AI features that don't pay for themselves
Token costs grow faster than revenue when prompts aren't designed for caching and routing.
AI SaaS & Product Development
AI SaaS and LLM product development
We design and build AI-powered SaaS products end to end: the LLM features, backend, billing, authentication, integrations and the testing that lets you ship with confidence. Prompt caching and model routing keep unit economics healthy as you grow.
What you can expect
- An MVP in weeks, built to scale
- ~90% lower token costs with prompt caching
- Subscription billing live from day one
(01) The problem
Why teams come to us for AI SaaS & Product Development.
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Prototype code that can't scale
Notebook-grade backends collapse under real users, payments and security reviews.
No way to know if quality regressed
Without automated tests and evaluations, every prompt change is a gamble.
Too many vendors to coordinate
Separate designers, backend developers and AI specialists slow everything down.
(02) Use cases
What we build with AI SaaS & Product Development.
- 01
AI copilots inside your product
Assistants and agents embedded in your app, grounded in each customer's own data.
- 02
Conversational SaaS
Always-on AI assistants with memory, safety guardrails and subscriptions.
- 03
Vertical AI products
Legal, healthcare, finance and HR products built on RAG and agents.
- 04
AI MVPs for founders
Validate an idea with a working product and real users in weeks.
- 05
Internal AI platforms
Shared LLM gateways, prompt libraries and evaluation tooling for your teams.
- 06
AI feature retrofits
Add LLM-powered search, summaries or automation to an existing product.
Deliverables
Everything you need to run it in production.
- Product and architecture blueprint
- LLM features with prompt caching and model routing
- Backend APIs (Node.js, FastAPI) and data models
- Authentication, roles and multi-tenant design
- Stripe subscriptions, trials and webhooks
- Web front end in Next.js and React when needed
- Automated test suites and LLM evaluations
- Cloud deployment, monitoring and handover
Our approach
How we deliver reliable results.
- 1
Start from the business model
Pricing, unit economics and the core user journey shape the architecture.
- 2
Build the thin slice
One end-to-end flow, live with real users, before the scope expands.
- 3
Engineer cost in
Stable prompt prefixes for caching, model routing and usage limits from day one.
- 4
Test everything that matters
Chat logic, sessions, webhooks and security each get their own test categories.
Proof
Related case studies, with real numbers.
Digital health SaaSProduction backend
AI Sponsor
A 24/7 AI recovery-support SaaS with WhatsApp voice and ~90% lower token costs
- lower input-token cost
- ~90%
- lower input-token cost
- text and voice support
- 24/7
- text and voice support
- automated test categories
- 12
- automated test categories
Digital mental healthProduction-ready MVP
Namhee
An AI mental-health companion with mood tracking and automatic risk detection
- pass rate on 11 core tests
- 100%
- pass rate on 11 core tests
- RAG response target
- <5s
- RAG response target
- crisis detection
- Risk-aware
- crisis detection
Enterprise AI & finance
MCP Multi-Tool Agent Platform
A governed agent platform with persistent memory and human approval before sensitive actions
- pluggable tool access
- MCP
- pluggable tool access
- sensitive calls human-approved
- 100%
- sensitive calls human-approved
- memory across restarts
- Persistent
- memory across restarts
Also delivered
- Full-stack engineering for an AI tech startup
Technology
Tools we use for AI SaaS & Product Development.
Model-agnostic and cloud-agnostic: we pick the right components for your data, budget and compliance requirements.
- Next.js
- React
- TypeScript
- Node.js
- Express
- FastAPI
- PostgreSQL
- MongoDB
- Redis
- Stripe
- Claude
- OpenAI
- Pinecone
- Docker
- AWS
Industries we serve
Engagement models
Flexible ways to work, one standard of delivery.
Start small with a discovery sprint or an audit, or go straight to a production build. Most projects start from $5,000, scoped after a free strategy call.
1–2 weeks · fixed scope
AI Strategy & Discovery Sprint
Teams validating an AI opportunity before committing a budget.
- Use-case and ROI workshop
- Data and systems audit
- Architecture blueprint
- Risk and compliance review
- Build plan with estimates
4–12 weeks · milestone-based
Production Build
A defined RAG platform, AI agent, automation or AI product, delivered end to end.
- Architecture and full build
- Evaluation suite and QA
- Security and compliance controls
- Deployment and monitoring
- Documentation and handover
Monthly retainer
Dedicated AI Engineering
Product and platform teams with an ongoing AI roadmap and no time to hire.
- Senior AI engineering capacity
- Weekly planning and demos
- Continuous evaluation and monitoring
- Model and cost optimization
- Scale up with vetted specialists
1–3 weeks · fixed scope
AI Audit & Evaluation
An existing LLM, RAG or agent system that underperforms or is changing hands.
- Architecture and code review
- Answer-quality evaluation
- Security, PII and compliance review
- Cost and latency analysis
- Prioritized remediation report
Not sure which model fits? A free 30-minute call is enough to decide.
Book a free strategy callAI SaaS & Product Development FAQ
Common questions about AI SaaS & Product Development.
Can't find your answer? Ask us on a free strategy call or send a short brief.
How fast can you build an AI MVP?
Most AI MVPs reach real users in four to eight weeks, depending on integrations and compliance needs.
Do you build the front end too?
Yes. We build web apps in Next.js and React, or work alongside your existing product and design team.
How do you control LLM costs?
Prompt caching, model routing, response limits and usage monitoring. On AI Sponsor, caching cut input-token costs by about 90%.
Can you add AI features to our existing product?
Yes. We work in your codebase, APIs and data, and ship features behind flags so you can roll out safely.
Will we own the code?
Yes. You own all code, prompts, datasets and documentation, delivered to your repositories.
Do you support the product after launch?
Yes, through a monthly engineering retainer covering improvements, monitoring and model upgrades.
Taking on new projects
Let’sbuild.
Have an AI project in mind? Let’s make it real.
Book a free 30-minute strategy call with our founder. You’ll leave with a clear view of the right architecture, timeline and next step, whether or not we work together.
- Reply within 24 hours
- NDA before you share anything confidential
- You own the code, prompts and documentation
- Hours overlap with US, UK, EU and Gulf teams