Build work
Systems built across Java, React and Python
Senforge Core builds complete systems: Java and Python backends, React and Next.js frontends, and the AI pipelines that sit behind them. The same engineering discipline at every layer.
What we build
Production systems, not prototypes
Three kinds of build, and the same expectation behind each one: it runs the business after we hand it over.
- 01
Full-Stack SaaS Platforms
End-to-end SaaS product engineering. Java or Python backends, React/Next.js frontends, multi-tenant architecture, subscription billing, and the API layer that scales with your customer base.
- 03
Enterprise Applications
Mission-critical enterprise systems with Java or Python backends and React frontends. Designed for the reliability, security, and auditability that regulated industries demand. Built for the long run.
- 04
High-Performance Backend Infrastructure
When your system needs to handle serious throughput. We architect the backend infrastructure layer — queues, caches, compute, storage — across Java Spring services and Python async workloads.
The difference
Architecture first, stack agnostic
Most shops start with the code. We design the system first, then write the code that realizes the design in Java, Python or TypeScript. That order is what keeps the system working three years later.
We treat software as infrastructure: clean domain models, written decisions and code that explains itself, applied the same way at every layer of the stack.
Domain-driven design
We model the business logic before writing a line of code.
API-first development
Contract-first APIs with full OpenAPI documentation.
Observability built in
Metrics, tracing and logging from day one, not added after an incident.
Test coverage as a deliverable
Integration and contract tests that give you the confidence to deploy.
AI systems
Python AI systems that run in production
We build the retrieval, agent and automation layers in Python and wire them into the system that already runs the business. RAG pipelines that read your own domain, agents that reason through multi-step workflows, and automation that runs in production rather than in a demo.
RAG pipelines
Retrieval over your technical documentation, codebase and domain knowledge, built with LangChain, LlamaIndex or custom vector search. Custom embedding models, hybrid dense and sparse search, multi-modal document ingestion and context-aware chunking.
AI agent systems
Agents that reason through multi-step workflows, call tools and make decisions, built with LangGraph, CrewAI or custom orchestration. Function calling, tool use, error handling and state management, with guardrails and validation layers.
Automation backends
Python services that take over repetitive engineering work: data pipelines, API integrations, scheduled jobs and background processing. FastAPI, Celery, Redis and PostgreSQL, including ETL and transformation pipelines, third-party API orchestration and event-driven workers.
ML model integration
Open-source or proprietary models fitted into production systems: model serving, inference optimization, API wrapping and monitoring. PyTorch, TensorFlow, ONNX or cloud AI services, with A/B testing frameworks behind them.
The Python AI stack
Frameworks
- LangChain
- LangGraph
- LlamaIndex
- CrewAI
Models and APIs
- OpenAI
- Anthropic
- Cohere
- HuggingFace
Vector databases
- Pinecone
- Weaviate
- Chroma
- Qdrant
Backend
- FastAPI
- Celery
- Redis
- PostgreSQL
Applications
What teams use this for
Six patterns that come up most often, each one starting from work a person is doing by hand today.
Technical documentation search
An engineer asks a question in plain language and the retrieval layer returns the exact passage from internal wikis, API docs and runbooks.
Code review automation
Agents read pull requests for security issues, performance problems and style violations before a human opens them.
Customer support triage
Agents classify support tickets, extract the key information and route each one to the right team with the context attached.
Data pipeline orchestration
Automated ETL that extracts from several sources, transforms and validates the records, then loads them into the data warehouse.
Compliance document processing
Structured data pulled out of unstructured legal or medical documents, with the issues flagged for human review.
Reporting dashboards
Background workers aggregate data from several APIs, process the metrics and push them to live dashboards.
Process
Eight weeks from kickoff to production
The schedule below is the usual shape of an AI build. Scoping starts before week one and the handover continues after week eight.
- Week 1 and 2
Discovery and architecture
The first two weeks go on the problem, the data sources and the definition of success. The architecture is designed before any code is written.
- Week 3 to 6
Build and integration
The core system is built, wired into your infrastructure and tested against the key workflows.
- Week 7 and 8
Production hardening
Monitoring, error handling, rate limiting and edge case coverage go in before the system goes live.
Most AI projects go from kickoff to production in six to eight weeks. Larger systems take ten to twelve. A retainer keeps the work going after the first release, for teams that want to expand the system as it proves itself.
Start here
Tell us what the system has to do
The workflow, the constraint and the deadline are enough to start. We will come back with an architecture and a plan for building it.