Gotobeat
SaaS, AI agents and MCP
A live music ticketing platform gained AI agents, an MCP server and automated reporting, from one full-stack team.
AI SaaS development, built for production from the first release
Full-stack products in Next.js, TypeScript and Python: data model, APIs, UI, background jobs, reporting pipelines and deployment on Vercel or AWS, with the AI layer designed in from the start.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. No deck, no pitch.
Discovery sprint from $2,500, builds from $6,000
Stack
- Next.js
- Remix
- React
- TypeScript
- Node.js
- NestJS
- Python/FastAPI
- PostgreSQL
- Supabase
- Redis
- AWS
- Docker
Sound familiar?
Too many vendors, no single owner
Separate frontend, backend and DevOps contractors leave gaps at the boundaries, and that is where the bugs live.
Reporting is still manual
Someone exports a spreadsheet every Monday because the product never got reporting pipelines.
AI wasn't part of the architecture
Adding agents later is harder when the data model and APIs were not designed for them.
What you get
- A written plan: architecture, data model, edge cases and the first release
- Next.js or Remix frontend in TypeScript and Tailwind
- APIs in Node.js, NestJS or Python/FastAPI
- PostgreSQL or Supabase data layer, with Redis where it helps
- Automated reporting pipelines in the first release
- AI agents and MCP servers where they add value
- CI/CD, Docker and deployment on Vercel or AWS
How it works
01
Plan the first release
Architecture, data model, edge cases and trade-offs in writing, with a list of what ships first and what is left out on purpose.
02
Build in small increments
Working software you can click through as we go, with a written update at every step.
03
Ship and support
We deploy, monitor and keep improving the product month by month, or hand it to your team with docs.
What an AI SaaS development company builds
An AI SaaS development company builds the whole product around the model calls: the data model and APIs, the UI, auth and multi-tenancy, background jobs, reporting, deployment, and the AI layer (agents, retrieval, MCP tools) designed into that architecture from the start. Tecifo does this in Next.js, TypeScript and Python, on Vercel or AWS.
Two of our four case studies are full products. A live music ticketing platform in Remix and TypeScript on AWS gained AI agents, an MCP server and automated reporting pipelines. An enterprise knowledge platform in Next.js on Supabase grew from a proof of concept into multi-tenant workspaces with streaming AI chat over 1,145 hours. One team built the product and the AI in each.
Designing the data model and APIs so agents can use them later
A SaaS data model built with agents in mind has clean, typed APIs for every action a user can take, a clear tenant boundary enforced in the database, and background jobs for anything slow. Those same properties are what an agent, an MCP server or a reporting pipeline needs, so adding AI later becomes wiring rather than a rebuild.
On the ticketing platform, the MCP server exposed the same actions the product already had as typed tools, and the reporting pipelines ran as scheduled jobs over the same data model. Neither needed the product to change shape.
One team for product, AI and deployment
One team owning the frontend, the APIs, the AI layer and the deployment removes the gaps between vendors where bugs and delays live. Tecifo ships Next.js or Remix frontends, Node.js, NestJS or Python/FastAPI APIs, PostgreSQL or Supabase data layers, and CI/CD to Vercel or AWS, with a written update at every increment.
Reporting is part of the product from the first release, as pipelines that run on a schedule, because the alternative is a team exporting spreadsheets every Monday for the life of the product.
What AI SaaS development costs and how long it takes
AI SaaS development at Tecifo starts with a discovery sprint from $2,500 that produces the architecture, data model and first-release plan, followed by a fixed-scope build from $6,000 for the first release or an ongoing team from $45 an hour. First releases are scoped to weeks in the plan; the full enterprise platform we built ran six months.
The plan lists what ships first and, just as important, what is left out of the first release. Scope changes after that are agreed in writing and priced against the plan.
When you should not build a new SaaS yet
Do not build a new SaaS when a Lovable or Bolt prototype would answer your market question in a week, or when the product is really a feature of a tool your customers already pay for. We will say so on the call and point you to the MVP route or an integration instead of a build.
Separate contractors vs one team
The default when you have no in-house engineering team. If you have a strong frontend team already, we slot in behind it instead.
Who owns a bug at the boundary
- Frontend, backend and DevOps contractors
- Nobody, by default
- One team
- Us, by definition
Adding AI later
- Frontend, backend and DevOps contractors
- A fourth vendor
- One team
- The same engineers, the same data model
Reporting
- Frontend, backend and DevOps contractors
- Someone's spreadsheet
- One team
- Scheduled pipelines in the first release
Updates
- Frontend, backend and DevOps contractors
- Three status threads
- One team
- One written update per increment
Deployment
- Frontend, backend and DevOps contractors
- Handed over at the end
- One team
- CI/CD from the first week, in your accounts
Example we shipped
- Frontend, backend and DevOps contractors
- The setup most rescue projects arrive with
- One team
- Ticketing platform: Remix, AWS, agents, MCP server, reporting pipelines
The right column is how the ticketing platform and the enterprise knowledge platform were both built.
Questions, including cost and timeline
Tell us what you're building.
Free 30-minute call. You leave with a written summary of the approach and a sensible first step. Prefer writing? Send a brief and we reply by email with questions.
Discovery sprint from $2,500, builds from $6,000
Or email info@tecifo.com with a few times that suit you.Not ready to talk? Take the checklist.
Fifteen checks we run before real users and real data hit an AI product: prompt caching, context budgets, approval steps, row-level security, agent state, traces. We email you the link.