AI Development for Startups (Pre-seed to Seed)
AI development for startupsis rapid, investor-ready AI product building for pre-seed to seed-stage companies. Done well, it's a different discipline from enterprise AI work — faster cycles, capital-efficient architecture, production from day one.
Free 30-minute call. We confirm scope, timeline, and team fit on the call.
Riverborn builds production-grade AI MVPs for founders: architecture designed to scale when the next round lands, not to be rewritten. We run the same stack across our own 10+ live AI products — including VoiceIQ (the enterprise evolution of vocalo.ai), with 100K+ live voice interactions in production.
Our default engagement is the 90-Day AI Agent MVP: $25,000, 12 weeks, a production-grade AI agent delivered. AI MVP development at this stage runs on different unit economics than the enterprise version — this page is for you if you're building the first one.
Pricing. Projects start at $25,000. The 90-Day AI Agent MVP is the recommended entry point.
Market Opportunity
The market window. 44% of organizations have already introduced agentic AI, and the share of autonomy-operating enterprises is projected to grow from 45% today to 74% within five years (Accenture Agentic Enterprise 2028 Report). The window for AI-native startups is now, not in twelve months.
| Field | Detail |
|---|---|
| Stage | Pre-seed to seed ($0.5M–$5M raised, or bootstrapped pre-raise) |
| Team size | 1–15 people |
| Product stage | Pre-product through first paid customer |
| Engagement | 90-Day AI Agent MVP (fixed scope, $25,000) — the default entry point |
| Decision-maker | Technical co-founder, solo founder, or CTO hire #1 |
What Pre-seed and Seed Startups Build with Riverborn
Founders at this stage need a small set of build patterns that are well understood, productionable in 12 weeks, and architected to survive into a Series A round. Below are the patterns we deploy most often for pre-seed and seed-stage teams.
AI-native product MVP.
Founders with an AI product thesis need to ship an investor-ready demo with real users in 90 days. The architecture is a single LLM-backed agent with tool-calling, retrieval, and a working evaluation loop. At delivery: a deployed product with onboarding flow, instrumented for usage tracking. We cover MVP scoping and handover discipline in our AI MVP development service.
Agent-based internal workflow as operational edge.
Seed-stage founders need to automate a core operational workflow as a differentiator, not hire it. The architecture is agent orchestration — built on agent orchestration frameworks for startup MVPs like LangChain, CrewAI, or LangGraph — wired into custom tool integrations. Delivery: a production agent connected to your existing stack with monitoring and structured outputs.
Voice- or chat-based conversational product.
Founders building conversational products need voice and text on a unified agent layer, not bolted together. Architecture: a single agent with sub-second latency targets, session memory, and guardrails. Delivery: a working conversational interface with documented fallback paths.
RAG-backed domain assistant.
Founders with a proprietary knowledge base need a grounded assistant, not a ChatGPT wrapper. Architecture: a retrieval pipeline over a named vector database (Pinecone, Weaviate, or pgvector) with reranking and source attribution. Delivery: a grounded assistant with documented retrieval logic and a runnable evaluation suite.
Embedded AI feature for an existing SaaS product.
Founders with a product already in market need an AI feature that becomes competitive moat. The architecture is the AI capability with its own evaluation harness and cost controls. Delivery: an embedded feature with usage analytics — covered in our generative AI feature development service.
Engagement Model for Pre-seed and Seed Startups
Our primary engagement for seed-stage startups is the 90-Day AI Agent MVP: a fixed-scope, $25,000, 12-week build that delivers a production-grade AI agent — deployed, tested, monitored, with documentation. The week-by-week breakdown is straightforward. Read the full 90-Day AI Agent MVP package for the deliverables list.
Discovery & Architecture
Use-case validation, agent design, data and integration scoping.
Build
Agent implementation, tool integrations, prompt and evaluation framework.
Integration
Connection to your existing stack, monitoring, observability.
Launch
Production deployment, documentation, handover.
Fixed scope matters at this stage for one reason: founders need predictable runway allocation. A fixed-scope engagement protects you from scope creep, protects us from diluted delivery focus, and gives you a single line item your board and investors can evaluate on delivery date. Proposal-based "custom engagement" models that dominate AI dev firms are the wrong fit at the seed stage. They convert founder time into negotiation cycles instead of shipping cycles, and they make the next investor update harder to write because the number you committed to keeps moving. Pre-seed AI development needs a clock, a price, and a deliverable list — not a Statement of Work draft.
