Service
AI SaaS development for multi-tenant products
Whether you're launching a new AI startup or adding intelligent features to an existing product, we design and develop scalable SaaS platforms that deliver real value — from product architecture and multi-tenant infrastructure to LLM integration and metered billing.
Products & platforms engineered across 12+ industries
AI runs delivered at 99.9% uptime on production SaaS deployments
LLM providers orchestrated in parallel for cost-optimized parsing (Callan Hawkins)
AI SaaS platforms shipped to production
We have built multi-tenant AI SaaS for organizational change, no-code voice agents, and agentic hiring — each with real subscription billing, scalable infrastructure, and measurable production outcomes.
ChangeTools.ai
An AI-powered change management platform for leaders, consultants, and HR teams. Multi-tenant SaaS architecture, GPT-4o-driven insights for strategic planning and employee assessments, subscription tiers, Google OAuth, and scalable AWS deployment.
Multi-tenant SaaS with subscription billing
GPT-4o-driven insights for assessments and planning
End-to-end auth, team collaboration, and scaling
Built on Next.js + Node.js + MongoDB + AWS EC2
Verdapt
A tool-agnostic, no-code AI conversation platform for deploying intelligent multi-channel bots. Bring-your-own APIs, OpenAI Agents orchestration, full Twilio Voice + SMS + Serverless integration, real-time STT → LLM → TTS streaming, Stripe metered billing, RBAC, credit wallets, and knowledge-base ingestion. Bots deploy in minutes from the dashboard.
One bot across voice, SMS, and web chat
Real-time STT → LLM → TTS streaming
Multi-tenant RBAC + Stripe metered billing
Bring-your-own APIs for industry-specific logic
Atlast
An agentic platform with conversational AI agents that handle CV parsing, candidate screening, interviewing, shortlisting, and scheduling — each stage owned by a specialized agent rather than a fragile sequential script.
Specialized agents per recruitment stage
Conversational interview and screening flows
OpenAI + AWS + Zapier integration layer
Scalable to high-volume recruiting pipelines
Let's take your SaaS product to the next level
Discover how AI-powered SaaS architecture compounds product value
Multi-tenant from day one
Tenants, roles, and metered billing baked in early — so the product can serve enterprise customers without a re-architecture later.
AI features that scale economically
LLM orchestration tuned for cost: provider selection, caching, and parallel-provider strategies like the one shipped on Callan Hawkins.
Real subscription and usage billing
Stripe plans, credit wallets, and usage metering wired into the product instead of bolted on at the end.
Production observability
Conversation analytics, sentiment, uptime monitoring, and run-level traces so the SaaS keeps improving after launch.
Driving measurable outcomes through AI SaaS
AI runs delivered
across GetBoardWise production with 99.9% uptime
NPS uplift
on GetBoardWise after AI pipeline rollout
LLMs orchestrated in parallel
for cost-optimized CV parsing on Callan Hawkins
Our partners find numerous reasons to love us
Tinkerbyte translated a complex SaaS idea into a clear, multi-tenant product with real subscription and AI infrastructure underneath — not a prototype dressed up to look like one.
Client
CTO, AI SaaS Startup
Well-structured AI SaaS solutions built for your needs
We help companies build production-grade SaaS platforms that scale with their business.
AI SaaS development — common questions
What does it take to build an AI SaaS product the right way?
Three things have to be right from day one: multi-tenant architecture (so you can serve enterprise customers without re-architecting), LLM orchestration with cost controls baked in, and real subscription + usage billing (Stripe plans, credit wallets, metered events). Skip any of these and you ship a prototype dressed up to look like a product.
How do you control LLM costs in a multi-tenant SaaS?
A few layers stack together. Provider routing — send each task to the model that handles it best for the price. Parallel-provider strategies — Callan Hawkins runs OpenAI, Gemini, and Claude in parallel for CV parsing and routes based on confidence/cost. Aggressive caching for repeated prompts. Per-tenant usage metering with hard caps tied to subscription tier. And telemetry on every call so cost-spike incidents get caught in hours, not on the next invoice.
How long does it take to launch an AI SaaS MVP?
A focused multi-tenant AI MVP — auth, tenant isolation, one core AI feature, subscription billing — runs 8–12 weeks. Adding more depth (Verdapt-style multi-channel agents with real-time STT/TTS streaming, or ChangeTools-style multi-tier subscription + AI insights) pushes the timeline to 3–5 months. We scope per project so the estimate matches reality.
Do you build voice AI products too, or only text-based AI SaaS?
Both. Verdapt is a no-code voice + SMS + chat platform we built with Twilio Programmable Voice + Serverless, OpenAI Agents, and real-time STT → LLM → TTS streaming. Voice AI has a different infrastructure shape than text — telephony, audio latency, streaming responses — and we have shipped it to production.
Can the SaaS we build with you scale to enterprise customers?
That is the design intent from the first commit. Multi-tenant data isolation, RBAC, document versioning, audit trails, SSO-ready auth, and integration-friendly REST APIs are baseline — not added later. Callan Hawkins ships with dynamic RBAC across clients, jobs, and candidates because that is what enterprise procurement requires.
What does an AI SaaS development project cost?
Lower-scope MVPs (one AI feature, one tier) cost less. Full multi-channel agentic platforms (Verdapt-tier) cost more. The cost-driver is feature surface area more than raw engineering hours. After a free tech audit we deliver a transparent estimate tied to specific milestones, not a vague number.
Ready to scale your business?
Book a free consultation to get clarity, direction, and expert advice you can implement right away.
