Complex Backend Development
The hard part of most platforms is not the screens - it is the data model, the permissions, the integrations, and the logic nobody wrote down. That is the work we take on: backends with real business logic, built by senior engineers, owned by you.
Business logic is the product
Hierarchical organizations, role-based permissions, branching workflows, compliance constraints - the logic that makes a platform valuable is exactly what template tools cannot express.
For Alidade we matched a five-figure-a-year survey platform feature-for-feature on an Enterprise-to-Team data model, then went past it: subgroup filtering, custom benchmarking, and compliance commitments the rented tool could never make.
AI-native, not AI-bolted-on
We build backends that expose their domain to AI properly. On Alidade, an MCP connector gives Claude direct access to surveys, templates, and distributions - the AI validates server calculations and generates reports that outperform the platform’s own engine. Open-response analysis dropped from 2-3 weeks to 2-3 days.
If your roadmap says "add AI later", the data model we design today is what makes that cheap instead of impossible.
Replace the SaaS you rent
A five-figure annual SaaS bill for a tool that almost fits is a backend project with a payback date. We scope the replacement honestly - sometimes the verdict is "keep renting" - and when the numbers work, you end up with a platform that fits exactly and costs a fraction to run.
Proof
One owned platform replaced Qualtrics, Stata, and a contract analyst.
A research lab replaced a five-figure Qualtrics bill, manual Stata exports, and a paid report analyst with one AI-native platform - Claude wired in through MCP, serving a pipeline that includes a 1,800-student university and a 3,000-staff organization.
Read the case studyStack
Related reading
Frequently asked questions
- What counts as "complex" backend work?
- Multi-tenant data models, role hierarchies, workflow engines, third-party integrations, compliance-sensitive data handling, and AI connectors. If a no-code tool could do it, we will tell you to use the no-code tool.
- Can you take over an existing backend?
- Yes. We start with a technical audit, stabilize what is fragile, and evolve the architecture without a big-bang rewrite - unless the numbers genuinely favor one, and then we say so with a scoped plan.
- How do you integrate AI into a backend?
- Through the data model, not a chatbot widget. MCP connectors, retrieval over your real entities, and AI that validates against server-side calculations. We run this in production today, not as a demo.
- What stacks do you work in?
- Node.js/NestJS and TypeScript at the core, with PostgreSQL or Oracle, plus Java/Spring Boot where it fits. Hosting on AWS or your infrastructure - you own the code and the keys either way.
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