AI product development for startups
A focused MVP around one core workflow, ready for pilots, design partners and investor demos.
Custom AI · SaaS
AI MVP Development for Startups and SaaS Founders
Go from idea to a working AI product that pilot customers use and investors can try. We build the model layer, the app and the infrastructure, ready to grow past the MVP.
Overview
AI MVP development, from idea to paying users
You have a thesis about where AI changes a market, and you need proof. Investors want traction, pilot customers want something they can log into, and you can't afford to spend six months on infrastructure before anyone sees it. AI MVP development is about building the smallest product that proves the idea with real users, on a foundation that doesn't need to be thrown away when it works.
We have done this for founders more than once. Vikk is an AI legal assistant built on LLMs, RAG and multi-agent orchestration. It went from kickoff to MVP in six weeks and has grown to 120,000+ users across all 50 US states, in 30+ languages. Medsuccour needed something different: a deep-learning model that reads head CT scans and localizes brain injuries in seconds, which reached a working MVP in about twelve weeks. Both are proof that an AI product built fast can still hold up under real use.
As an AI SaaS development company, we cover the full product, not just the model. That means multi-tenant architecture, authentication and roles, retrieval over each customer's data, evaluation pipelines, usage metering and billing through Stripe, admin panels, and a web or mobile front end. We choose models on evidence, run evaluation sets before every release, and design the AI layer so you can switch between OpenAI, Anthropic Claude, Google Gemini or open-source models as costs and quality change. You keep 100% of the IP, so the codebase is an asset in your next round, not a dependency on us.
Use cases
Custom AI Development use cases in SaaS
LLM app development
Products built on language models: drafting, analysis, research, extraction and chat over domain documents.
Multi-agent systems
Several specialized agents that split a complex task, check each other's work and hand off to humans, as in Vikk.
Custom and fine-tuned models
Computer-vision, classification or fine-tuned language models where a general API isn't accurate enough.
AI features for an existing platform
Copilots, semantic search, summarization and prediction added to a product that already has customers.
Rebuilding a fragile prototype
Turn a weekend demo into a product with evaluation, monitoring, tenant isolation and sensible costs.
How it works
How it works
- 01User works in your appA web or mobile front end handles accounts, roles and the workflow your product is built around.
- 02Requests reach the AI layerAn orchestration service picks the right model for the task and pulls context from the user's own tenant data.
- 03Models and agents do the workLLMs, agents or custom models produce drafts, analysis or predictions, with validation on every structured output.
- 04Guardrails and evaluationOutputs are checked against rules, sampled for quality and tracked against evaluation sets across releases.
- 05Usage is metered and billedTokens, requests and costs are recorded per tenant and feed plans and invoices in Stripe.
Capabilities
Key features
Model-agnostic AI layer
Swap between OpenAI, Anthropic, Google and open-source models without rewriting your product.
RAG with tenant isolation
Retrieval over customer documents and records, scoped to tenant and role at query time.
Evaluation pipelines
Test sets built from real cases catch hallucinations and regressions before a release ships.
Production SaaS foundations
Auth, roles, admin panel, audit logs, usage metering and billing, built in from day one.
Compliance-aware design
Controls SOC 2 auditors look for, GDPR data-processing support and documentation that helps with EU AI Act duties.
100% IP ownership
Source code, prompts, evaluation data and trained weights belong to you.
Integrations
Works with your SaaS stack
- OpenAI
- Anthropic Claude
- Google Gemini
- LangChain
- LlamaIndex
- pgvector
- PostgreSQL
- Supabase
- Next.js
- React Native
- Vercel
- AWS
Using something else? If it has an API, a database or a webhook, we can connect to it.
Outcomes
Benefits for SaaS teams
Proof in weeks
A real product in front of pilot users while competitors are still hiring.
No rewrite after the MVP
The foundation is built to grow, so traction doesn't trigger a six-month rebuild.
A team you don't have to assemble
AI, backend, front end and DevOps engineers who have shipped together, available from the first week.
Due-diligence ready
Clean code, documented architecture and full IP ownership hold up when investors look closely.
Our work
Proven Startup & SaaS Work
AI Chatbot2024
Vikk AI: AI-Powered Legal Assistant
Vikk AI is a 24/7 AI-powered legal assistant that gives users instant, private legal guidance, analyzes their documents, and connects them with verified lawyers when needed. We helped build a conversational platform that delivers state-specific answers across dozens of legal categories and languages.
View case study →
Custom AI Model Development2024
Medsuccour AI: Revolutionizing CT Scan Diagnosis
Medsuccour is an AI-powered project designed to revolutionize the diagnosis of traumatic brain injuries through rapid and accurate analysis of CT scans. Given that every minute counts after a head trauma, our model drastically reduces the time required to detect and localize brain injuries, generating detailed reports in seconds.
View case study →
AI Chatbot2025
CanVisas AI: Canadian Immigration Eligibility Assistant
CanVisas AI is an intelligent chatbot that helps users check their eligibility for Canadian permanent residence through simple conversations. The chatbot asks pre-screening questions about immigration requirements and provides instant eligibility assessments. It qualifies leads by gathering important information before connecting them with the CanVisas team.
View case study →
Products built by The Botss
Product
Commify.ai
Commify.ai is an AI-driven platform designed to help small and medium-sized e-commerce businesses automate. By integrating directly with Shopify or WooCommerce stores, Commify.ai enables companies to deploy AI chatbots for 24/7 support, generate and edit product descriptions and images, and reduce manual workload, allowing merchants to focus on growth and sales.
Explore Commify.ai ProductIWMCRM
I Will Manage (IWM) CRM is an AI-powered SaaS platform purpose-built for pharma distribution companies, designed to replace manual operations with intelligent automation and real-time insights. Developed by The Botss, the platform centralizes lead, vendor, product, and team management while leveraging AI-driven communication, predictive analytics, and seamless integrations with tools such as Microsoft 365, Google Workspace, Zoom, and Twilio.
Explore IWMCRMFAQ
Custom AI Development for SaaS: FAQs
How much does AI MVP development cost?
It depends on how many workflows the MVP covers, whether it needs custom models or just APIs, how many integrations it has and what security your first customers expect. We scope it in discovery and give a fixed quote. Book a call and we'll tell you what a sensible first version includes.
How long does it take to build an AI SaaS MVP?
Usually six to twelve weeks. Vikk's MVP took six weeks and Medsuccour's custom deep-learning model took about twelve. A narrow chatbot product can be faster, as CanVisas showed at three weeks.
What is LLM app development?
It's building software whose core feature runs on a large language model, such as drafting, analysis, research or chat over documents. Most of the work is around the model: retrieval, prompts, evaluation, guardrails, cost control and the product itself.
Should we fine-tune a model or use RAG?
Usually RAG first. Retrieval keeps answers tied to your current data and is easier to update. Fine-tuning helps when you need a specific style, format or task performance that prompting can't reach, and we test both on your data before deciding.
Can a startup build an AI SaaS without in-house AI engineers?
Yes. Many founders start with an external team for the MVP, then hire once there's traction. We document everything and can hand over to your hires, or stay on as a dedicated team.
Will we be locked into one AI provider?
No. We build a provider-neutral AI layer, so you can move between OpenAI, Anthropic, Google and open-source models when pricing or quality shifts.
Who owns the IP in an AI MVP you build?
You do, fully. Code, prompts, evaluation sets and any trained model weights are assigned to your company, which matters when investors run due diligence.
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Your AI idea deserves real users, not another deck.
Bring the idea and the market you're going after. We'll tell you what the MVP should include, how long it takes and what it proves.