We write the same code whether or not you're watching.
No spaghetti for demos, no tech debt handed off quietly. Every codebase ships with tests, documentation, and a runbook. Your team takes over without a two-week offboarding call.
Currently accepting new clients for Q2 2026
Systems built to
run in production,
not just demos.
We design and ship AI systems, backend infrastructure, and data pipelines that engineering teams actually want to maintain. No bloat. No shortcuts.
What We Build
Engineered for
production scale.
Five service areas. Each one staffed by specialists who have shipped these systems in real environments — not just designed them in theory.
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01
AI & Machine Learning
From model selection to inference optimization, we build ML systems designed for real throughput — not notebooks. We handle training pipelines, model serving, monitoring, and continuous retraining at production latencies.
- LLM fine-tuning & RAG system architecture
- Predictive models: classification, regression, anomaly detection
- MLOps: experiment tracking, model registry, drift monitoring
- Computer vision pipelines & NLP systems
- GPU-optimized inference with Triton / vLLM
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02
Custom Software Development
Full-stack product engineering for teams that need software built correctly from the start. We architect, build, test, and deploy — then hand over clean, documented code your team can own.
- SaaS platform development (web + mobile)
- Internal tooling & workflow automation
- Architecture design & technical roadmapping
- Code audits & legacy system modernisation
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03
Backend & API Development
High-throughput API systems designed around your data model — not around a framework's opinions. We build for correctness, observability, and the team that will debug it at 2 AM.
- REST & GraphQL API design, versioning strategy
- Microservices & event-driven architecture
- Real-time systems with WebSockets & message queues
- Authentication, rate-limiting, and security hardening
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04
Cloud & DevOps
Infrastructure that disappears into the background — resilient, observable, and exactly as complex as it needs to be. We set up environments your team can operate without a dedicated platform engineer.
- AWS / GCP / Azure architecture & cost optimisation
- CI/CD pipelines, GitOps, zero-downtime deployments
- Kubernetes cluster setup & Helm charts
- Observability: tracing, alerting, SLO dashboards
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05
Data Engineering & Automation
Data infrastructure that makes your analytics team independent — and your ML team unblocked. We build the pipes that move, transform, and store data at whatever volume you're dealing with.
- ETL / ELT pipelines with Airflow, dbt, Spark
- Data warehouse design (Snowflake, BigQuery, Redshift)
- Streaming data with Kafka & Flink
- Business process automation & workflow orchestration
Why Snapwork
Engineering depth,
not just headcount.
Most agencies optimise for hours billed. We optimise for systems that don't wake you up at 3 AM. Our engineers have built at scale — in production — and that experience shapes every architecture decision we make.
40+
Production systems shipped
8
Years average eng experience
99.7%
Uptime across client deployments
3×
Average execution speed vs. in-house
Specialists, not a single full-stack generalist.
ML work is handled by engineers who've trained models on real datasets. Infrastructure is set up by people who've debugged production clusters at 2 AM. The right specialist for each layer of your stack.
We challenge the brief before we start building.
If the architecture you've sketched will cause problems at scale, we'll say so in week one — not month six. You're hiring us for judgment, not just execution.
Fixed-scope engagements with clear deliverables.
No open-ended retainers with unclear output. Each engagement has a defined scope, timeline, and acceptance criteria. You know what you're getting before work begins.
Delivery Model
How we work.
Four phases, zero ambiguity. Each step has a defined output and a clear handoff before the next one starts.
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Discover
Technical deep dive
We review your existing stack, understand your data flows, and identify constraints before writing a single line of code. Output: a written architecture proposal you can take elsewhere if needed.
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Design
System architecture
Data models, service boundaries, API contracts, infra diagrams — all agreed upon before build starts. We don't improvise architecture mid-sprint.
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Build
Iterative delivery
Two-week sprints with shippable increments. You see real progress, not status updates. Each sprint closes with a demo and a decision on what's next.
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Scale
Production handoff
Load testing, runbook creation, monitoring setup, and a structured handoff. Optional ongoing support contracts for teams who want us on call.
Stack
- Python
- TypeScript
- Rust
- Go
- Kubernetes
- AWS
- GCP
- Terraform
- Kafka
- PyTorch
- PostgreSQL
- dbt
- Airflow
- FastAPI
Case Study
What shipped
looks like.
One real project. The problem we were handed, the decisions we made, and the numbers at the end.
Real-time risk scoring engine for a fintech lending platform
Kova Finance
The Problem
Kova's underwriting team was running credit risk decisions through a rule-based engine with 17 hand-coded conditions. It had a 34% false-positive rate on high-risk loans and required manual review for 60% of applications — creating a 48-hour approval window that was killing conversion.
What We Built
A gradient-boosted risk model trained on 3 years of repayment history, served via a low-latency FastAPI service at p99 < 80ms. Feature engineering pipeline in dbt, real-time inference with ONNX, and a shadow-mode deployment running alongside the legacy system for four weeks before cutover.
What Changed
Manual review queue dropped by 73%. Approval time went from 48 hours to under 4 minutes for 91% of applications. The model runs on a single c6i.xlarge instance at $310/month, replacing an outsourced bureau check costing $28 per query.