After 90 days the agent is yours: code in your repository, infrastructure in your accounts, documentation in your wiki. Continuation is optional. Many founders extend into post-launch evolution or Series A prep builds — those engagements are covered on our growth-stage AI engagements page. We treat continuation as a decision you make after delivery, not a commitment you make at signing. Founders who extend typically do so 4–8 weeks post-launch, once usage data has surfaced the highest-leverage iteration paths. Founders who don't extend take the agent forward with their next engineering hire and never look back. Either path is a valid outcome.
Our Bangladesh-based team enables competitive pricing on a $25,000 starter engagement without cutting scope. Engineering hours that would cost more in San Francisco or London are spent on the same architecture work, the same documentation discipline, and the same evaluation rigor we apply to our own product portfolio.
Services Seed-Stage Founders Use Most
The four services below are the ones seed-stage founders draw from most often. Each links to its dedicated service page for technical depth — we do not duplicate that depth here.
AI Agent Development.
Most seed-stage AI MVPs are agent-based; the agent is the product surface where retrieval, tool-calling, and evaluation come together into something a user actually interacts with. Agent design is also the place where most early-stage AI builds quietly fail diligence — usually because the agent boundary, the tool schema, or the failure handling weren't designed at all.
AI MVP Development.
The core service that underpins the 90-day package. Covers MVP scoping, architecture-first proposals, and the production handover discipline that distinguishes a 90-day delivery from a notebook demo.
Generative AI Development.
For founders building GenAI-native products — content pipelines, image, voice, or multimodal applications.
AI Consulting & Strategy.
For founders pre-build who want architecture validation before they commit budget. A 1–2 week scoping engagement that produces a build-or-don't-build recommendation, a stack proposal with the trade-offs surfaced, and a cost projection you can take to your board. Cheaper than a wrong architecture choice.
Production Proof — 10+ Shipped AI Products
Riverborn has shipped 10+ AI products to a combined 100K+ worldwide users over four-plus years of operation. Our founders don't advise on AI product development — we ship AI products alongside client work. The infrastructure that powers our own portfolio is the same infrastructure we build for startup founders. The peer-to-peer credibility matters at the seed stage in a way it does not at the enterprise stage: at seed, the question is whether your build partner has shipped working AI before.
The portfolio maps directly to the build categories seed-stage teams need:
Conversational and voice:
VoiceIQ, the enterprise evolution of vocalo.ai, handles real-time voice intelligence with speech analysis at scale across 35+ languages.
Visual and creative:
VisualOS, the API-first creative production platform behind photofox.ai, ships brand-controlled visual assets at production volume.
Knowledge and assessment:
CertifyAI, our document-to-assessment infrastructure, productized from quizmakerai.org.
Content and multi-format:
NarrativeEngine, the brief-to-multimodal content pipeline, productized from aistorygen.org.
Design and visualization:
DraftForge, the sketch-to-image pipeline with ControlNet, productized from sketchtoimage.com.
Founders who have shipped their own products understand what other founders need. Riverborn's founders ship AI products. We know what 90 days from idea to a production-grade MVP actually looks like — because we've done it for ourselves, repeatedly, across voice, vision, content, and knowledge categories. Each shipped product carried its own evaluation discipline, its own cost envelope, and its own scaling decision. That experience is what makes the 90-day commitment we make to a startup founder credible rather than aspirational.
At the seed stage, the difference between a build partner who has shipped and one who hasn't shows up in the boring places: how the agent boundary is drawn, how cost-per-inference is tracked, what gets logged and what doesn't. None of that is exciting in a sales conversation. All of it is decisive in week 11 when you're three days from launch and a tool call is failing in production. The 90-Day AI Agent MVP is priced and scoped against this kind of week, not against the demo cycle.
How Investor Diligence Will Read Your MVP
Series A technical diligence reads an AI MVP differently than a board demo. The questions that surface are specific, and the documentation either exists or it doesn't. The 90-day build we ship is designed to answer those questions on delivery — not to retrofit answers later. The standard inclusions are the diligence inclusions; they ship inside the package, not as paid add-ons.
What Series A diligence typically examines in a seed-stage AI MVP:
Architecture decision records.
Documented choices on models, orchestration framework, and vector database, with the reasoning preserved for the next team.
Evaluation coverage.
How agent correctness is measured, with benchmarks the team can re-run.
Cost structure.
