Real-time voice platform for scalable decision-making
- 300+
- users in real-time audio discussions
Dedicated Backend Development Team
Backend engineers who design APIs that scale, databases that don't leak, and integrations that hold in production.
Slow APIs, brittle integrations, and databases nobody wants to touch are engineering debt that compounds. We embed senior backend engineers, Node.js, Python, Go, PostgreSQL, GraphQL, directly into your team. They own the server-side architecture, not just the tickets. Data models designed for the query patterns you actually have. APIs built to handle the scale you're heading toward. Integrations that work in production, not just in the demo.
Node.js, Python, Go, Django, PostgreSQL, Redis, GraphQL, matched to your existing stack
API design with versioning, rate limiting, and authentication built in from the start
Database schema design and query optimization, not just ORM defaults
Third-party integrations with Stripe, Twilio, Salesforce, HubSpot, and custom APIs
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Are your API response times causing frontend performance problems you're blaming on the wrong layer?
How many integrations in your system were built to demo, not to scale?
Short answer
RaftLabs embeds dedicated backend engineers working in Node.js, Python, Go, Django, PostgreSQL, MySQL, Redis, and GraphQL. Engineers join the client's existing team and own API design, database architecture, third-party integrations, and server-side performance. Engagements start within one week and bill at a fixed monthly rate per engineer, quoted firm after a scoping call.
Key takeaways
- Dedicated backend engineers work in Node.js, Python, Go, Django, PostgreSQL, Redis, and GraphQL matched to your existing stack.
- Engagements start within one week and bill at a fixed monthly rate per engineer, with no long lock-in.
- A senior backend engineer is $6,000 to $6,500 per month, quoted firm after a scoping call.
- Engineers own API design, database architecture, third-party integrations, and server-side performance from day one.
- Third-party integrations include Stripe, Twilio, Salesforce, HubSpot, Xero, QuickBooks, and custom APIs with idempotency and retry logic built in.
- Database schema design is based on actual query patterns with EXPLAIN ANALYZE-backed index selection, not ORM defaults.
Trusted by
Backend systems accumulate technical debt in predictable ways. APIs designed for a single consumer grow into a dozen. Databases tuned for 1,000 rows now hold 10 million. Integrations built for the demo path fail on every edge case in production. Developers already lose an average of 17.3 hours a week to maintenance and bad code (Stripe and Harris Poll, 2018), so unowned backend debt is not a someday problem. That debt is a weekly tax. The engineers we embed have worked in these codebases and know where the debt hides before they read a line of it.
Embedding a senior backend engineer changes the quality of decisions being made at the code level. API contracts that third parties won't regret. Database indexes that serve the actual query patterns. Integrations built with idempotency keys and retry budgets. Those choices are cheap to make upfront and expensive to retrofit.
According to Gartner's March 2024 analysis, more than 30% of the increase in API demand will come from AI and tools using large language models by 2026. For engineering teams, that means the API surface you design today must handle integration patterns that didn't exist six months ago, which is exactly why contracts, versioning, and rate-limit semantics matter before the first client connects.
What we deliver
How embedded backend engineers work
- 01API design and development
Deliberate API design: resource-oriented URLs, correct HTTP verb semantics, consistent error formats, and a versioning strategy agreed before the first client integrates. Specs are written contract-first so SDKs and route handlers never drift, with authentication, rate limiting, and object-level authorization enforced on every endpoint from the start, not retrofitted after an audit.
- Built with
- REST · GraphQL · OpenAPI · OWASP API Top 10
- 02Database architecture and optimization
Schema design based on the query patterns the application actually needs, with partial, composite, and covering indexes chosen from evidence rather than guesses. ORM configuration avoids silent N+1 queries, read replicas take reporting load off the primary, and reversible, version-controlled migrations make rollback possible without manual intervention.
- Built with
- PostgreSQL · EXPLAIN ANALYZE · PgBouncer
- 03Third-party integrations
Payment, communications, CRM, and accounting integrations built with the same reliability primitives: idempotency keys so webhook retries never double-charge, retry with exponential backoff, and dead letter queues so failed events are inspectable, not silently dropped. HMAC signature verification secures every webhook endpoint, and monitoring separates upstream provider degradation from bugs in the integration logic.
- Built with
- Stripe · PayPal · Twilio · Salesforce · HubSpot · Xero
- 04Performance, caching, and scaling
Caching for data requested constantly but changed rarely, invalidated on write rather than by stale expiry, with stale-while-revalidate to avoid thundering herds. Background jobs run with concurrency limits and retries, and failure alerting reaches whoever holds the pager. Application servers stay stateless, so you add capacity by adding instances, and query stats show what to fix before you buy hardware. The latency budget we design each endpoint to is p50 under 100ms and p95 under 400ms, verified under load before it ships rather than asserted after.
