API Design and Development
REST and GraphQL APIs with clear contracts, versioning, and documentation, built in Python, Node.js, or Go depending on what your system needs.
010Backend Service Provider
Backend Service Provider Company for Serious Systems
Webisoft designs and builds the backends your product runs on: APIs, data layers, integrations, and the infrastructure underneath. A senior Montreal engineering team for companies where downtime and bad data are not acceptable.
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What we build
Backend engineering, end to end
The backend is where your business logic, your data, and your uptime actually live. We build that layer so it stays fast and correct as the product grows.
Data Architecture
Schema design, PostgreSQL and other stores chosen for the workload, migrations, and query performance work that holds up past the first million rows.System Integrations
Reliable connections to payment providers, CRMs, ERPs, and third-party APIs, with retries, idempotency, and failure handling designed in, not bolted on.Cloud Infrastructure
AWS and GCP environments defined as code, with CI/CD pipelines, sensible environments, and cost visibility from the first deployment.Performance and Scale
Profiling, caching strategy, queueing, and load testing that fix the actual bottleneck instead of adding servers to hide it.Legacy Modernization
Incremental migration of aging backends to maintainable architectures, keeping the business running while the risk comes down release by release.
How we work
How a backend engagement runs
(4)- 1
Technical Discovery
We review your current system, data flows, and pain points, then deliver an architecture proposal with explicit trade-offs and a scoped estimate. - 2
Foundation Sprint
Environments, CI/CD, testing, and the core service skeleton go in first, so every feature after that ships on rails instead of on hope. - 3
Iterative Delivery
Features ship in short cycles with automated tests and weekly demos. You see working endpoints and real data, not slide decks. - 4
Handover or Ongoing Ownership
We document the system, train your team, and either hand it over cleanly or stay on for operations, monitoring, and continued development.
Why Webisoft
A backend partner, not a body shop
Plenty of vendors can staff a backend project. Fewer can be accountable for whether the system is still fast, secure, and maintainable two years in.
Senior North American Team
Experienced engineers in Montreal design and build your system directly, in your time zone, without layers of intermediaries.Engineering Led Decisions
Architecture choices, estimates, and scope calls are made by the people writing the code, which keeps promises aligned with reality.Security Minded by Default
Authentication, authorization, secrets handling, and data protection are part of the base build, not a hardening phase that gets cut.Built for the Next Team
Tests, documentation, and boring, explicit code. Any competent engineer should be able to pick up the system we leave behind.
FAQ
Common questions about backend engagements
(4)- The main cost drivers are the number of integrations, data complexity, and uptime requirements. A single-service API for an MVP is a very different engagement from a multi-service platform with strict SLAs. A discovery phase that produces a scoped estimate before the build starts protects the budget, because it replaces open-ended hourly billing with a defined deliverable and price.
- Python, Node.js, and Go are common choices on the service side, PostgreSQL is a frequent default for the database, and AWS or GCP for infrastructure. The right stack depends on the workload, the existing team's skills, and hiring reality in the company's market. A technology chosen because engineers are easy to find for it often beats a technically fancier option nobody local can maintain.
- Yes, and it is a common arrangement. An external team can own the backend while the internal team owns the frontend, embed alongside in-house engineers, or lead the architecture while the internal team builds. Whatever the split, shared code review and common standards are what keep the codebase coherent across both groups.
- It usually begins with a technical review of the product and the current state of the system, followed by a proposed approach and a discovery phase. Discovery commonly runs two to three weeks and ends with an architecture plan, a scoped backlog, and an estimate. A good discovery output is executable by any competent team, which keeps the client in control rather than locked in.
Backend capabilities
What separates a backend that lasts from one that limps
The backend is where reliability, speed, and cost are decided. These are the capabilities that matter when the system has real users, real load, and a team that needs to keep shipping on top of it.
