Drafting a Technical Specification for a Website

We develop technical specifications for websites that prevent client dissatisfaction. As engineers with a decade of experience, we've prepared over 50 TS for online stores, services, and portals. A common client complaint: 'This is not what I wanted.' This happens due to poor specification. Our approach: detailed analysis, formalized requirements, and versioning in Git. A good TS is a communication tool between the client, developers, and testers. It defines what will be built, how it looks, and how it is verified. Without a TS, a project descends into chaos. Our services for drafting a TS start at $1,500 for simple projects and can reach $5,000 for complex e-commerce platforms, saving you thousands in rework costs. Contact us for an audit of your project — we'll turn chaotic wishes into clear documentation.

The Purpose of a Technical Specification

The main mistake in a TS is describing the interface instead of behavior. 'A blue button in the right corner' is not a TS. But 'When the "Place order" button is clicked, the system creates an order with status draft, reserves the product, sends a confirmation email, and redirects to /orders/{id}' — that is a TS.

A quality TS saves budget. It eliminates ambiguity and reduces rework risk. Using a detailed TS reduces development time by up to 2 times compared to ad-hoc requirements.

Characteristics of a Quality TS

A good TS includes:

• measurable criteria (LCP < 2.5 s, conversion > 3%),
• unambiguous wording — any developer understands it the same way,
• behavior description, not interface,
• stored in Git with versioning and discussion via Pull Request.

A poor TS is a collection of screenshots and phrases like 'make it beautiful'. It guarantees rework, which costs 3–5 times more.

Why Git is Better for Storing a TS

Markdown + Git offers advantages over Word:

A TS in Git is 10 times more convenient for version management. That's our approach.

Components of an E-commerce TS

Purpose and System Goals

Describes the context: who it's for, what business problem it solves, success criteria (conversion >= 3%, load time under 2 seconds).

User Roles and Permissions

Example access matrix:

Functional Requirements

Each requirement in the format: ID, description, preconditions, result, exceptions.

FR-001: User registration

• Preconditions: user not authenticated, email not registered.
• Scenario: enter email, password, name; validation; create account with status 'not confirmed'; send email with link (TTL 24 hours).
• Exceptions: email already in use → error; invalid email → error; send failure → retry queue.
• Result: record in users table, record in email queue.

Non-functional Requirements

• Performance: 95th percentile API < 300 ms at 100 rps; FCP < 1.5 s, LCP < 2.5 s; JS bundle < 150 KB gzipped.
• Reliability: uptime 99.5%, recovery < 15 minutes, daily backups with 30-day retention.
• Security: JWT (TTL 1 hour + refresh 30 days), bcrypt (cost 12), rate limiting, HTTPS, HSTS, parameterized queries, CSP.

Integrations

• Payment: YooKassa (API v3: card, SBP, YooMoney), webhook with HMAC-SHA256.
• Shipping: CDEK (calculation, waybill creation, tracking).
• Email: SendGrid (transactional: confirmation, recovery, status updates).
• Analytics: Yandex.Metrica + GA4 with e-commerce.

Data Schema

users (1) --- (N) orders
orders (1) --- (N) order_items
order_items (N) --- (1) products
products (N) --- (1) categories
products (1) --- (N) product_images
users (1) --- (1) carts
carts (1) --- (N) cart_items

Environments and Deployment

• development: Docker Compose locally
• staging: staging server for pre-release testing
• production: live server
• CI/CD: develop → auto-deploy to staging, main → manual deploy to production with mandatory test run
• Monitoring: UptimeRobot (every minute), Sentry (errors), Grafana + Prometheus (metrics)

Acceptance Criteria

• Pagination (20 products per page)
• Filtering by category, price, availability
• Sorting by price, novelty, popularity
• Full-text search from 3 characters
• Product card: photo, description, price, 'Add to cart' button
• Breadcrumbs with BreadcrumbList microdata
• LCP < 2.5 s on mobile (Lighthouse)
• Adding a non-existent product returns 404

Timeline for TS Preparation

For a medium-sized e-commerce site (50–100 screens), drafting a TS takes 2–3 weeks. This includes analytical interviews, scenario writing, non-functional requirements, and technical review. The cost is calculated individually — depends on complexity and number of integrations.

Typical Mistakes When Drafting a TS

• Describing the interface instead of behavior
• Skipping non-functional requirements
• Using vague phrases without numbers
• Not specifying exceptions and edge cases
• Storing the TS in Word without versioning

What's Included in the Work

1. Full audit of the current state (if a prototype or previous documentation exists)
2. Interviews with key client employees (up to 5 people)
3. Formation of functional and non-functional requirements
4. Description of integrations and data schema
5. Creation of role and permission matrix
6. Agreement and recording of acceptance criteria
7. Versioning in Git with change history
8. Final review with the development team

We guarantee transparency at every stage. Order a technical specification — we start with an audit. Get a consultation for your project.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.