Fordel StudiosBackend Development
Backend infrastructure built for AI workloads from the first line of code.
What this means
in practice
Backend work in 2026 covers the same ground it always has — clean APIs, solid data models, reliable async jobs — plus a new layer of infrastructure that AI features require. Postgres now needs a vector column. Your API now streams. Your background workers now run embedding pipelines. We design for all of it from the start rather than retrofitting it later.
Our standard stack is Go (Gin, Fiber) for high-throughput infrastructure components and NestJS or Hono for application backends where developer velocity matters more than raw concurrency. Every system ships with OpenTelemetry traces, Prometheus metrics, and structured logs — not as an afterthought but as part of the initial architecture. If you're building anything LLM-powered, we design the inference proxy, the vector schema, and the streaming layer before the first line of application code gets written.
What Changed in Backend Development
Backend fundamentals have not changed: reliability, maintainability, performance under load. What has changed is the infrastructure a modern backend needs to support. Any application with AI features needs vector storage, embedding pipelines, streaming endpoints, and often an inference proxy. These are not exotic requirements — they are the new baseline.
The teams that treat AI infrastructure as an afterthought end up with embedding logic in API handlers (blocking, slow), vector queries without indexes (slow at scale), and LLM API calls without fallback (single point of failure). These are solvable engineering problems, and solving them at design time is much cheaper than fixing them in production.
The Database Changed Too
Postgres is still the right database for most applications. What has changed is that a Postgres schema in 2026 typically includes vector columns alongside the traditional relational schema. pgvector provides IVFFlat and HNSW indexes for approximate nearest-neighbor search on embedding vectors. For applications with under ten million vectors and standard accuracy requirements, this is the entire vector infrastructure story — no separate vector database, no new operational complexity.
The pattern: add a vector column (embedding VECTOR(1536)) to the document or content table, generate embeddings in a background job when content is created or updated, query by cosine similarity for semantic search. The similarity query lives alongside your regular SQL queries. Your existing Postgres expertise applies.
Go and the AI Infrastructure Backend
Go has become the language of choice for AI infrastructure components in 2026 — not because of any AI-specific feature, but because of its concurrency model and performance characteristics. Building an inference proxy that handles hundreds of concurrent streaming requests, each maintaining an open SSE connection, is a natural fit for Go's goroutine model. The same proxy in Node.js works but requires more careful backpressure management and is more sensitive to event loop blocking.
Node.js with NestJS remains the right choice for application backends where you are building CRUD APIs, managing business logic, and integrating with a broad ecosystem of npm packages. The two languages are complements, not competitors, and most production AI systems use both.
- Vector schema: pgvector columns on content tables, HNSW index for query performance
- Embedding pipeline: BullMQ job queue for async generation, idempotent on retry
- Streaming layer: SSE endpoints with proper flush configuration and timeout handling
- Inference proxy: routing, caching, fallback, and cost tracking across LLM providers
- Observability: OpenTelemetry traces, Prometheus metrics, structured JSON logs
- Migration discipline: every schema change as a versioned, reversible migration file
- 01
API development in Go (Gin, Fiber) and Node.js (NestJS, Hono)
- 02
Database architecture: Postgres with pgvector, query optimization, migration management
- 03
Background job infrastructure for embedding pipelines and async AI processing
- 04
Streaming response servers for real-time LLM output (SSE, chunked transfer)
- 05
Inference proxy layers: request routing, caching, fallback, cost control
- 06
Authentication and authorization architecture (JWT, OAuth2, RBAC)
- 07
Event-driven architecture: webhooks, message queues, change data capture
- 08
Performance optimization: connection pooling, query analysis, caching strategy
Our process
- 01
Architecture Design
We define the service topology, API surface, and data boundaries before writing code. For AI applications this includes the vector schema, embedding pipeline design, and streaming layer — decisions that determine system maintainability for years.
- 02
Data Model
We design the schema with the full data lifecycle in mind: creation patterns, retrieval indexes, update frequency, and archival. Applications with AI components get vector columns built in from the start, not added as a migration six months later.
- 03
Core API Implementation
We build primary endpoints with input validation via Zod or equivalent, structured error responses, and OpenAPI docs generated from the code. No undocumented endpoints, no raw error strings leaking to clients.
- 04
Background Infrastructure
We implement the job queue for async processing — embedding generation, AI enrichment pipelines, email, report generation. BullMQ with Redis for Node.js, channel-based worker pools for Go.
- 05
Integration Layer
We wire up third-party services — payment processors, LLM APIs, external data sources — with circuit breakers, retry logic, and fallback strategies. Failures in external services do not propagate upstream to your users.
- 06
Performance and Observability
We profile hot paths, set up query analysis, configure connection pooling, and instrument with OpenTelemetry and Prometheus. The system ships with dashboards and structured logs so your team can diagnose production issues without reading source code.
Tools and infrastructure we use for this capability.
Why work
with us
- 01
We design for the AI infrastructure layer upfront
Vector columns, embedding pipelines, streaming APIs, inference proxies — these are standard requirements for any AI-powered feature, not optional add-ons. We spec them in the architecture phase so they're not painful retrofits six months into a project.
- 02
We pick Go or Node.js based on the actual workload
Go's concurrency model is genuinely better for high-throughput streaming servers and embedding pipelines. NestJS is genuinely better for application backends where team velocity and npm ecosystem depth matter more. Most projects end up using both, and we make that call before the first sprint.
- 03
Data model quality determines long-term maintainability
A well-designed schema with correct indexes, migration discipline, and documented access patterns survives years of feature additions. We invest heavily here upfront because reworking a data model in a live system is one of the most expensive problems in software.
- 04
Observability ships with the system, not after the first incident
Every backend we deliver includes distributed tracing with OpenTelemetry, a Prometheus metrics endpoint, and structured logging with consistent correlation IDs. When something breaks at 2am, the on-call engineer diagnoses from dashboards — not from git blame.
Frequently
asked questions
When should we use Go versus Node.js for a backend service?
Go for inference proxies, streaming servers, and embedding pipelines where you need high concurrency and predictable memory overhead. Node.js with NestJS for application backends where developer velocity matters, the team is JavaScript-heavy, or you're leaning on npm ecosystem tooling. Most production systems we build use Node.js for the application layer and Go for any high-throughput infrastructure component.
What backend infrastructure does an AI feature actually require?
At minimum: a vector column in Postgres (pgvector) for semantic search, background jobs for embedding generation and indexing, a streaming endpoint for LLM output, and usually an inference proxy for model routing and cost control. These aren't optional — skipping them means no observability, no cost visibility, and reliability problems under real load.
How do you handle streaming LLM responses from the backend?
Server-Sent Events is the standard pattern. The backend connects to the LLM API, transforms the token stream, and forwards it to the client via SSE with proper flush behavior. The critical details are connection timeout configuration for long responses and mid-stream error handling — both of which break in subtle ways if you don't design for them explicitly.
How do you manage database migrations safely in production?
We use migration-based schema management (Prisma Migrate, Flyway, or golang-migrate) with every change versioned and applied in CI before deployment. Destructive operations always happen in two phases: a migration making the old thing optional, a deployment, then a cleanup migration. We never drop a column in the same deployment that stops using it.
What is an inference proxy and does our application need one?
An inference proxy sits between your application and LLM APIs and handles model routing (GPT-4o for complex tasks, Haiku for cheap ones), response caching, fallback when a provider is down, and cost accounting by feature or user. If you have more than one LLM-powered feature in production, the operational clarity from a proxy layer pays for itself within the first month of real traffic.
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