API design and versioning
REST and GraphQL APIs specified with OpenAPI, designed with pagination, idempotency, and versioning rules from the first endpoint. Getting these conventions right early is what lets mobile apps, partners, and future services build against the API without a breaking-change fire drill every quarter.Database architecture
PostgreSQL as the default, with schema design, indexing strategy, and query review treated as core engineering rather than an afterthought. Most production slowdowns trace back to the data layer, so we profile query plans under realistic data volumes before launch, not after the first outage.Asynchronous processing
Queues and workers with Celery, BullMQ, or cloud-native equivalents for everything that does not need to happen inside a web request: emails, exports, billing runs, model inference. Moving slow work off the request path is the single cheapest way to keep response times flat as volume grows.Caching and performance
Redis caching, HTTP cache headers, and CDN configuration applied where measurement shows they pay off, with cache invalidation rules written down because stale data bugs are the hardest to debug. We set explicit latency targets per endpoint and load test against them before go-live.Security and access control
Authentication with OAuth 2.0 or OpenID Connect, role-based authorization enforced at the API layer, encrypted data at rest and in transit, and secrets kept out of code. Designs account for requirements like SOC 2, GDPR, or Quebec's Law 25 where they apply, so compliance work later is documentation, not rearchitecture.Observability and operations
Structured logs, metrics, tracing, and health checks wired in from the first deployment, with dashboards that show error rates and latency per endpoint. When something breaks at 2 a.m., the difference between a five-minute fix and a five-hour one is whether the system can explain itself.
Engagement flow
How we build or take over a backend
(4)- 1
Requirements and load profile
We start by pinning down what the backend must actually do: expected request volumes, data growth, integration points, and compliance constraints. This produces a short architecture document with technology choices justified against those numbers, not against fashion. - 2
Core architecture first
The first sprints deliver the data model, authentication, and one or two critical endpoints deployed through a full CI/CD pipeline to a staging environment. Proving the skeleton end to end early means every later feature lands on rails that already work. - 3
Feature delivery with load testing
Endpoints ship in one or two week increments, each with automated tests and API documentation, and we run load tests at meaningful milestones rather than saving performance for the end. Frontend or mobile teams get a stable contract to build against from the first weeks. - 4
Production hardening and handover
Before launch we complete security review, backup and restore drills, monitoring, and runbooks for the failures we can foresee. Handover includes architecture documentation and working sessions with your engineers, and you keep full ownership of code, infrastructure, and accounts.
FAQ
Common questions about backend development
(6)- The deciding factors are hiring pool, ecosystem maturity, and fit with the workload, not raw benchmark numbers. Python, Node.js with TypeScript, Go, and Java or C# all handle the vast majority of business workloads well, so the differentiator is which one the team can hire for and maintain over five years. Exotic choices carry a hidden tax: every future hire, library gap, and debugging session costs more. A reasonable default is the most boring stack that comfortably meets the performance requirement.
- A focused API for a single product typically runs from tens of thousands of dollars, while platforms with many integrations, strict compliance, or high-availability requirements reach well beyond that. The main drivers are the number of integrations, data migration from legacy systems, compliance scope, and uptime targets, since each extra nine of availability adds real architecture and infrastructure cost. Ongoing cloud and maintenance spend should be budgeted from the start, as it commonly reaches 15 to 20 percent of build cost per year.
- For most new systems, a well-structured monolith is the right starting point: one deployable, one database, clear module boundaries inside. Microservices buy independent scaling and team autonomy but charge for it in distributed debugging, data consistency work, and infrastructure overhead, which small teams feel immediately. The pragmatic path is a modular monolith that extracts a service only when a specific module has a proven scaling or ownership reason, and the extraction is straightforward because the boundaries already exist.
- Scalability comes from a handful of concrete practices: stateless application servers behind a load balancer, a database with sound indexing and a read-replica path, queues for slow work, and caching for hot reads. Load testing against a realistic traffic model matters more than any single technology, because it reveals the actual first bottleneck, which is rarely where intuition says. Premature scaling architecture is its own risk, since complexity added for traffic that never arrives slows every feature in the meantime.
- The non-negotiables are TLS everywhere, hashed and salted credentials, token-based authentication with short expiry, authorization checks on every endpoint rather than only in the UI, input validation against injection, and rate limiting. Secrets belong in a vault, dependencies need automated vulnerability scanning, and every data mutation should leave an audit trail. Regulatory context adds specifics, GDPR and Quebec's Law 25 for personal data, PCI DSS if cards are touched, and it is far cheaper to design for these up front than to retrofit them.
- A real handover has four ingredients: documentation that covers architecture decisions and operational runbooks, not just code comments, working sessions where internal engineers deploy and debug with the original builders watching, full transfer of accounts and credentials, and a support window for questions after the switch. A useful acceptance test is whether the internal team can deploy a small change and resolve a simulated incident without outside help. If a vendor resists any of this, treat it as a lock-in signal and address it contractually before the build starts.
