Backend development services that keep growth work from breaking

Most growth stacks don't have a tools problem. They have a plumbing problem.

The average organization now runs 957 applications, and only 27% of them are connected, per MuleSoft's 2026 Connectivity Benchmark. Scale that down to a growing business and the shape is identical: a CRM that disagrees with the billing system, lead forms posting to a spreadsheet nobody checks, Monday reports assembled by hand, and an automation that quietly stopped firing three weeks ago because a third-party API changed.

Marketing, automation, and AI systems are only as strong as the backend underneath them. The visible layer, the website, the dashboard, the agent, gets the attention. The layer that decides whether any of it can be trusted is server-side: APIs, databases, queues, authentication, retries, logs. Entropy builds that layer: the APIs, databases, integrations, and workflow infrastructure that help your tools share data, trigger the right actions, and stay reliable as usage grows.

This page covers backend work as its own discipline. If you need the interface built too, see full-stack development. If you are scoping an entire product from zero, start at custom software development.

What we build

• REST and GraphQL APIs, typically FastAPI or Django on Python, or Node.js with Express, documented with an OpenAPI spec from day one

• Database design and optimization, PostgreSQL first, Redis for caching and queues, with indexes and query plans reviewed before anyone blames the hardware

• CRM, ERP, and third-party integrations, HubSpot, Salesforce, GoHighLevel, Shopify, Stripe, QuickBooks, and the long tail of vertical tools your industry runs on

• Webhook systems and event-driven workflows, signature verification, retry logic, and idempotency keys, so a duplicate event never becomes a duplicate invoice

• Authentication and role-based access, OAuth 2.0, JWTs, and SSO through Clerk or Auth0 when building auth from scratch is not worth the risk

• Admin tools and internal dashboards your operations team can use without filing a developer ticket

• Data syncs between marketing, sales, and operations tools, with conflict rules decided on purpose, not by whichever system wrote last

• Backend logic for AI agents and automations, the data access, permission boundaries, logging, and fallbacks behind anything that calls OpenAI, Claude, or Gemini

• Reporting pipelines that pull GA4, Search Console, ad platforms, and your own database into one queryable store

• Monitoring, logging, and deployment support, Sentry for errors, structured logs you can actually search, Docker images, CI through GitHub Actions, hosted on AWS, Railway, or Render depending on budget and scale

API development services

Most integration pain traces back to an API that was never designed, only accumulated. We treat the API as a contract: the spec is written in OpenAPI before the code, versioned so existing consumers don't break, and documented so the next developer, yours or ours, doesn't have to reverse-engineer behavior from production traffic.

That contract work covers the unglamorous details that decide reliability: authentication and scoped API keys, rate limiting, pagination that holds up past 10,000 records, error responses that say what went wrong and what to do next, and webhooks with retries and replay protection. If your product needs to expose an API to customers or partners, the same discipline applies in reverse, we build the API as a product, because to other software, that is exactly what it is.

When the glue tools stop being cheap

Zapier, Make, and n8n are often the right first move, and we say so even though we build custom systems. They prove a workflow has value before you commit engineering money to it.

They stop being the right tool at predictable points: task-based pricing that climbs with volume, multi-branch logic that no longer fits a linear editor, third-party rate limits, no real audit trail, and error handling that amounts to an email nobody reads. At that point the honest comparison is glue-tool subscription plus manual patching versus a small custom backend you own. For market context, Clutch's software development pricing guide shows most listed firms billing $25–$49 per hour, with senior US-based teams at $100–$149 and up, scope drives any real quote. Our framework for making the call is in buy vs build: a decision framework, and if the workflow currently lives in a spreadsheet, read when to replace spreadsheets with a custom app first.

The wrong answer is usually one of the extremes: paying agency rates to rebuild what a $30-per-month Zap does fine, or running a revenue-critical process on a tool that cannot tell you why Tuesday's records are missing.

How we work

1. Understand the system. We map the users, data, tools, workflows, and reliability requirements before proposing anything.

2. Design the architecture. We define the data model, API contracts, integrations, permissions, and deployment approach, in writing, so it can be challenged before it gets expensive.

3. Build in stages. We ship the core backend first, then add integrations, workflows, and reporting. You see working software early, not a reveal at the end.

4. Test the failure points. Authentication, edge cases, API errors, webhook retries, permissions, and performance under realistic load. Backends do not fail on the happy path, they fail at 2 a.m. on the retry nobody wrote.

5. Document and hand off. Your team gets the documentation needed to understand, maintain, and extend the system: architecture notes, runbooks, and an OpenAPI spec, not a folder of mystery code.

Timelines depend on integration count and data complexity more than anything else, we publish honest ranges in how long custom software takes to build.

The backend is why the automation kept working

Entropy's case studies show automation impact across order fulfillment, legal operations, lead intake, CRM syncing, and client onboarding. Strip the AI label off any of those and what remains is backend work: an order pipeline that reconciles inventory, a document workflow with permissions and an audit trail, an intake system that routes leads in seconds, a two-way CRM sync with conflict rules, an onboarding sequence that creates accounts and tasks without a human copying data between tabs.

That is the pattern worth copying. The distance between an automation that demos well and an automation your business can rely on is data access, permissions, logging, retries, and human approval where it matters. We build those foundations here and the agent layer in AI automation, the integration half is written up in integrating AI agents with existing systems.

FAQ

Can you connect our existing tools?

Yes. A large part of backend work is connecting CRMs, websites, ad platforms, payment systems, databases, and internal tools. Most platforms you already use, HubSpot, Salesforce, Shopify, Stripe, have documented APIs. The real work is mapping the data correctly, handling failures, and deciding what wins when two systems disagree.

Do you only build new systems?

No. We can also audit, stabilize, and improve existing backends. That usually starts with logging and monitoring, because you cannot fix what you cannot see, then the highest-risk failure points, then performance. Inheriting a system nobody fully understands is a normal starting point, not an embarrassing one, see what software maintenance actually costs.

Can backend work support AI automation?

Yes. Reliable AI automation needs clean data access, secure APIs, logging, permissions, and error handling. That is backend work. It is the difference between an agent demo and an agent your team trusts with real customer data.

What does backend development cost?

Market rates first: Clutch's pricing data puts most software development firms at $25–$49 per hour, with senior US-based teams at $100–$149 and up. Project totals vary too much with scope for a flat number to be useful, integration count, data volume, and compliance requirements move it most, and we break the variables down in custom software development costs in 2026. Bring your workflow to a scoping call and you get a number for your scope, not a tier.

Which stack do you build on?

Python (FastAPI, Django) and Node.js for application code, PostgreSQL for data, Redis for caching and queues, Docker and GitHub Actions for shipping. Deliberately boring choices: each has years of production history, a deep hiring pool, and documentation the next team can use. If your existing system runs on something else, we work with what is there before proposing a rewrite.

Scope a backend build

If your tools do not talk to each other cleanly, the backend is usually where the fix starts. Bring the workflow that breaks most often, we will map the system, name the failure points, and quote the smallest build that fixes it. Scope a backend build.

Related: Custom software development · Full-stack development · Front-end development · AI automation