Intelligent solutions powered by artificial intelligence
Harness the power of AI and machine learning to unlock new possibilities for your business. We develop intelligent systems that learn, adapt, and provide valuable insights to drive better decision-making and automation.
AI in production looks nothing like AI in a demo. Aystera builds AI/ML systems that actually run — with evals, guardrails, cost ceilings, and fallback paths for when the model is wrong (it will be). Our AI work ranges from LLM-powered customer support copilots to computer-vision pipelines processing thousands of images per minute, and forecasting models embedded inside operational dashboards.
We're framework-pragmatic — OpenAI, Anthropic, Google, open-source via Together/Replicate/Bedrock, or self-hosted on our own GPUs. We're also honest about when AI isn't the answer: a well-tuned heuristic, a search index, or a better form is often the right call. Our discovery output is as likely to say 'don't build the model' as it is to recommend one.
Every AI project starts with an evaluation dataset — 50–200 real examples of what 'good' looks like for your use case. Without evals, you can't tell whether a model change made things better or worse. We build the eval before we touch the prompt.
For LLM applications we ship: a versioned prompt store, retrieval-augmented generation where it improves answer quality, structured output validation, prompt-injection defences, latency/cost telemetry, and a fallback path (cached answer, simpler model, or human handoff). For ML pipelines: feature stores, model registries, drift monitoring, and shadow deployments before traffic.
We run quarterly model reviews — has the underlying foundation model changed? Are user behaviours drifting? Is the cost-per-query still defensible? AI systems aren't ship-and-forget; they're more like databases — they need attention to stay healthy.
Assess data quality and identify AI opportunities
Train and optimize machine learning models
Integrate AI models into your existing systems
Monitor performance and continuously improve accuracy
Depends on volume, latency budget, data sensitivity and cost ceiling. Closed-source APIs are right for most use cases under 100K requests/day; self-hosted Llama or Mistral starts paying back above that, or when you can't send data outside your VPC. We'll model the unit economics on a discovery call.
Yes — that's usually the whole point. We build retrieval pipelines that respect your row-level permissions, handle PII redaction, and keep your data inside your VPC if needed.
Three layers: retrieval-augmented generation grounded in your real data, structured output with schema validation, and an eval loop that flags drift. Plus a fallback path — cached answer, simpler model, or human handoff — for when the model is genuinely uncertain.
Tell us where you are and where you'd like to be — we'll come back with similar work, a plan and a number.