Production-Grade SaaS & MaaS
Engineered for Regulated Industries
NexGenHealth.io is a Software as a Service (SaaS) and Models as a Service
(MaaS) provider. We specialize as a custom software and AI-model training
provider. We build data pipelines, train customized Ai models, build custom
user interfaces, we curate datasets for customized Ai models, license software,
build your architecture, and prove your system through industry validation
standards. We will integrate your business and train your employees in
everything you need to know about Artificial Intelligence.
HIPAA-aware architectureOpen standards, no lock-inEngineer-to-engineer delivery
What NexGenHealth.io Does
We are a SaaS and MaaS
company that specializes in shipping the hard parts of modern software — the parts
most teams wish they didn’t have to build. Every service on this page is running
in production on our own platform before we offer it to yours.
Data Pipelines
Fivetran-class ingestion and ELT orchestration, structured extraction from PDFs / APIs / databases / event streams, and audit-grade throughput for regulated environments.
PDF / OCR / API / DB / stream ingestion with schema automation
PII / PHI scrubbing with Presidio-class 18-identifier coverage
Expert-in-the-loop labeling, Snorkel-style programmatic labeling functions, provenance-tracked versioning, and synthetic augmentation — clean, licensable corpora that become your training moat.
Human + AI-native labeling with quality gates
Semantic versioning, data lineage, DUA / license tracking
Synthetic augmentation for long-tail and rare-event coverage
Cloud, Network & VPS Architecture
VPS provisioning, cloud and local network topology, secure cloud storage, and zero-trust edge — engineered, documented, and delivered with runbooks your on-call team can actually use.
Hardened Linux VPS with systemd, Nginx, UFW, fail2ban
VPC peering, bastion hosts, encryption at rest & in transit
Backup / DR runbooks, threat model, drift detection
AI Model Training
Fine-tune open-source LLM and domain-NER backbones on your proprietary, internal, or restricted corpus — including air-gapped and VPC-isolated runs. Published benchmarks, private weights, your evaluation authority.
Air-gapped or VPC-isolated training for restricted data
Held-out eval harnesses with published F1 / precision / recall
Retrieval & Grounding (RAG / CAG)
Retrieval-Augmented and Cache-Augmented Generation pipelines with hybrid sparse + dense search, hand-tuned chunking strategies, and evaluation harnesses for faithfulness, relevance, and context precision.
Hybrid retrieval (BM25 + dense) with semantic re-ranking
CAG for fixed corpora — lower latency & cost than pure RAG
User Interfaces & LLM Chat
Accessible, internationalized, framework-agnostic UIs and grounded LLM chat interfaces — from operator consoles to customer portals to tool-using assistants that cite their sources.
WCAG-aware, i18n-ready, mobile-first design systems
Grounded chat with tool-use, streaming, multi-turn memory
Pluggable model backends (Claude / GPT / Grok / local)
Multi-Agent & Workflow Automation
LangGraph-orchestrated multi-agent swarms, CrewAI role delegation, and workflow agents for email triage, web scraping, and data aggregation — executable on-prem for sensitive data.
LangGraph / CrewAI / AutoGen with checkpointing & time-travel
Bespoke engineering for the systems off-the-shelf SaaS can’t touch — compliance posture, legacy interoperability, LLM-native security scanning, and auditor-ready evidence bundles.
HIPAA / SOC 2 / ISO 27001 / GDPR posture by default
LLM-native security + audit agents with triaged reports
Hybrid cloud, on-prem, edge, and air-gapped deployments
Software
as
a
Service
Software as a Service is a way of delivering software over the internet. Instead of installing programs on your own servers or computers, you reach the application through a web browser and pay a subscription to use it. The provider handles hosting, security, updates, and uptime — you focus on using the product.
Familiar examples: Gmail, Salesforce, Shopify, Stripe — and every NGH SaaS product.
Model
as
a
Service
Model as a Service is a way of delivering trained AI models — not applications — over an API. You send data (text, images, documents, audio) to an endpoint and receive the model’s output: predictions, embeddings, classifications, or generated text. You don’t own the GPUs, the training pipeline, or the evaluation harness underneath; you pay per inference or per unit of reserved capacity.
