AI Solutions
Building AI-Native SaaS
We build SaaS products with AI at the core, handling the multi-tenant, billing and cost realities that set AI products apart from ordinary software.
Overview
AI SaaS Development
An AI SaaS product has the usual SaaS needs, such as tenancy, authentication, subscriptions and admin tools, plus some of its own. Model usage creates a variable cost for every customer action, so pricing, quotas and metering must be designed in. Customer data must stay isolated, including in retrieval indexes and logs. Codemotives treats these as architecture decisions made early, not features added once the bills arrive.
We also design for trust. Customers will ask where their data goes, whether it trains any models and how wrong answers are handled, so the product needs clear answers and controls. We build evaluation and monitoring into the release process, so a prompt or model change is tested before it reaches every tenant, and we scope an initial release small enough to learn from real users.
Context
Problems we solve
- 01
Variable cost per action
Each AI call costs money. Pricing, quotas and metering must reflect it or margins disappear.
- 02
Tenant data isolation
Embeddings, caches and logs can leak across customers if not segregated carefully.
- 03
Quality drift across releases
A model or prompt change can degrade results for some tenants. Releases need evaluation gates.
- 04
Customer trust questions
Buyers ask about data use, retention and accuracy. The product needs honest, documented answers.
Capabilities
What we deliver
- 01
Multi-tenant AI architecture
Per-tenant data separation across databases, vector indexes, storage and logs.
- 02
Usage metering and billing
Tracking of AI consumption with quotas, plan limits and integration with subscription billing.
- 03
Model abstraction
A provider-neutral layer so you can change models, route by task and manage cost.
- 04
Evaluation in release flow
Automated test sets run on prompt and model changes before they ship to customers.
- 05
Customer controls
Settings for data retention, opt-outs and feature access, plus admin visibility into usage.
- 06
Lean first release
A narrowly scoped launch with analytics to learn what users value before expanding.
Where it fits
- AI writing or analysis tool sold by subscription
- Vertical SaaS with built-in document intelligence
- Workflow product with an embedded copilot
- Platform exposing AI features through an API
- Existing SaaS adding a paid AI tier
Technologies we use
- Next.js
- Node.js
- Python
- PostgreSQL
- pgvector
- Stripe
- Redis
- AWS
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
How should we price an AI SaaS product?
Pricing depends on your variable model costs, how customers use the product and what they value. We help you meter usage and model the unit economics, though the pricing decision itself is a business choice.
Will our customers' data train the models?
That is your decision and your providers' terms. We build controls so customer data is isolated and not used for training unless explicitly agreed, and we help you document this for buyers.
Can you add AI to our existing SaaS instead?
Yes. Often the better route is a focused AI feature on your current product, tested with a subset of customers before you commit to a larger rebuild.
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.