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Developer community · Enterprise Solution Provider

One community to build, run and support your

ENTSOLPRO brings together engineers with 20+ years of experience building enterprise-grade products for clients around the world. We deliver every layer of enterprise software - from portals and ERP to RAG platforms, fine-tuned models and long-term support - and build our own native products in Rust, Java and Python for companies across industries.

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New Termspli - our native terminal workspace is here
Years of experience
0+
Service domains
0
AI tracks
0
Native languages
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runtime.trace live request flow
User query AI Agent plan · route Retrieval hybrid + rerank Vector DB embeddings LLM grounded gen Response cited request tool call k-NN search context answer
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01 Services

Every layer of enterprise software, one community.

Ten engineering practices that compose into one delivery team. Start with a single system - or hand us the whole estate, from web front ends to ERP, AI and round-the-clock support.

S-01

Enterprise Software Engineering & Support

Custom platforms, modernisation of legacy systems and long-term support for the software your business already runs on - owned end to end by one accountable team.

  • Greenfield platforms and internal tools
  • Legacy modernisation and cloud migration
  • L2/L3 application support with agreed SLAs
  • Security patching, upgrades and performance tuning
  • Code audits, documentation and team handover
  • Dedicated squads that scale with your roadmap
JavaPythonRust.NETSLA support
S-02

Web Platforms & Portals

Every kind of web build - corporate sites, customer portals, SaaS products, e-commerce and CMS.

  • Web apps, PWAs and dashboards
  • E-commerce and payment flows
  • Headless CMS and multilingual sites
  • Accessibility, SEO and Core Web Vitals
ReactNext.jsNode.js
S-03

Mobile & Desktop Applications

Field, sales and operations apps that work offline and sync safely with your back office.

  • iOS, Android and cross-platform apps
  • Native desktop tools for operators
  • Offline-first sync and device management
  • App-store release pipelines
KotlinSwiftFlutter
S-04

RAG & Enterprise Search

Answers from your documents, tickets and wikis - with citations, permissions and evals.

  • Ingestion, chunking & embeddings
  • Hybrid search with reranking
  • Row-level permissions per tenant
  • Grounding and hallucination guards
LangChainVector DBReranking
S-05

AI Agents

Goal-driven agents that use tools, call your APIs and complete multi-step work under supervision.

  • Tool calling against internal services
  • Stateful graphs with LangGraph
  • Human-in-the-loop approval gates
  • Full trace logging and replay
LangGraphTool useGuardrails
S-06

Generative AI & Fine-tuning

Production LLM features and custom models tuned on your data - wrapped in real product surfaces.

  • Structured outputs and streaming UX
  • LoRA / QLoRA fine-tuning
  • Model routing, caching, cost controls
  • Evaluation sets and quality gates
TransformersPEFTvLLM
S-07

AI & Process Automation

Back-office workflows that run themselves: intake, triage, routing, reconciliation, reporting.

  • Document & email intake pipelines
  • Rules + model hybrid decisioning
  • Queues, retries, dead-letter handling
  • Exception dashboards for operators
WorkflowsOCRQueues
S-08

ERP & CRM Solutions

Modular ERP and CRM for inventory, sales, purchasing, finance and HR - built to your process.

  • Master data, ledgers and audit trails
  • Role-based approval chains
  • Reporting, exports and BI feeds
  • AI copilots over live ERP data
ModularAuditableMulti-role
S-09

API & Enterprise Integration

Connecting the systems you already run - ERP, CRM, warehouses, payments and legacy services.

  • REST, GraphQL and event-driven APIs
  • Schema mapping and data contracts
  • Idempotency, retries, reconciliation
  • Legacy adapters and migration paths
RESTKafkaWebhooks
S-10

Cloud, DevOps & SRE

Infrastructure as code, CI/CD and observability on AWS, Azure or GCP - with cost under control.

  • Kubernetes and container platforms
  • GitHub Actions release pipelines
  • Prometheus & Grafana monitoring
  • IAM, governance and FinOps reviews
DockerKubernetesTerraform

Not sure where to start?

Most problems begin as “our knowledge is unusable” or “this process is manual”. We map yours to an architecture in a first call.

Talk to an engineer

02 Advanced AI engineering

From research notebook to governed production AI.

Five tracks cover the full AI lifecycle - model development, LLM applications, fine-tuning, MLOps and the enterprise cloud architecture that keeps it all secure and affordable. Select a track to see how we build it.

model.forward()

Core AI development

Custom models for classification, forecasting, vision and language - trained in PyTorch, built on Hugging Face Transformers or classic scikit-learn where that is the better tool, and exposed as typed FastAPI services your systems can call.

