AiGanak
Scope a project
Infrastructure · Private deployment

Run capable models where your data already lives.

Secure cloud migrations and local open-source model optimisation. We benchmark candidate models on your tasks, deploy them in your cloud account or on your own servers, and tune them for latency and cost.

In short

AiGanak deploys and optimises open-weight language models in a client's own cloud account or on-premise, including offline setups, so enterprise data never reaches public AI APIs.

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When to call us

Problems this solves

Data cannot leave

Customer, patient or financial data can't be sent to a public AI API under your contracts or regulations.

Unpredictable API bills

Per-token costs grow with usage and are hard to forecast.

Latency and availability

Shared APIs add queueing and outages you can't control.

What you get

Deliverables

Open-weight modelsYour cloud accountOn-premiseOffline setupsQuantisation
  • Model shortlist benchmarked on your tasks in Model Lab
  • Break-even analysis against your current API spend
  • Deployment in your cloud or on-premise, including offline
  • Quantised builds tuned for your hardware
  • Monitoring, access control and audit logging
  • Upgrade path to a custom model when it pays
How it runs

Engagement steps

Benchmark

Compare open models on your own examples and work out the break-even volume.

Deploy

Set up inference in your environment with security controls.

Optimise

Quantise, cache and route for latency and cost.

Related

Often paired with

Custom small language model development

AiGanak builds domain-specific small and large language models in stages: a proof of concept on sample data with an evaluation report, an MVP for a pilot team, then private production deployment. The work uses the training pipeline built for AiGanak-SLM-1B.

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Model Lab

Model Lab benchmarks fine-tuned and candidate models against production baselines on your own examples, scoring accuracy, drift, hallucination and latency, and emails a performance report.

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LLM Cost & Reliability Audit

The LLM Cost & Reliability Audit measures your OpenAI, Anthropic, Azure, Bedrock or Gemini usage from your own logs, prices every saving (model routing, caching, prompt trimming, retrieval, batch) against your approved answers, and ranks reliability risks. A free Quick Check gives an instant estimate.

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FAQ

Questions people ask

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Can AI run without sending data to OpenAI or Anthropic?

Yes. Open-weight models can run in your own cloud account or on your own servers, including fully offline. AiGanak benchmarks candidates on your tasks first so you know what quality to expect.

When is self-hosting cheaper than an API?

Only above a certain monthly volume. The LLM Cost Audit calculates your break-even point from your own usage before you buy hardware.

Tell us what you're building.

AiGanak Agent drafts a scoped proposal in a few minutes. An engineer reviews it with you.

Scope a project →Free cost check