped AI products, 100K+ users globally. The productized AI agent development methodology is built for startup velocity expectations: 4-phase delivery model with weekly milestone checkpoints, LangGraph multi-agent orchestration, Guardian Agent + Policy-as-Code governance, and Riverborn’s Autonomy Ladder positioning.
Productization is the benefit. Riverborn has shipped 10+ AI products and runs hundreds of architectural patterns across the portfolio. Productizing the most-repeatable engagement types into 5 fixed-scope offers gives you the same deliverable depth without the bespoke-engagement cost or timeline overhead.
What’s Included
Production-deployed AI agent in your environment.
Sub-200ms p95 latency SLA validated. Single-agent default with multi-agent orchestration expansion available.
Integration documentation.
Overlay (existing IT stack) OR greenfield integration based on your deployment context, documented per integration point.
Audit trail and observability infrastructure.
Logging, metrics, traces, alerting, and audit trail generation for AI agent decisions.
Code repository with deployment runbooks + KPI baseline measurement report.
GitHub or GitLab repository handed off to your engineering team with full ownership transfer.
Handoff documentation for your engineering team.
Training materials, operational runbooks, troubleshooting playbooks, on-call escalation framework.
Multi-agent orchestration architecture documentation + autonomy ladder positioning report.
LangGraph state machine framework architecture and Riverborn's Autonomy Ladder positioning analysis (default L1 to L2).
Productized scope ≠ cookie-cutter delivery. 30-Day AI Agent MVP deliverables are tailored to your specific workflow and IT stack within the fixed scope. Productization fixes the engagement structure, not the engagement substance.
Methodology: 4-Phase Delivery Model with Weekly Milestone Checkpoints
Gartner estimates roughly 30%+ of agent projects stall before reaching production. Separately, Accenture (2025) reports 44% of organizations have already introduced agentic AI. The gap between introducing AI and shipping it to production is where the MVP’s engagement architecture matters.
The 4-week AI agent build runs across four phases, each one week long.
Phase 1: Discovery + Architecture
Stakeholder discovery, agent use case scoping, multi-agent orchestration architecture design, and Guardian Agent + Policy-as-Code applicability assessment.
Phase 2: Build Foundation
Core agent build, multi-agent orchestration foundation deployment, and observability infrastructure foundation.
Phase 3: Integration + Testing
Integration deployment, audit trail generation, and sub-200ms p95 SLA validation.
Phase 4: Production Deployment + Handoff
Production deployment, KPI baseline measurement framework, handoff documentation delivery, and final deliverable presentation.
Phase decision gates sit at each phase boundary. Your engineering team signs off on deliverables before the next phase begins. Weekly milestone checkpoints within each phase give you finer-grained progress visibility for investor or board reporting cycles.
Production-grade architectural framework distinguishes MVP from pilot or proof-of-concept engagements. Multi-agent orchestration deploys via LangGraph state machine framework for supervisor-worker patterns or multi-step workflow agent architectures. Guardian Agent + Policy-as-Code governance covers policy decisions where your compliance posture requires it.
Riverborn’s Autonomy Ladder positioning defaults to L1 to L2 (recommendation + override patterns). L3+ progression routes through bespoke engagement scoping. Sub-200ms p95 latency SLA is validated against production load testing. HIPAA-compliant and PCI-DSS-compliant deployment patterns apply where your compliance posture requires.
ⓘHIPAA and PCI-DSS framing is architectural alignment, not corporate certification. Riverborn is not corporately certified under HIPAA or PCI-DSS. Your attorney remains the system of record for regulatory interpretation.
Eight Deliverable Artifacts
You get eight specific artifacts. Each is tailored to your agent use case, IT stack context (overlay or greenfield), and compliance requirements within the productized 4-week scope.
Deployed AI agent in your environment.
Production-grade AI agent operating in your production environment. Sub-200ms p95 latency SLA validated against production load testing. Single-agent architecture as productized default. Multi-agent orchestration MVP expansion is available as scoped expansion above the $5,000 floor.
Integration documentation.
