AI & MACHINE LEARNINGAI systems your operators can trust.
We introduce AI into real company systems: document flows, support queues, smart factories, approval paths, legacy operations, and ML inference. The model is never the whole product; the controls, evals, latency budget, and rollback path decide whether it survives production.
- 01AI workflow pilotTwo weeks, fixed price. Domain map, risks, and a delivery plan.
- 02Working sliceOne real workflow shipped end to end.
- 03Progressive rolloutTraffic moves in steps; rollback stays ready.
- 04HandoffRunbooks, dashboards, and docs your team keeps.
AI coach shipped inside a deterministic finance product, bounded by user consent
CloudFloo product portfolioFixed price, quoted before kickoff.
This is an engineering line, not a shelf product: we scope it from a pilot rather than from a wish list. Two weeks, one real workflow shipped end to end, and the architecture map, decision records and working slice stay yours whatever you decide next.
Get a process reviewWhat we build
RAG, agents & workflow automation
Retrieval, tool use, approvals, audit logs, and fallback behavior for business-critical flows.
Smart factory AI
Machine events, quality gates, maintenance tasks, operator approvals, and legacy system integration.
Custom ML & inference
Training, fine-tuning, serving, batching, caching, and model routing by latency and cost.
MLOps & evaluation
Drift checks, regression evals, prompt/version control, monitoring, and rollback gates.
What we ship
The architecture, the gates, and the cutover, drawn out.
Stack in production
The tools we run for this work, and what each one is there to do.
Model training and fine-tuning.
Open-weight models, tokenizers, and eval datasets.
GPU inference serving with batching.
Distributed training and batch inference jobs.
ML pipelines on the Kubernetes you already run.
Experiment tracking and model registry.
Hosted models behind our own evals and cost controls.
The default system of record.
Runtime for every platform we operate.
What you get
- 01AI opportunity map: where agents help, where deterministic software should stay in charge.
- 02Working vertical slice connected to one real workflow, dataset, or company system.
- 03Eval suite, monitoring, safety checks, and human approval points for risky actions.
- 04Cost and latency model: per request, per workflow, and per environment.
Describe the system that worries you.
Four fields, no call. You get a written process review: what we would change first, which systems it touches, and what the AI workflow pilot would cover. We reply within one business day.