  • Data preparation, feature pipelines and baselines
  • PyTorch training loops and experiment tracking
  • Transformer models for text, vision and audio
  • scikit-learn models for tabular business data
  • FastAPI inference services with OpenAPI contracts
PythonPyTorchHugging Face Transformersscikit-learnFastAPI

03 Native products

We build native products in Rust, Java and Python.

Beyond client services, our community designs and ships its own native products for companies across industries - licensed, white-labelled or tailored to your operation, with source-level support from the engineers who wrote them.

Featured product Available for macOS & Linux · Windows coming soon

Termspli logo

Termspli

The terminal that holds your whole workday

A native terminal with real split panes, built in Rust - plus the tools developers would otherwise open in a dozen windows: a code editor, AI coding assistants, an API client, load testing, Docker, CI/CD, a security checker and an encrypted vault.

  • Split panes
  • AI agents
  • Code editor
  • API client
  • Load testing
  • Docker
  • CI/CD
  • Encrypted vault
Explore Termspli Request download
ingest-engine/src/main.rs rust
Rs

Rust products

Memory-safe, low-latency software for workloads where every millisecond and megabyte counts.

  • Streaming ingestion and event-processing engines
  • Edge and IoT agents for plants and devices
  • High-performance APIs and gateways
  • WebAssembly modules and CLI tooling
  • Safe replacements for legacy C/C++ components
TokioAxumWebAssembly
Jv

Java products

Transaction-heavy enterprise platforms built for long life, strict audit and large teams.

  • Billing, invoicing and payment platforms
  • ERP and back-office modules
  • Integration hubs and workflow engines
  • Spring Boot microservice suites
  • Native executables with GraalVM
Spring BootKafkaGraalVM
Py

Python products

Data- and AI-driven products that turn operational data into decisions and automation.

  • Forecasting and demand-planning suites
  • Document intelligence and OCR pipelines
  • Analytics and reporting platforms
  • AI assistants over company knowledge
  • Desktop and automation tooling
FastAPIPyTorchpandas

04 Reference architecture

How a grounded answer is actually produced.

A retrieval platform is three pipelines, not one prompt. Hover or focus a stage to trace it through the system.

Ingestion batch · streaming Connectors Parse / OCR Normalise Metadata Index acl-aware Chunking Embeddings Vector store Lexical (BM25) Retrieval hybrid · filtered Query rewrite k-NN search Permission filter Reranker Generation grounded Context builder LLM Citations Guardrails Evaluation & Observability Traces Evals Feedback

note Every box is a seam we can swap - model provider, vector store or reranker - so you are never locked to one vendor's roadmap.

05 Technology stack

A stack chosen for boring reliability.

Seven layers, one graph. Select a layer to isolate it, or hover a node to see what it connects to.

AI & ML

  • PyTorch
  • Transformers
  • scikit-learn
  • LangChain
  • LangGraph
  • PEFT / TRL
  • LLM APIs

Native

  • Rust
  • Java
  • Python
  • Tokio
  • GraalVM

Backend

  • FastAPI
  • Spring Boot
  • Node.js
  • REST APIs
  • Kafka

Data

  • PostgreSQL
  • Vector DB
  • MySQL
  • MongoDB
  • Redis

Frontend

  • React
  • Next.js
  • TypeScript
  • Flutter

MLOps & DevOps

  • Docker
  • Kubernetes
  • GitHub Actions
  • MLflow
  • DVC
  • vLLM
  • Prometheus
  • Grafana

Cloud

  • AWS
  • Azure
  • GCP

06 Selected work

Case studies.

Representative systems drawn from two decades of enterprise delivery - the problem, the architecture and the stack. Client names are withheld; details are shared under NDA on a call.

P-01 RAG platform

Enterprise RAG Platform

Problem
Institutional knowledge scattered across drives, wikis and ticket history; search returned documents, never answers, and staff re-solved the same questions.
Solution
Unified ingestion across sources, ACL-aware hybrid retrieval with reranking, and a grounded answer layer that cites its sources and abstains when evidence is thin.
Technologies
PythonFastAPIPostgreSQLVector DBLangChainRedisDocker
P-02 Agent system

AI Business Agent

Problem
Repetitive operational requests - status lookups, quote drafting, data entry - consumed skilled team hours and moved between four disconnected systems.
Solution
A tool-using agent with scoped API access, deterministic guardrails, human approval before any write, and full trace replay for every decision it makes.
Technologies
PythonLLM tool callingNode.jsRedis queuesREST integrations
P-03 ERP

Intelligent ERP

Problem
Off-the-shelf ERP forced the business to change its process, while spreadsheets quietly became the real system of record for inventory and approvals.
Solution
Modular ERP built around the actual workflow - inventory, purchasing, sales and finance with audit trails, role-based approvals and an AI copilot over live ERP data.
Technologies
FastAPIPostgreSQLReactDockerCI/CD
P-04 Document AI

