SaaS and startups Design stage
Designing a goal tracker that measures effort, not streaks
A product design built around handwritten daily proof, locked plans and honest self-scoring. The build is in progress.
Industries
Product development and AI for SaaS companies and startups
Startups need to learn fast without building a mess they cannot scale. We help founders and product teams ship quickly on foundations that last.
Common challenges
What slows this industry down
Founders and SaaS product teams face a constant trade-off between speed and quality. Ship too slowly and the market moves on. Ship carelessly and the product becomes hard to change just as customers arrive. Most teams are also small, so every week of engineering time matters.
AI has changed both sides of this. Our own AI-accelerated process lets us build a first version faster than a traditional team, while senior engineers review every change. And customers now expect AI features in many products, such as search that understands questions, assistants inside the app and automatic summaries. Adding those well takes care with cost, speed and quality.
We build MVPs, subscription and billing systems, SaaS platforms that serve many customers from one codebase, and AI features inside existing products. We work as your product team or alongside it. You own the code.
Getting to a first version
Founders need something real in front of users quickly, on a limited budget.
Billing and subscriptions
Plans, trials, upgrades, invoices and failed payments are complex to get right.
Serving many customers
Each customer needs its own data, users and settings, securely separated.
Adding AI features
Customers expect AI, but costs, speed and quality must be controlled.
Growing without breaking
Early code choices start to slow the team as users and features grow.
What we build
What we build for this industry
The services founders and SaaS teams ask us for most often.
MVP development
A first version with just enough features to put in front of real users and learn what to build next.
Subscription platforms
Products people pay for monthly or yearly, with plans, trials, upgrades and billing that just works.
LLM integration
Add a large language model to software you already have, with the guardrails, costs and logging handled.
Multi-tenant architecture
One product serving many customer accounts, with each account's data kept separate and secure.
Solutions that fit
Problems we solve here most often
AI support agent
An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
AI lead qualification
Score, research and route every new inquiry so your team talks to the best leads first.
Feedback analysis
Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
AI-assisted content workflows
Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
Case studies
Work in this industry
In practice
What it looks like in practice
An illustrative walk-through, not a client story.
A founder has a validated idea for a scheduling product for small clinics and a list of interested customers. We agree on the smallest version that would get a clinic to pay: booking, reminders and a simple admin area. It is built on a multi-tenant structure so each clinic has its own data from day one, with subscription billing through Stripe.
The first clinics use it within weeks. Their feedback shapes the next features, and support questions are handled by an assistant that knows the help pages. When usage grows, an AI feature that summarizes each day's bookings for clinic staff is added, with cost per clinic measured and capped. Because the foundations were planned, the team adds features without rewriting the core.
The founder owns the code and can hire an in-house team at any time, and the handover is simple because the code is documented and tested.
Throughout, the founder sees progress in a working product each week rather than in status reports, and can change priorities as customers respond. Nothing is hidden behind a long build phase, and every decision is visible in the tracker.
Where to start
Where most SaaS and startups teams start
Founders with a new idea start with MVP development. Teams with an existing product usually come to us for AI features, subscription billing or multi-tenant architecture as they grow.
Common AI additions include AI customer support, feedback analysis and lead qualification for the sales side of the business. When growth brings performance problems, product scaling is the next step.
Models and tools
MCP servers and AI models that matter here
These connect AI to the tools this industry already uses. We pick per project and test on real data first.
- StripeStripe's official MCP server lets AI search and read Stripe data, make changes with confirmation for sensitive actions, run analytics and search docs.
- GitHubGitHub's official MCP server lets AI read repositories, search code, manage issues and pull requests and, when allowed, change files.
- LinearLinear's official hosted MCP server lets AI find, create and update issues, projects and comments, with a read-only endpoint available.
- SupabaseSupabase's official hosted MCP server lets AI work with your Supabase projects: tables, SQL, migrations, edge functions, branches and docs.
- Claude Sonnet 5Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
- GPT-6 SolThe middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
Keep exploring
More for SaaS and startups
Related services
View all related services- MVP development A first version with just enough features to put in front of real users and learn what to build next.
- Subscription platforms Products people pay for monthly or yearly, with plans, trials, upgrades and billing that just works.
- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
- AI-Powered Web Development Business websites, customer portals, dashboards and web applications that load fast, rank well and are easy to change.
