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
- Weekly Checkpoints
- Multi-Agent Capability
30-Day AI Agent MVP
Fixed scope. Fixed timeline. Fixed price.
The Build Archetype
Productized 4-Week Production-Grade Build. 30-Day AI Agent MVP is a productized 4-week production-grade ai agent mvp build engagement. Riverborn delivers eight named deliverables starting from $5,000 USD. 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.
Studio & Advantage
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 Deloitte Autonomy Ladder positioning.
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 Deloitte Autonomy Ladder positioning analysis (default L1–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.
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 $5,000 USD 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.
LangGraph 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.
Deloitte autonomy ladder positioning analysis for your deployed agent. Default positioning at L1–L2 (recommendation + override patterns). Covers positioning rationale, oversight requirements, exception handling patterns, and progression pathway analysis. L3+ progression routes through bespoke engagement scoping.
Our Methodology
4-Phase Delivery Model with Weekly Milestone Checkpoints + Production-Grade Architectural Framework. Per Gartner, roughly 30%+ of agent projects stall in production attempts (Accenture, 2025reports 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.
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.
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.
Discovery + Architecture
Stakeholder discovery, agent use case scoping, multi-agent orchestration architecture design, and Guardian Agent + Policy-as-Code applicability assessment.
Decision Gate
Build Foundation
Core agent build, multi-agent orchestration foundation deployment, and observability infrastructure foundation.
Decision Gate
Integration + Testing
Integration deployment, audit trail generation, and sub-200ms p95 SLA validation.
Decision Gate
Production Deployment + Handoff
Production deployment, KPI baseline measurement framework, handoff documentation delivery, and final deliverable presentation.
Decision Gate
Engagement Process
4-Phase Structure with Weekly Milestone Checkpoints. Four phases. Phase decision gates at each 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.
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
Phase 2: Build Foundation
Core agent build. Multi-agent orchestration foundation deployment (LangGraph state machine framework). Observability infrastructure foundation.
Decision Gate
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
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
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 10–15 hours per MVP week, heaviest in Phase 1 (architecture review) and Phase 4 (handoff training).
Single production-grade agent is the starts from $5,000 USD 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 $5,000 USD floor at extended timeline.
Buyer Pathways
Pre-MVP Pathway + Post-MVP Pathway. 30-Day AI Agent MVP serves multiple buyer pathways. After MVP deployment, scope expansion routes by finding type.
Entry Pathways
Startup Direct Entry
Startups commissioning production-grade AI agent build enter MVP directly.
Post-Sprint Expansion
Post-Sprint buyers route via the AI Integration Sprint build expansion pathway.
AI Readiness Audit Routing
AI Readiness Audit findings route to MVP when audit identifies an MVP-scope opportunity.
AI Strategy Workshop Routing
AI Strategy Workshop findings route to MVP when the workshop identifies production-grade agent build as the priority deployment after executive alignment.
Expansion Pathways
Multi-Agent Orchestration MVP Expansion
Routes to scoped expansion above $5,000 USD floor at extended timeline.
Autonomy Ladder L3+ Progression
Routes through bespoke engagement scoping. L3+ autonomy positioning requires extended Guardian Agent + Policy-as-Code governance work and is appropriately sized as a bespoke engagement.
L3+ autonomy positioning requires extended Guardian Agent + Policy-as-Code governance work and is appropriately sized as a bespoke engagement.
Why Riverborn
Why Riverborn for 30-Day AI Agent MVP. Production-grade AI agent build. Not pilots. Not demos.
4-phase delivery + production-grade architecture
Phase 1 through Phase 4 with 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, Deloitte Autonomy Ladder at default L1–L2, sub-200ms p95 latency SLA.
Eight specific deliverable artifacts
Named deliverables 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, starts from $5,000 USD.
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
4-week delivery, starting from $5,000 USD — fits startup investor and board cycles.