Typical traits: per-inference or per-capacity billing, private or shared endpoints, versioned releases, published evaluation metrics, optional private-weight ownership.
Familiar examples: OpenAI’s API, Anthropic’s API, Hugging Face Inference Endpoints — and every NGH custom-model endpoint.
Advanced Service Lines
Twelve specialty practices that extend the core pillars. Each is grounded in production-class techniques used across the industry, packaged for the way you want to buy — SaaS API, MaaS endpoint, fixed-scope build, or embedded engineering.
01 Infrastructure & Deployment
Cloud VPS Engineering
Build from the kernel up.
Provisioned, hardened, observable Linux servers with systemd-managed services, Nginx reverse proxies, Let’s Encrypt auto-renewal, UFW firewalling, fail2ban, Redis authentication, LUKS-encrypted vaults, and non-root service accounts. Hostinger, DigitalOcean, Hetzner, AWS Lightsail — or your existing rack. Every box ships with a runbook your on-call engineer can actually use.
systemdNginxUFWfail2banCertbotLUKS
API Routing & Container Architecture
MCP-ready, Docker-native, zero-downtime.
Gateway design for multi-service architectures: edge→VPS proxies, Docker-containerized microservices, Model Context Protocol (MCP) servers for agent-to-tool wiring, OAuth 2.1 / SAML / OIDC enterprise auth, and .well-known metadata endpoints so your services self-describe to any compliant agent. One connector per tool — not N × M integrations.
MCPDockerNginxKubernetesOAuth 2.1
Network & Cloud Architecture Design
From LAN to multi-region cloud.
Topology design for local networks, VPS / cloud architecture, and secure storage. VPC peering, bastion hosts, private subnets, zero-trust edge, encryption-at-rest / in-transit, S3 and object-storage lifecycle rules, and backup & disaster-recovery runbooks. You leave the engagement with a documented topology, a threat model, and drift detection — so the architecture stays the way you intended it.
VPCZero-trustBastionEncryption at restBackup / DRThreat model
Custom AI Model Training
Your data. Your weights. Your benchmarks.
Fine-tune open-source LLM and domain-NER backbones on your proprietary corpus — LoRA adapters for multi-tenant hosting, full-parameter training for maximum capability, or reinforcement fine-tuning with as few as 10 labeled examples. Every model ships with a held-out evaluation harness and published precision / recall / F1 your team can re-run. No black-box promises; no vendor retention of your weights.
LoRAQLoRARFTEval harnessPrivate weights
Private & Restricted-Data Model Training
Train where your data lives.
Fine-tune models on internal, proprietary, or classified data inside your firewall — or on a provisioned GPU cluster inside your VPC. Differential-privacy options, PHI / PII-filtered training sets, isolated execution environments, and auditor-reviewable training logs. No data leaves your perimeter; no weights leak; every batch is accounted for.
Grounded conversational UIs wired to your document corpus, toolchain, and business logic. Streaming responses, tool-use with citation, multi-turn memory, accessibility-first markup, and i18n-ready copy. Brandable theming and pluggable model backends — swap Claude, GPT, Grok, or a local Llama without touching the UI layer.
RAGTool-useStreamingWCAGMulti-provider
RAG & CAG Systems
Retrieval + cache, engineered end-to-end.
Grounded generation over your data with Retrieval-Augmented Generation (hybrid sparse + dense search, semantic re-ranking, pgvector or Weaviate) and Cache-Augmented Generation for fixed-corpus workloads that beat RAG on latency and cost. Expert curation of large document sets; hand-tuned chunking strategies — semantic, parent-child, proposition-based, late-interaction — and eval harnesses that measure faithfulness, answer relevance, and context precision.
RAGCAGHybrid searchChunkingRe-rankingpgvector
Multi-Agent Orchestration & Swarm Execution
Graph-planned, locally executable swarms.
LangGraph-orchestrated multi-agent systems with CrewAI-style role delegation, AutoGen conversational patterns, and local / on-prem execution for sensitive data. Built-in checkpointing, time-travel debugging, conditional routing, and drift monitoring. Prototype in CrewAI, productionize in LangGraph, deploy on Docker or Kubernetes — or run the swarm entirely inside your firewall.