Cost-per-inference tracking, model routing logic, and a caching strategy, so unit economics are legible.
Scalability.
A written path from current usage to 10× or 100× without an architecture rewrite.
Observability.
Monitoring, logging, and audit trails the next engineering hire can read on day one.
These are not extras priced separately. They ship inside the 90-Day AI Agent MVP because demo-only shortcuts produce code that breaks under diligence. That conversation arrives at exactly the wrong moment in a fundraising cycle. An investor-ready AI prototype is what we build by default.
Series A diligence isn't adversarial; it's informed. The diligence team reading your MVP wants to invest. Their job is to confirm that the architecture they're funding will scale to the next round's milestones, without an immediate rewrite. The deliverables we ship answer that question on day one, not after six weeks of remediation work that eats into your team's next sprint.
Why Riverborn for Pre-seed and Seed Startups
Fixed-scope 90-day engagement with published price.
Other AI dev firms say "custom engagement" and ask for a discovery call before quoting. We say $25,000, 12 weeks, production-grade agent delivered — the scope, the price, and the timeline are all on the page.
Production-from-day-one architecture.
Named modern stack from the first commit: LangChain, CrewAI, or LangGraph for orchestration; Pinecone, Weaviate, or pgvector for retrieval; evaluation suites and structured outputs as standard. Designed to scale past Series A, not be rewritten after it. Architecture choices are documented in the repository, so the team you hire next month can read them on day one.
10+ shipped AI products as proof.
Peer-to-peer credibility — Riverborn's founders ship AI products. The portfolio (VoiceIQ, VisualOS, NarrativeEngine, CertifyAI, DraftForge) carries 100K+ users in aggregate. The same engineering team builds your MVP.
Investor-diligence-ready deliverables.
Architecture decision records, evaluation benchmarks, cost-per-inference tracking, and scalable infrastructure are standard inclusions, not paid extras. The MVP we ship is the MVP your Series A investors will interrogate.
Projects start at $25,000. The 90-Day AI Agent MVP is the default entry point for any startup AI development company conversation we have at this stage.
Department Focus Areas for Startup Builds
Many seed-stage product builds include a customer-facing conversational interface, especially in B2B and consumer SaaS. Our department-level capabilities for AI for customer support operations transfer directly into the startup MVP pattern — voice and chat agents, intelligent routing, and deflection-grade conversational handling are the same primitives whether the buyer is a startup founder or a CX leader at a 500-person organization.
Frequently Asked Questions
Yes. $25,000 covers the full 12-week engagement: discovery, architecture, build, integration, launch, and handover documentation. Infrastructure costs you own directly — hosting, model API usage, vector database — are scoped transparently in week 1, so there are no surprises later. The build itself is fixed.
Deployed to production infrastructure, monitored, documented, with evaluation benchmarks and architecture decision records. Not a Jupyter notebook demo. See the 90-Day AI Agent MVP breakdown for the full deliverables list. The difference matters when the next funding round runs technical diligence on what you shipped.
The MVP is yours: code in your repository, infrastructure in your accounts, documentation handed over. Continuation is optional. Many founders extend into post-launch iteration or Series A prep builds; the engagement model adjusts to growth stage. There is no retainer obligation built into the package.
No. We work with technical co-founders directly where you are technical, and alongside non-technical founders with architecture-first proposals that make every build decision transparent. Either way, the delivery — code, infrastructure, documentation — is yours to own and review at the end.
Stack choice depends on the use case. Common: LangChain, CrewAI, or LangGraph for orchestration; Pinecone, Weaviate, or pgvector for retrieval; GPT-4o, Claude, or Llama for generation — selected by evaluation, not vendor loyalty. Every choice is documented as an architecture decision record for future engineering teams.
No. 90 days is the minimum because production-grade architecture — deployment, monitoring, evaluation, documentation — takes that long to build correctly. Shorter engagements produce demo-ware that does not survive Series A diligence. We don't sell that. The 90-day floor is deliberate.
Riverborn has shipped 10+ AI products to 100K+ users over four-plus years. Our founders build alongside startup founders — peer-to-peer execution, not handover to juniors. The Bangladesh team composition enables competitive pricing on the $25,000 starter engagement without cutting scope.
A deployed production agent; source code in your repository; infrastructure in your accounts; architecture decision records; evaluation benchmarks; monitoring dashboards; and a handover document that lets your next engineering hire pick up without us. No lock-in, no proprietary platform dependency.