AI Document Intelligence

Problem
High-volume inbound documents - invoices, contracts, forms - were keyed in by hand, with inconsistent extraction and no reliable audit of what changed.
Solution
Classification and structured extraction with confidence scoring, an exception queue for low-confidence items, and validated writes into downstream systems.
Technologies
PythonOCRLLM extractionMongoDBFastAPI
P-05 Rust product

Streaming Ingestion Engine

Problem
A batch ETL job written in a scripting language could no longer keep up with order and sensor events, and memory spikes caused nightly failures.
Solution
A Rust streaming engine on Tokio that consumes from Kafka, validates and enriches events in parallel stages, and writes to PostgreSQL with back-pressure and exactly-once checkpoints.
Technologies
RustTokioKafkaPostgreSQLPrometheus
P-06 Fine-tuned LLM

Domain-Tuned Support Assistant

Problem
A general-purpose model misread internal product codes and support jargon, and sending tickets to an external API raised data-residency concerns.
Solution
An open-weight model adapted with QLoRA on curated ticket history, served by vLLM on Kubernetes inside the client's cloud, with RAG over the knowledge base and MLflow-tracked releases.
Technologies
TransformersPEFTTRLvLLMKubernetesMLflow

07 Engineering process

How the work runs.

Short cycles, visible artefacts, and a working system in front of you early. No six-month discovery phases.

  1. Stage 01 · week 1

    Discovery & problem framing

    We map the workflow, the data and the failure modes - then write down what "working" means in measurable terms before any code exists.

  2. Stage 02

    Architecture & data design

    System diagram, data contracts, model and store selection, security boundaries, and the seams that keep components replaceable.

  3. Stage 03

    Prototype on real data

    A thin vertical slice against your actual documents and systems. It either holds up under real inputs or the design changes now, not later.

  4. Stage 04

    Build & evaluate

    Iterative delivery with an evaluation harness running alongside - retrieval quality, groundedness and latency tracked as first-class metrics.

  5. Stage 05

    Integrate & harden

    Connection to ERP, CRM and internal services; access control, rate limits, retries, observability and a deployment pipeline you control.

  6. Stage 06 · ongoing

    Operate & improve

    Monitoring, regression suites, model and cost review, and documented handover so your team can own the system outright.

08 Community

A developer community built for enterprise delivery.

ENTSOLPRO is a community of senior engineers, architects and specialists with 20+ years of enterprise experience, working as one team. You get the right expert for every layer of your system - and a single point of accountability.

ENTSOLPRO

Enterprise Solution Provider

Taking on new engagements and members

Our engineers have spent more than two decades building enterprise-level products for clients around the world. Along the way we have worked with renowned multinational corporations and leading software firms, and delivered systems across many industries. ENTSOLPRO brings that experience together: we design, build and support production software - web platforms, ERP, integrations, cloud infrastructure and AI - and build our own native products in Rust, Java and Python. Every engagement is staffed by engineers who specialise in that layer, and led by an architect who stays with you from discovery to long-term support.

  • 0+years building enterprise software
  • Globalclients across many industries
  • MNC& software-firm delivery background

Who you work with

  • Rust & systems engineers
  • Java & JVM architects
  • Python & ML engineers
  • Web & mobile developers
  • Cloud, DevOps & SRE
  • QA & security specialists

Focus areas

  • Enterprise Software
  • Web Platforms
  • ERP & CRM
  • AI Engineering
  • RAG
  • AI Agents
  • Fine-tuning
  • MLOps
  • Cloud Architecture
  • Native Products
Experience
20+ years in enterprise software
Clients
Worldwide, across many industries

Principles

  • Grounded over generative. If a system can't cite it, it shouldn't claim it.
  • Measured, not guessed. Evaluation and monitoring ship with the feature.
  • Replaceable parts. Model, store and cloud stay behind interfaces.
  • Operable by your team. Documentation and handover are deliverables.

Engagement models

  • Architecture review. A focused assessment of an existing system.
  • Project delivery. End-to-end build of a defined platform.
  • Dedicated squad. A cross-functional team inside your roadmap.
  • Managed support. Maintenance, upgrades and incident response under SLA.
  • Product licensing. Our native products, deployed and adapted for you.

note Every engagement starts with a free technical call and a written scope.

Join the community

Experienced in Rust, Java, Python, web, cloud or AI? Work on enterprise projects and native products with engineers who care about craft.

Apply to join

09 Contact

Have a complex problem?
Let's build the solution.

Tell us what's breaking, what's manual, or what you want to build. Clients get a technical reply with an architecture outline; engineers who want to join the community hear back from our team.

  • 01 A 30-minute technical call with an engineer, not a sales pitch.
  • 02 A written architecture outline for your problem.
  • 03 Scope, sequencing and a realistic timeline.

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