AI Development
Custom AI agents, MCP servers, and self-hosted LLM infrastructure — engineered for real production workloads.
The Problem: AI That Doesn't Ship to Production
Most businesses experimenting with AI hit the same wall: prototype demos that never make it to production. Generic API wrappers around OpenAI or Anthropic work in a demo but fail under real constraints — unpredictable latency, data privacy violations, vendor lock-in, and runaway costs the moment traffic scales. The gap between "it works in a sandbox" and "it works at 3 AM on a Friday under peak load" is where most AI projects die.
Worse, bolting AI onto legacy systems through generic REST calls creates fragile architectures. There's no structured way for AI agents to interact with your internal tools, no role-based access control on what the model can touch, and no path to running it on your own infrastructure when compliance or cost demands it.
The Solution: Production-Grade AI, Built for Your Constraints
NemesisNet builds custom AI infrastructure — MCP agents, self-hosted speech-to-text (STT) and text-to-speech (TTS) pipelines, multi-agent coding harnesses, and fine-tuned on-premise LLMs. Each system is designed for the operational constraints of the business it serves: latency budgets, hardware availability, security posture, and integration surface area.
Designed to minimize dependency on third-party API providers where self-hosting is appropriate. Architecture is portable across cloud providers, on-premise hardware, and hybrid deployments. You own the stack, you control the data, and the system works whether you're running on a cloud VPS or a rack of local GPUs.
MCP Agents & Tool-Use Infrastructure
Model Context Protocol (MCP) servers let AI agents interact with real systems — databases, CMSs, CRMs, internal tooling — through structured, role-safe interfaces. Rather than generic API calls that the model hallucinates, MCP agents expose deterministic tools that agents can actually reason about.
NemesisNet builds custom MCP servers that connect AI workflows to the specific tools and data your organization already runs. Built on FastMCP and Python, with Dockerized deployment for consistent environments. Works with Claude Desktop, Cursor, Windsurf, and any MCP-compatible client. Each server includes audit logging, permission scoping, and graceful error handling so destructive operations can be restricted, validated, and audited.
Speech Pipelines: Speech-to-Text (STT) & TTS
Speech infrastructure that runs entirely on your own hardware — both text-to-speech (TTS) and speech-to-text (STT). Using open-source models like Kyutai Labs' Pocket TTS and AIH parameters' Kokoro for synthesis, and Whisper-family models for transcription, NemesisNet builds speech pipelines that deliver natural speech synthesis and real-time transcription without cloud API dependencies. Sub-second latency. Full data privacy. Hardware-accelerated on CPU or GPU.
Our PocketTTS-MCP project wraps Kyutai's model in an MCP server, letting AI agents generate speech as a native tool — useful for voice assistants, accessibility features, and automated content narration. We build parallel speech-to-text (STT) pipelines for transcription, dictation, and voice-driven workflows.
Multi-Agent Coding Systems
Production-grade multi-agent coding harnesses that orchestrate LLMs across complex engineering tasks. Built with Python, GGUF model files, and Docker sandboxes that isolate agent execution. Each agent has a defined role — research, implementation, testing, review — with structured communication protocols.
This isn't a single prompt-and-pray interaction. It's a system where multiple specialized agents collaborate on multi-step engineering problems: one agent researches the API surface, another writes the implementation, a third writes and runs tests, and a fourth reviews the diff. Handles complex tasks that single-agent tools can't reliably complete.
Who This Service Is For
Startups building AI-native products who need infrastructure that scales without handing control to a cloud vendor. Enterprise engineering teams integrating AI capabilities into existing systems with strict data governance. Agencies and consultancies who need reliable, reusable AI infrastructure for client projects. Research teams running experimental models that require custom pipeline orchestration.
How We Build It
Discovery & Scoping
We map your use case, data constraints, latency requirements, and compliance needs. No assumptions — every architecture decision starts from your actual workload.
Architecture Design
We design the full stack: model selection, inference optimization, MCP tool definitions, data flow, and deployment topology. You get a technical spec before we write a line of code.
Build & Iterate
We build in Docker containers, test with your real data, and iterate on latency and accuracy. Infrastructure-as-code means every environment is reproducible.
Deploy & Handoff
Deployed to your infrastructure or ours. Full documentation, monitoring dashboards, and a runbook so your team can operate the system independently.
Why NemesisNet
NemesisNet builds production-grade AI infrastructure: MCP agents, self-hosted speech-to-text (STT) and text-to-speech (TTS) pipelines, multi-agent coding harnesses, and fine-tuned on-premise LLMs. Our approach is infrastructure-first: we care about what happens at 3 AM under load, not just what looks good in a demo. Based in Cape Town, South Africa, with time zone overlap to European markets.