AIthatshipstoproduction.
Custom LLM apps, RAG pipelines, predictive analytics, voice and vision AI. We build AI features that survive contact with real users, real data and real budgets.
Built like a product,
not a service.
Most AI demos die in production. We focus on shipping AI that's evaluated, observable, cost-controlled and useful — not just impressive in a recorded demo.
Everything we ship
in this practice.
Custom LLM applications
GPT-4, Claude, Llama — picked for the job, not the hype.
RAG pipelines
Retrieval-augmented generation with real evals and grounded answers.
Voice & vision AI
Speech-to-text, voice cloning, OCR, object detection.
Predictive ML models
Forecasting, churn, fraud detection, recommendation systems.
Agentic workflows
Multi-step AI agents that read, decide and act on real systems.
ML ops
Eval pipelines, drift detection, A/B testing, cost monitoring.
Numbers from real
production engagements.
Four phases.
No surprises.
Use case framing
We separate the AI hype from the actual job to be done.
Eval first
We build the evaluation harness before the model — so we know what good looks like.
Build & iterate
Prompt engineering, fine-tuning, RAG — whatever the evals say works.
Productionize
Cost controls, fallbacks, observability, on-call rotation.
Things people ask
about ai & ml solutions.
Should we use GPT-4, Claude or open-source?
+
Depends on cost, latency, privacy and accuracy needs. We'll benchmark for your use case.
Can you fine-tune models on our data?
+
Yes — full fine-tunes, LoRAs, instruction tuning, you name it.
How do you control AI costs?
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Caching, smaller models for routing, prompt compression, and per-tenant budgets.
You might also need
readytobuild something great?
Tell us what you're building. We'll come back within 24 hours with a real engineering perspective — no sales pitch, no slideware.