Skip to content
GO LOCAL.AI
Menu

Lower AI Costs. Lower Carbon.

Go Local AI's software provides automated AI assessments, self-improving routing and continuous AI usage monitoring to reduce your costs and environmental impact.

Get started Explore the platform
Seven workload types all go to the frontier model by default. Usage and billing evidence feeds the assessment below; this is not an extra inference hop.
Routine Sensitive Complex High-volume Short-form Long-form Tool use
Frontier model Used by default £££
Current setup
Usage and billing evidence continues into workload assessment.
Usage + billing baseline
Real workloads are tested on the current model and alternatives. A blind quality gate leads to a validated architecture and savings estimate. Monitoring evidence returns here for re-assessment.
Real workloads Cost · quality · data sensitivity
Current model
Alternatives Cloud + open-weight + right-sized
Required Quality Threshold
Usage, infrastructure, data sensitivity, scale
Validated architecture Model choices + savings + carbon
Re-assess
The validated architecture informs classifier training and evaluation. Client requests enter the existing gateway. The Go Local AI classifier is an add-in, returning a routing decision to that gateway. It is not a replacement gateway. Approved requests continue down to the model choices.
Client request Task · complexity · data sensitivity
Your gateway Bifrost · LiteLLM · Portkey etc
Go Local AI add-in Routing classifier Tuned to your work
Train + evaluate Classify Decision Lowest-cost suitable route
The gateway sends a workload to a small local model, a larger local model or a frontier API fallback. Private models sit inside the data boundary on on-premise, private cloud or managed infrastructure. Routing and outcome telemetry from every destination is monitored.
Your data boundary
Private fast Open-weight · routine
Private large Open-weight · harder
Cloud / API Frontier fallback
On-prem · private cloud · managed Routing + outcome telemetry
Monitoring combines gateway logs, GPU and host telemetry, and grid data. Six dimensions are recorded: cost, quality, energy, operational carbon, routing and escalation. Changes feed back to assessment, classifier training, quality evaluation and an updated policy. Carbon is an estimate, not a direct measurement. No live results are represented here.
Gateway logs + GPU / host + grid data
Monitoring Engine TASK · TEAM · PROMPT
Cost
Spend per request
£
Quality
Task + baseline + drift
✓
Energy
Measured GPU / host
Wh
Carbon
Energy + grid estimate
CO₂e
Routing
Model selected
→
Escalation
Fallback + reason
↗
Re-evaluate on shift Models · prices · workload drift
Assess → retrain → evaluate→ update

One platform.

Assessment Engine

Assessment compares AI models and infrastructure to identify potential savings.

Routing Engine

Routing chooses the lowest cost suitable AI model for each request.

Monitoring Engine

Monitoring tracks AI costs and quality, and estimates carbon across your business.

Explore the engines

Cut your AI costs with
infrastructure that adapts.

Reuse your hardware
for SQL & Python.

Replace underused Gemma 4 with Qwen3.8 as demand shifts from prose to coding.

+34% SQL & Python tasks

Currently routed to GPT-5.6 Terra.
AI costs are up 18%.

Underused private model

Gemma 4 31B

−42%Copywriting & prose tasks

Private infrastructure
Proposed model mix
23% Potential cost reductionEstimate · vs current setup

Qwen3.8 27B

SQL workPython coding

Reuse your existing hardware

GPT-5.6 Terra

Remaining prose workApproved exceptions & fallback

Existing API connection

Go Local AI evaluatesQuality · Cost · Hardware fit

You approve deploymentProduction stays unchanged until then.

How recommendations work

Self-improving
routing.

Your AI work shapes the small model that chooses where requests go. As your business evolves, the model is automatically retrained and tested to stay relevant.

Explore adaptive routing
Self-improving routing Client requests enter your gateway. The Go Local AI classifier above it returns routing decisions. Several model destinations handle the work. Successful outcomes, escalations and feedback are shown. Feedback informs a tested and approved classifier update; this animation compresses that process. Client GO LOCAL AI Classifier Decision Gateway Private fast £ Private large ££ Specialist Cloud / API £££ Learn · Test · Improve
Matched Escalated Feedback
Examples of gateways, model providers and compute platforms that can remain part of the customer's existing AI stack.
Gateways Bifrost LiteLLM Portkey
Models + compute OpenAI Anthropic OpenRouter

Works with your existing AI stack.

Keep your gateway, providers and agents. Go Local AI adds the intelligence that decides what should run where.

Explore the architecture and integrations

Why Go Local AI

Explore why Go Local AI

  1. Much more than AI FinOps

    See your AI costs, what you can do about them and your environmental impact.

  2. Forget inconsistent heuristic routing

    Our classifiers learn your requirements and keep adapting as they change.

  3. Beyond a one-off assessment

    Continuous monitoring and automated infrastructure assessments reduce the need for AI consulting or architecture engagements.

  4. Sustainability and ethics first

    Run inference on sustainably powered infrastructure, with customer-controlled data boundaries and UK-sovereign deployment options.

Go Local AI, your way.

Licence the software directly, or start with an assessment or pilot.

Not sure where to start?

Book a free 30-minute review