Overlay integration architecture where you have established CRM, ERP, HRIS, helpdesk, or observability platforms. Or greenfield integration architecture where startup context has no established stack constraints.
Audit trail and observability infrastructure.
Production observability configured against your existing stack (Datadog, Splunk, New Relic, Grafana, or ELK) or deployed greenfield where startup has no established observability. Includes audit trail generation for AI agent decisions.
Code repository with deployment runbooks.
GitHub or GitLab repository with deployment runbooks, monitoring playbooks, and architectural documentation. Handed off to your engineering team at MVP completion with full ownership transfer. Not a Riverborn-retained code base.
KPI baseline measurement report.
Pre-MVP baseline metrics where measurable. Post-MVP measurement framework and KPI improvement reporting framework for your investor or board cycle reporting.
Handoff documentation for your engineering team.
Training materials, operational runbooks, troubleshooting playbooks, and on-call escalation framework. Enables your engineering team to maintain deployment post-MVP without ongoing Riverborn engagement dependency.
Multi-agent orchestration architecture documentation.
Google ADK multi-agent architecture covering supervisor-worker patterns and multi-step workflow agent architectures. Agent role definitions, agent communication patterns, agent escalation patterns, and agent state management patterns. MVP-specific deliverable distinguishing MVP scope from Sprint scope.
Autonomy ladder positioning report.
Autonomy ladder positioning analysis for your deployed AI agent. Default positioning at L1 to L2 (recommendation + override patterns). Covers positioning rationale, oversight requirements, exception handling patterns, and progression pathway analysis. L3+ progression routes through bespoke engagement scoping. MVP-specific deliverable.
Engagement Process: 4-Phase Structure with Weekly Milestone Checkpoints
Phase 1: Discovery + Architecture
Stakeholder discovery interviews. Agent use case scoping. Multi-agent orchestration architecture design. Guardian Agent + Policy-as-Code applicability assessment. Autonomy ladder positioning analysis.
Decision gate: architecture sign-off.
Phase 2: Build Foundation
Core agent build. Multi-agent orchestration foundation deployment (LangGraph state machine framework). Observability infrastructure foundation.
Decision gate: foundation build sign-off.
Phase 3: Integration + Testing
Integration deployment (overlay or greenfield). Audit trail generation configuration. Sub-200ms p95 SLA validation against production load testing. Guardian Agent governance configuration.
Decision gate: integration testing sign-off.
Phase 4: Production Deployment + Handoff
Production deployment. KPI baseline measurement framework. Multi-agent orchestration architecture documentation and autonomy ladder positioning report. Handoff documentation delivery. Final deliverable presentation.
Decision gate: full deployment ownership transferred to your engineering team.
Build archetype MVP is collaborative and requires your engineering team’s availability for architecture design review, integration testing, deployment validation, and handoff training. Typical commitment is 10 to 15 hours per MVP week, heaviest in Phase 1 (architecture review) and Phase 4 (handoff training).
Single production-grade agent is the $5,000 starting productized default at 4-week delivery. Multi-agent orchestration MVP expansion (multiple agents requiring supervisor-worker patterns, multi-step workflow agent architectures, or cross-domain agent orchestration) is available as scoped expansion above the $5,000 floor at extended 5 to 6 week timeline.
Pre-MVP Pathway + Post-MVP Pathway
30-Day AI Agent MVP serves multiple buyer pathways. Startups commissioning production-grade AI agent build enter MVP directly. Post-Sprint buyers route via the AI Integration Sprint build expansion pathway. AI Readiness Audit findings route to MVP when audit identifies an MVP-scope opportunity. AI Strategy Workshop findings route to MVP when the workshop identifies production-grade agent build as the priority deployment after executive alignment.
After MVP deployment, scope expansion routes by finding type. Multi-agent orchestration MVP expansion routes to scoped expansion above the $5,000 floor at extended 5 to 6 week timeline. Autonomy ladder L3+ progression routes through bespoke engagement scoping, since it requires extended Guardian Agent + Policy-as-Code governance work and is appropriately sized as a bespoke engagement.
Why Riverborn for 30-Day AI Agent MVP
4-phase delivery model with weekly milestone checkpoints plus production-grade architectural framework.