- SaaS Product Development MVPs, subscription platforms, admin panels and multi-tenant products, planned to grow past the first launch.
Solutions
View all solutions- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- AI-assisted content workflows Briefs, first drafts, edits and repurposing in a workflow where people set the angle and approve every word.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
Industries
View all industries- Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.
- Hospitality and travel Booking sites, guest messaging, review analysis and multilingual content for hotels, tour operators and travel businesses.
- Marketing agencies Landing pages, content workflows, client reporting and white-label development capacity for marketing agencies.
- Home services Booking, dispatch, quotes, invoicing and reviews for plumbers, electricians, cleaners and other home service businesses.
- Ecommerce Online stores, product data cleanup, support assistants, price monitoring and order automation for online retailers.
- Real estate Listing sites, lead handling, CRM automation and document workflows for agencies, brokers and developers.
Guides and articles
View all guides and articles- How to plan a SaaS MVP Decide what goes into the first version of a SaaS product, what waits, and which foundations to build properly.
- MVP vs full product Whether to launch small and learn, or build the complete product first.
- How to validate a product idea before building Cheap, fast ways to test whether customers want your product before you spend on development.
- Multi-tenant SaaS architecture explained How SaaS products serve many customers from one system, the main design options and how to choose.
- How to write a software requirements document Write a clear requirements document that gets accurate estimates and fewer surprises during the build.
- How much does it cost to build a web app? What drives the cost of a web app, how complexity changes the budget, and how to keep spending under control.
AI models
View all ai models- Claude Sonnet 5 Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
- GPT-6 Sol The middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
- General purpose All-round language models for writing, summarizing, support, extraction and most everyday business tasks.
- Claude Opus 5.5 Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
- Gemini 3.8 Flash Google's most capable Flash model, stable since September 2026, for agents, software engineering and enterprise workflows with full multimodal input.
- Meta Muse Spark Meta's newer proprietary model family, offered through the Meta Model API, with text, image, video and PDF input, tool calling and a 1 million token window.
MCP servers
View all mcp servers- Stripe Stripe's official MCP server lets AI search and read Stripe data, make changes with confirmation for sensitive actions, run analytics and search docs.
- GitHub GitHub's official MCP server lets AI read repositories, search code, manage issues and pull requests and, when allowed, change files.
- Linear Linear's official hosted MCP server lets AI find, create and update issues, projects and comments, with a read-only endpoint available.
- Supabase Supabase's official hosted MCP server lets AI work with your Supabase projects: tables, SQL, migrations, edge functions, branches and docs.
- BigCommerce BigCommerce's official Storefront MCP, in beta, lets AI shopping assistants browse the catalog, build carts and hand shoppers to checkout.
- Shopify Dev Shopify's Dev MCP server helps developers build for Shopify: it searches Shopify docs and API schemas and validates GraphQL, components and themes.
Glossary terms
View all glossary terms- MVP An MVP, or minimum viable product, is the smallest version of a product that real users can use and pay for, built to learn quickly what works.
- SaaS SaaS, or software as a service, is software delivered over the internet on a subscription, rather than installed and owned by each customer.
- Large language model A large language model, or LLM, is an AI model trained on vast amounts of text that can understand and generate language, and often images and code.
- Multi-tenant Multi-tenant describes software where one application and database serve many customers, called tenants, while keeping each customer's data separate.
- Prompt A prompt is the instruction and context you give an AI model to get the output you want.
- AI copilot An AI copilot is an assistant built into a tool people already use, which suggests, drafts and explains while the person stays in control.
From the blog
View all from the blog- The real cost drivers of a SaaS MVP What really drives the cost of a SaaS MVP: roles, integrations, billing, data, design, open decisions and what AI does and does not reduce, with ways to save.
- How to write a project brief that gets an accurate estimate What to put in a software project brief so teams can give an accurate estimate: goals, users, workflows, integrations, data, limits and priorities.
FAQ
Questions from SaaS and startups teams
Have a question that is not here? Ask us directly.
It depends on scope. We agree on the smallest useful version first and give an estimate after a short discovery. See MVP development and pricing and estimates.
Yes. The code and repository belong to you. See security and IP ownership.
Yes. We pick models by testing them on your data and keep cost and speed under control. See LLM integration.
Yes. Many clients use us alongside their own team. See engagement models.