Backend systems, APIs, and AI engineering built by the team behind procedure.tech, Treebo, Last9, and KredX.
Trusted by engineering teams at
REST and GraphQL APIs built with Django, FastAPI, or Flask. We pick the framework based on your workload: Django for full-stack apps with admin panels and auth, FastAPI for high-concurrency APIs and AI-serving endpoints, Flask for lightweight services and serverless functions. Not a one-size-fits-all decision.
Production ML pipelines, LLM integrations, and AI-powered features. We build with PyTorch, scikit-learn, and LangChain, then deploy on AWS SageMaker or self-hosted infrastructure. Model training is one thing. Getting it into production with monitoring, versioning, and rollback is what most teams struggle with.
ETL pipelines, data warehousing, and real-time processing with Apache Airflow, Celery, and Pandas. We build the data infrastructure that feeds your dashboards, ML models, and reporting. Clean data in, usable insights out. No 'data lake' that nobody can query.
Process automation, workflow orchestration, and system integration. Python scripts that replace manual work your team does every week. We have automated reporting pipelines, compliance checks, and multi-system data syncs that saved teams 20+ hours per week.
Migrating PHP, Ruby, or Java backends to modern Python. We do this incrementally - running old and new systems in parallel, migrating service by service. No big-bang rewrites. Typical migration: 3-6 months depending on system complexity.
Python backend paired with React, Next.js, or Angular frontend. One team, shared TypeScript types, unified deployment. Most Python projects need a frontend. We staff both sides instead of making you coordinate two vendors.
Python's strength is data. We design systems around data flows, transformation pipelines, and storage patterns first. Application logic wraps around the data model, not the other way around.
Training a model is 10% of the work. We focus on the 90% that matters: serving infrastructure, monitoring drift, versioning models, handling failures gracefully, and retraining pipelines that run without manual intervention.
Python rewards readability. We write code that junior engineers can understand and senior engineers can extend. Clever one-liners become maintenance burdens. Clear, explicit code compounds in value over time.
Python scripts that replace manual work should be treated as infrastructure: tested, monitored, and documented. We build automation that teams can rely on, not fragile scripts that break when someone changes a spreadsheet column.
Django, FastAPI, and Flask each solve different problems. We pick based on your workload and team, not our preferences. Sometimes the answer is all three in the same system, each handling the workload it was designed for.
We map your requirements, data flows, and integration points. You get a technical proposal covering framework selection (Django vs FastAPI vs Flask), database design, API architecture, hosting recommendation, and CI/CD setup. No code until the architecture makes sense.
API contracts documented in OpenAPI spec before a single endpoint is built. Database schema designed, relationships mapped, migration strategy defined. If there is an ML component, we define the model serving architecture here.
Sprint-based delivery with working endpoints shipped every two weeks. Automated tests written alongside features. Your team gets staging access from week one. For AI/ML projects, model training runs in parallel with API development.
Production traffic simulation, bottleneck identification, query optimization, caching layers, rate limiting. For ML endpoints: latency benchmarking, model warm-up strategies, and fallback handling. Nothing ships until it handles your expected load.
Complete documentation, architecture decision records, and runbooks. For ML projects: model retraining guides and monitoring dashboards. Your team owns the system. Optional support retainer for ongoing work, but no lock-in.
Engineers with 3+ years building production Python systems in Django, FastAPI, or Flask. Full backend capability spanning API design, data engineering, and cloud infrastructure. AI/ML experience available.
A self-contained team that owns full API or service delivery. Architecture, implementation, testing, and deployment covered. Mixed team of leads and mid-level engineers staffed based on project needs.
Fixed-scope engagement with clear deliverables, timeline, and budget. Defined API contracts, agreed milestones, and transparent pricing.
Talk directly with engineers, not sales. We will assess fit and give honest next steps.
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It depends on scope. An API backend for a web or mobile app with 10-20 endpoints typically runs $15,000 to $40,000. A data platform with ETL pipelines and dashboards sits in the $40,000 to $120,000 range. AI/ML systems with custom model training, serving infrastructure, and monitoring can run $80,000 to $250,000 or more. Our architecture consultation is free and scopes your specific project.