Phase 1 Discovery + Architecture through Phase 4 Production Deployment + Handoff, with weekly checkpoints for investor and board cycle visibility. Production-grade architecture: LangGraph multi-agent orchestration, Guardian Agent + Policy-as-Code governance, Riverborn's Autonomy Ladder at default L1 to L2, sub-200ms p95 latency SLA.
Eight specific deliverable artifacts including multi-agent orchestration architecture and autonomy ladder positioning report.
Production-deployed AI agent, integration documentation, audit trail and observability infrastructure, code repository, KPI baseline report, handoff documentation, multi-agent orchestration architecture documentation, autonomy ladder positioning report. Predetermined deliverables, fixed 4-week timeline, $5,000 starting.
Multi-pathway convergence: startup direct entry, Sprint build expansion, Audit findings routing, Workshop findings routing.
MVP serves as the production-grade agent build convergence point across multiple buyer pathways: startup primary entry, Sprint expansion to multi-workflow or multi-agent scope, Audit findings routing, and Workshop findings routing after executive alignment.
Startup velocity specialization plus shipped production AI agent portfolio.
Production-grade startup AI agent build typically requires 6 to 12 months at enterprise consultancy timelines. MVP delivers in 4 weeks at $5,000 starting, on a timeline that fits startup investor and board cycles.
Riverborn's shipped portfolio informs the methodology: Dhoni AI at 100K+ voice interactions in production, Jachai AI for compliance training, Nothi AI for AP/AR automation, and Rachona AI for multi-format content production. 4+ years, 10+ AI ENGINEERS, 4.8+ average rating.
Bangladesh delivery model produces 40 to 60% cost reduction vs US/EU agencies at identical production-grade benchmarks. MVP at $5,000 starting, 4-week fixed scope. Multi-agent orchestration expansion scoped above the $5,000 floor at 5 to 6 week extended timeline.
Adjacent Productized Offers
Three adjacent-in-pathway offers route from MVP:
Business Types, Industries, and Departments We Deploy 30-Day AI Agent MVPs Across
Frequently Asked Questions
Eight named deliverables: production-deployed AI agent, integration documentation, audit trail and observability infrastructure, code repository, KPI baseline report, handoff documentation, multi-agent orchestration architecture documentation, and autonomy ladder positioning report. Fixed 4-week timeline, $5,000 starting.
$5,000 starting for single production-grade agent build at 4-week delivery. Multi-agent orchestration expansion is scoped above the $5,000 floor at 5 to 6 week extended timeline. Riverborn's Bangladesh delivery model produces 40 to 60% cost reduction vs US/EU agencies.
MVP does not include autonomy ladder L3+ positioning (routes to bespoke scoping), single-workflow overlay in 4 to 6 weeks (routes to AI Integration Sprint), pre-MVP capability assessment (routes to AI Readiness Audit), or executive strategy alignment (routes to AI Strategy Workshop).
Your engineering team has full deployment ownership via handoff documentation. Multi-agent orchestration expansion routes to scoped expansion above the $5,000 floor at 5 to 6 week extended timeline. Autonomy ladder L3+ progression routes through bespoke scoping.
Productization fixes engagement structure, not substance. Each artifact is tailored to your specific agent use case, IT stack, and compliance requirements within fixed 4-week scope at $5,000 starting. No scope-creep surprises, no open-ended discovery cycles.
Sprint is single-workflow overlay in 4 to 6 weeks at $5,000 entry for first AI deployment on an existing IT stack. MVP is production-grade agent build in 4 weeks at $5,000 starting for multi-workflow integration and multi-agent orchestration. Sprint validates the pattern; MVP delivers full scope.
Yes. MVP supports overlay integration (where you have established CRM, ERP, HRIS, helpdesk, or observability platforms) and greenfield contexts (no established stack). Both fit within the 4-week productized scope.
Default autonomy positioning is L1 to L2 (recommendation + override patterns), appropriate for production-grade agent build. Deliverable 8 documents positioning rationale, oversight requirements, and exception handling patterns. Higher positioning at L3+ routes through bespoke scoping.