Email triage and response agents, multi-site web scrapers with self-healing selectors, and data aggregation pipelines that deduplicate, reconcile, and normalize across sources. Playwright-powered browser automation, intent-driven scrape targets, LLM-based extraction of unstructured content, and audit-grade logs of every action taken.
Always-on agents that monitor competitor pricing, product launches, press, job postings, and customer sentiment. Source-cited briefs, scheduled digests, and alerts on material changes — backed by RAG over your industry corpus. Replaces the quarterly intelligence report with a live dashboard your exec team can trust.
Continuous monitoringRAGSource citationAlerting
Software Security Agents
Read code like a researcher, not a rulebook.
LLM-native vulnerability scanning that reasons about code flow instead of pattern-matching. Detects prompt injection in AI applications, OWASP Top-10 regressions, secret leakage, and exploitable logic flaws. Triaged findings with reproducers and proposed patches — not a CVE firehose. Complements Snyk / CodeQL / Semgrep, doesn’t replace them.
Automated codebase audits for compliance posture (HIPAA, SOC 2, ISO 27001, GDPR), dependency hygiene, SBOM generation, license conformance, and architectural drift. Produces evidence bundles, remediation tickets, and an auditor-ready report your legal team can hand over unchanged.
Structured logging, RLS policies on every table, traceable request IDs, and runbooks published alongside every production service.
📊
Benchmarked Models
Every model ships with published precision / recall / F1 on held-out data and an external benchmark. No black-box promises.
🌐
Data Sovereignty
Choose cloud, self-hosted VPS, on-prem, or hybrid. Export-ready at any time — your data is portable, your models licensable.
📜
Documented, Not Folklore
Architecture, API map, environment matrix, port map, alerts — every service ships with the documentation we use internally.
Frequently Asked
Are you a healthcare-only provider?
No. NexGenHealth.io was born inside the regulated healthcare stack, but the underlying capabilities — de-identification, RAG, dataset curation, auditable pipelines — apply cleanly to legal, financial, insurance, and government workloads. If your industry has an audit, we have an architecture.
Can we self-host the SaaS offerings?
Yes. Every SaaS service can be delivered as self-hosted software (Docker or Kubernetes) or on-prem. Pricing and SLA adjust to reflect the operational boundary.
Do you sell custom-trained models with private weights?
Yes. Under the MaaS model we train on your corpus, hand you the evaluation harness, and can license weights exclusively or host them behind a private endpoint. Fine-tuned LLM adapters ship on open-source backbones you can audit.
What does a typical engagement cost?
Subscription SaaS starts at $399/mo. Fixed-scope builds start around $14K setup + monthly. Enterprise MaaS and embedded-team engagements are sized by scope. Every quote is itemized — no bundled mystery line items.
How do you handle BAAs and data residency?
We’ll sign a Business Associate Agreement for HIPAA engagements. Data residency is configurable at the deployment layer — US West (default), self-hosted in your VPC, or on-prem inside your firewall.
Who owns the data and the models?
You do. Data is exported in open formats on request; model weights are licensable or transferable depending on the engagement. Our exit plan is written into the SOW.
Do you build Model Context Protocol (MCP) servers?
Yes. We design and deliver MCP servers to wire your internal tools and data stores to any MCP-compliant agent runtime — Claude, GPT, Gemini, or in-house LLMs. Scope typically includes the server implementation, .well-known metadata, OAuth 2.1 / SAML / OIDC enterprise auth, audit logging, and a deployment target (Docker, Kubernetes, or bare-metal). One MCP server replaces the usual N × M point-to-point integrations.
Can your multi-agent systems and workflow agents run on-prem?
Yes. Every agent service line — orchestration swarms, email / scraping / aggregation workflows, market research, security, and audit agents — can be delivered as a cloud SaaS, a self-hosted Docker stack, a Kubernetes deployment inside your VPC, or a fully air-gapped on-prem install. Local swarm execution is specifically designed for sensitive data that cannot leave your perimeter.
Let’s Scope Your Build
Free 30-minute architecture call. Come with a problem, leave with a shaped approach and an honest range. No credit card, no NDA required to start.