AI that does more with less waste.
IgnisPrompt handles routine AI tasks locally — summarizing, drafting, extracting — and only escalates to the cloud when truly needed. Then it shows you the impact, like a health app for your AI footprint.
Built on research and data from
AI usage is growing faster than visibility.
Organizations are adopting AI rapidly, but many have limited insight into where prompts run, why workloads end up in the cloud, how much token overhead accumulates, and whether local alternatives could serve them just as well.
AI workloads often default to cloud services even when local or edge execution may be sufficient, increasing latency, cost, and data exposure.
Repeated, verbose, and low-value prompting patterns — including courtesy phrases like "thank you" — can increase token usage and compute overhead at scale. (67% of users regularly use courtesy language with AI)
Many teams lack clear explanations of why a particular model or runtime was selected, making governance, auditing, and optimization difficult.
AI infrastructure impact is difficult to quantify when compute location, model choice, and usage patterns remain opaque. Teams can't improve what they can't see.
AIGNITRON makes AI infrastructure decisions visible.
AIGNITRON is developing a local-first stack for routing, explaining, and observing AI inference decisions. The goal is to help users and teams understand when local AI is enough, when cloud fallback is warranted, and how usage patterns can become more efficient.
Policy-based AI routing across local, edge, enterprise, and cloud environments — with the local path always attempted first.
Audit-friendly route explanations that surface why a workload was handled a particular way, making AI governance practical.
Dashboards designed to help users understand usage patterns, routing behaviour, and future sustainability indicators — without overclaiming.
Two tools. One mission.
Building blocks for responsible AI infrastructure — open-source foundation, early-stage development.
IgnisPrompt
Local-first AI routing daemon
IgnisPrompt is an open-source Rust-based routing daemon designed to run locally. It provides a foundation for policy-aware AI routing, local model discovery, route explanations, and audit event visibility — ensuring cloud is the last resort, not the default.
- Local daemon architecture — runs on your device or server
- Health, model, and route explanation endpoints
- Policy-configurable: local-first by default
- Audit event visibility for routing decisions
- No model weights included; bring your own local SLM
- Open-source development — being built in public
GET /health
{
"status": "ok",
"local_models": ["phi4-mini", "gemma3-1b"],
"policy": "local-first",
"cloud_fallback": false
}
Aethra
AI inference & environmental observability
Aethra is the planned standalone dashboard for observing AI routing decisions, model/runtime status, audit events, and future resource-impact indicators. Designed to make local-first AI infrastructure understandable — for founders, developers, and decision-makers.
- Local AI routing observatory — read-only, transparent
- Route explanation viewer: why each decision was made
- Model and runtime status across local, edge, and cloud
- Designed for future environmental inference visibility
- Not a carbon accounting or compliance certification tool
- MVP dashboard concept — fixture-driven early stage
LOCAL ██████████████ 78%
EDGE ████ 16%
CLOUD █ 6%
// routes explained: 124 this session
// cloud calls avoided: 97 (est.)
wh_preserved: ~14.2 Wh // indicative
From request to route explanation.
The vision is simple: evaluate the request, prefer local capability where appropriate, escalate only when policy allows, and make the decision visible.
Prompt Intercepted
IgnisPrompt receives the incoming AI request before it leaves your device or network — acting as a local-first gatekeeper.
Policy Classifier Runs
A lightweight on-device classifier assesses intent, complexity, and applicable routing policy — in milliseconds, with no cloud call.
Route Decision Made
Based on policy, model availability, and risk level, IgnisPrompt routes to the most appropriate tier — local, edge, enterprise, or cloud fallback.
Audit Event Emitted
A structured audit event captures the routing decision and reasoning. Aethra surfaces this for teams to review, analyze, and learn from.
Why now is the right moment.
AI adoption is accelerating across businesses, schools, developers, and institutions. As usage grows, so do concerns around cost, infrastructure dependence, privacy, governance, and environmental impact.
Organizations need practical tooling to understand and control AI data flows without sacrificing capability.
Microsoft's AI-driven electricity consumption jumped 30% in 2024; Google's emissions rose 48% year-over-year.
The SLM ecosystem has matured dramatically since 2024, enabling capable local-first architectures.
EU CSRD and evolving requirements are creating commercial imperatives for AI infrastructure transparency.
Statistics included for context only. AIGNITRON does not guarantee the accuracy of third-party figures. Proprietary analysis figures are illustrative projections based on published research.
Why AIGNITRON is different.
AIGNITRON starts from the device and local environment rather than assuming cloud-first. The cloud is a fallback — not a default — by design and by policy.
The vision combines AI routing decisions with explainability and dashboard visibility in one coherent stack — not two separate bolt-on tools.
Instead of treating efficiency as a backend metric, AIGNITRON aims to make responsible usage visible, understandable, and rewarding for teams and individuals.
IgnisPrompt is being built in public to encourage transparency, feedback, and community participation. No black boxes; the routing logic is inspectable by design.
The company avoids overclaiming and is building toward measurable, auditable signals. Every metric is clearly framed as illustrative, projected, or methodology-based.
Built in the open.
AIGNITRON is using an open-source-first approach for IgnisPrompt to invite developer feedback, improve trust, and create a foundation that others can inspect, test, and extend.
$ git clone https://github.com/aignitron/ignisprompt
Cloning into 'ignisprompt'...
$ cd ignisprompt && cargo build --release
Compiling ignisprompt v0.1.0
Finished release [optimized] target(s)
$ ./ignisprompt --policy local-first --port 8080
→ IgnisPrompt daemon starting on :8080
→ Policy: local-first | Cloud fallback: disabled
→ Local models discovered: 2
→ Ready. Routing locally.
Note: IgnisPrompt is an early-stage open-source project. We welcome feedback from developers, sustainability experts, and organizations exploring responsible AI infrastructure.
AIGNITRON was founded with a mission to make AI feel powerful, responsible, and efficient. We focus on practical infrastructure that helps people use AI with more visibility, control, and confidence — starting with open-source tooling that puts local-first design at the core.
Let's build responsible AI infrastructure.
AIGNITRON is preparing for the next stage of validation through advisor feedback, customer discovery, technical refinement, and public MVP development. We're seeking guidance, validation, and early partners.