- Built with
- Redis · BullMQ · pg_stat_statements
How an embedded backend engineer owns the system
An embedded engineer is accountable for outcomes, not tickets. That means a clear ownership boundary, service-level objectives you can hold them to, an on-call posture for the systems they build, and a handover that survives their rotation off the team.
- Ownership model
- The engineer owns a defined slice of the backend end to end, an API surface, a data domain, or an integration, from design and code through review, deploy, and the pager for it. They work inside your existing repo, branching model, and review process, never a parallel one.
- SLOs and error budgets
- We agree the numbers that matter before code gets written: latency targets, an acceptable error rate, and an error budget that decides when to ship features versus stop and harden. p50 under 100ms and p95 under 400ms is the default budget we design to; yours may be tighter.
- On-call and incidents
- Integrations and background jobs are built to be observable and recoverable, with idempotency keys, dead letter queues, retry budgets, and alerting that tells an upstream provider outage apart from a bug in our code. Each service ships with a runbook, so a 2am page has an answer.
- Handover
- Every decision lands in the codebase and a written runbook, not in one person's head. When an engineer rotates off, the next one reads the service topology from the docs and contributes without a multi-week ramp.
What a latency budget looks like under real load
- p95 347ms
- at 1,000 concurrent users, 100% request success
- RaftLabs load test, Voter IQ real-time platform (Artillery.io + k6)
- 7,000-8,000
- concurrent users sustained on a single instance
- Load-tested ceiling on one m5a.large EC2 (~$38/mo)
- 60,000-70,000
- documented, costed horizontal scale-out path
- Voter IQ capacity plan, not a live usage figure
These figures come from load-testing Voter IQ, a real-time audio platform we shipped in 16 weeks against an election deadline. We ran the tests with Artillery.io and k6, found the honest ceiling, and gave the client a costed path to grow rather than a confident guess. Latency targets on your build get the same treatment: measured under load, not asserted on a slide.
Need backend engineers embedded in your team?
Tell us what your stack looks like, where performance or reliability is breaking down, and what you're trying to build. We'll match you with the right engineers and get them started within a week.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreFrequently asked questions
Node.js with Express or Fastify for high-concurrency APIs. Python with FastAPI or Django for data-heavy applications and ML integrations. Go for performance-critical services where latency matters. We use PostgreSQL as our default relational database and Redis for caching and queuing. For GraphQL: Apollo Server or Hasura depending on the use case. We match to whatever you're already running or recommend based on your performance and team requirements.
Yes. We've built integrations with Stripe, PayPal, Twilio, SendGrid, Salesforce, HubSpot, Shopify, Xero, QuickBooks, AWS services, and many custom APIs. Integration work involves more than calling an API, it means webhook handling, idempotency for payment events, retry logic for failed requests, and monitoring that tells you when an upstream service is degraded. We build integrations that are observable and recoverable, not just connected.
For new systems: we start with the query patterns the application actually needs, then design the schema to serve those patterns efficiently. We don't normalize for normalization's sake if it creates joins that hurt read performance. For existing systems with performance problems: we run EXPLAIN ANALYZE on slow queries, check for missing indexes, identify N+1 patterns in the ORM, and review schema decisions that made sense at 1,000 rows but not at 10 million. We produce a written audit before making changes.
A senior backend engineer runs $6,000 to $6,500 per month, quoted firm after a scoping call rather than off a rate card. A lean delivery pod (one senior engineer plus part-time PM and QA) typically starts at $12,000 to $15,000 per month. Rate depends on seniority and specialization, a Node.js generalist versus a distributed-systems specialist. Engagements bill at a fixed monthly rate with no long lock-in, and we quote after a short call to understand your stack, team size, and what you need the engineer to own.
Yes. Engineers we embed have experience working across multiple services, including event-driven architectures, message queues (Kafka, SQS, RabbitMQ), and distributed systems with consistency requirements. They can join a system mid-flight, understand the service topology from the codebase and runbooks, and contribute without a multi-week onboarding period.
Embedded engineers follow your existing code review process and branching strategy, they don't introduce a parallel workflow. If you have no review standards, they will establish minimal conventions in the first week: PR size limits, required reviewers, and a simple checklist. Engineers are expected to write code that passes review on the first attempt, not use review rounds as a bug-finding mechanism.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Dedicated Backend Development Team in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.