Production-grade startup ai agent build typically requires 6–12 months at enterprise consultancy timelines. MVP delivers in 4 weeks starting from $5,000 USD, on a timeline that fits startup investor and board cycles. Riverborn’s shipped portfolio: Dhoni AI at 100K+ voice interactions in production, Jachai AI, Cheklist.ai, InvoiceAgent, and Rachona AI. 4+ years, 10+ AI engineers, 4.8+ average rating. Bangladesh delivery model produces 40–60% cost reduction vs US/EU agencies at identical production-grade benchmarks.
Adjacent Productized Offers
Three adjacent-in-pathway offers route from MVP.
Business Types, Industries, and Departments We Deploy 30-Day AI Agent MVPs Across
Startups
MVP is Riverborn's canonical entry point for production-grade AI agent builds at startup velocity.
Series A–C.
Growth-stage production-grade agent builds between startup direct entry and mid-market Sprint expansion context.
SaaS & Technology.
In-product AI agent builds, customer success multi-agent orchestration, and AIOps deployments.
E-commerce & Retail.
Conversational commerce agents, inventory optimization agents, and creative production multi-agent orchestration.
Customer Support & CX.
Agent-based support deployment, ticket triage multi-agent orchestration, and conversational support agent build.
Sales.
Lead qualification, opportunity scoring multi-agent orchestration, and sales handoff agent build.
Marketing.
Content production multi-agent orchestration and campaign orchestration agent builds, informed by Rachona AI multi-format content production experience.
Ready to scope your 30-Day AI Agent MVP?
Book a discovery call. Free 30-minute call. We confirm MVP scope (single agent or multi-agent orchestration expansion), agent use case, and delivery timeline together.
Free · 30 minutes
Book a Discovery Call
Direct conversation about your agent use case, integration context, and delivery timeline with Riverborn’s engineering team.
Book a discovery callFixed scope · starts from $5,000 USD · 4 weeks
30-Day AI Agent MVP
Fixed scope. Fixed price. Fixed timeline. Eight named deliverables. Production-grade AI agent build with multi-agent orchestration capability.
Frequently Asked Questions
Eight named deliverables: 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. Fixed 4-week timeline, starts from $5,000 USD, 4-phase delivery with weekly milestone checkpoints.
Starts from $5,000 USD for single production-grade agent build at 4-week delivery. Multi-agent orchestration MVP expansion is scoped above $5,000 USD floor at 5–6 week extended timeline. Riverborn's Bangladesh delivery model produces 40–60% cost reduction vs US/EU agencies at identical production-grade benchmarks.
MVP does not include autonomy ladder L3+ positioning (routes to bespoke engagement scoping), single-workflow overlay in 4–6 weeks (routes to AI Integration Sprint), pre-MVP capability assessment (routes to AI Readiness Audit), or executive strategy alignment (routes to AI Strategy Workshop). MVP scope is production-grade agent build at default L1–L2 autonomy positioning.
Your engineering team has full deployment ownership via handoff documentation. Multi-agent orchestration expansion routes to scoped expansion above $5,000 USD floor at 5–6 week extended timeline. Autonomy ladder L3+ progression routes through bespoke engagement scoping.
Productization fixes engagement structure, not engagement substance. Each artifact is tailored to your specific agent use case, IT stack, and compliance requirements within fixed 4-week scope at starts from $5,000 USD. No scope-creep surprises, no open-ended discovery cycles, no budget anxiety.
Sprint is single-workflow overlay in 4–6 weeks starting from $5,000 USD for first AI deployment on an existing IT stack. MVP is production-grade agent build in 4 weeks starting from $5,000 USD for multi-workflow integration, autonomy ladder progression, and multi-agent orchestration. Sprint validates the deployment pattern. MVP delivers production-grade agent build at full scope.
Yes. MVP supports overlay integration (where you have established CRM, ERP, HRIS, helpdesk, or observability platforms) and greenfield startup ai agent build contexts (no established stack). Both deployment contexts fit within the 4-week productized scope.
Default autonomy positioning is Deloitte L1–L2 (recommendation + override patterns), appropriate for production-grade agent build. Deliverable 8 documents positioning rationale, oversight requirements, and exception handling patterns. Higher ai agent autonomy positioning at L3+ routes through bespoke engagement scoping.