01
Predictive analytics
Forecasting, churn, propensity, and risk models trained on your data. Calibrated, monitored, and retrained on a cadence we agree up front.
Machine Learning Development Company
Machine learning development company shipping AI that runs in production.
Aiinfox is a machine learning development company — predictive analytics, NLP, computer vision & MLOps. Senior engineers, 50+ shipped, 99.95% uptime.
train.py — runs/exp-0142epoch 04/12
sys> train_loss=0.214 val_loss=0.231 acc=0.918
sys> drift_alert: feature_8 → KL=0.034 ok
tool> eval suite: 1,284 examples · pass 94.6%
claude> next: distill 70B → 8B for inference
latency target: <120ms p95▍
latency target: <120ms p95▍
GPU: 4× A100 · cost: $11.4/hrcheckpoint 14/120 saved
Building withPythonPyTorchTensorFlowscikit-learnHugging FacevLLMRay
Overview
Most ML pilots don't survive the production handoff because they were never designed for one. The notebook ran on a tidy CSV; the model was scored against a held-out split; nobody owned the data pipeline, the drift monitor, the cost telemetry, or the question of what happens when an upstream schema changes. We build the other thing. Every AI and machine learning development engagement starts with the success metric and the eval harness — the model is a means to that end. The pipeline, the monitoring, and the runbook are not a phase 2; they're scope-line items in week one.
Across 50+ shipped systems, we've delivered predictive analytics (churn, propensity, fraud, uplift), NLP (classification, extraction, semantic search, RAG), computer vision (detection, OCR, video analysis), and bespoke ML for clients who outgrew foundation models. The work spans Python, PyTorch, scikit-learn, Hugging Face, Ray, MLflow, and vLLM — chosen per task, deployed to AWS / GCP / Azure / Cloudflare Workers, or self-hosted on Kubernetes for regulated workloads. Customer-facing models run at sub-2-second p95 latency. Production uptime sits at 99.95% across deployments.
Outcomes
50+
AI systems shipped to production
<2s
average p95 latency on customer-facing models
99.95%
production uptime across deployments
Quick definition
What is AI and machine learning development?
AI and machine learning development is the end-to-end engineering of systems that learn from data — from data pipelines and feature engineering through model training, evaluation, deployment, monitoring, and retraining. Production ML development is 80% the platform around the model: drift detection, observability, cost telemetry, and an eval harness that gates every change against business KPIs.
What we deliver
What you actually get.
02
Natural language processing
Classification, extraction, summarisation, semantic search, and intent routing. RAG when retrieval matters, fine-tune when it doesn't.
03
Computer vision
Object detection, OCR, image classification, and video analysis pipelines. On-device, on-prem, or cloud — whichever your data residency demands.
04
Recommendation systems
Behaviour-grounded, eval-gated recommenders for content, products, or learning paths. We A/B test from day one.
05
MLOps & evaluation
Eval harnesses, drift detection, prompt-cache layers, and observability. Production AI is 80% the platform around the model — we build that platform.
06
Custom model training
Fine-tunes, distillations, and domain adaptation when foundation models aren't enough. Reproducible runs with versioned data and weights.
How it fits together
A picture of the whole system.
The shape of every engagement — three lanes from data to delivery, with the parts most teams skip already wired in.
1
Ingest
Raw data
S3 · Postgres · APIs
ELT + dbt
tests + lineage
Feature store
versioned
2
Train
Model registry
MLflow
Fit + tune
PyTorch · Ray
Eval harness
1k+ test set
3
Serve
Inference API
vLLM · FastAPI
Observability
drift · cost · p95
Retrain loop
weekly cadence
Process
How we ship.
01
Discover
Define the success metric, the data shape, and the eval set before any model selection.
02
Build
Senior engineers ship working pipelines week-over-week. No throwaway prototypes.
03
Evaluate
Quantitative evals + red-team + cost/latency baselines before any production traffic.
04
Operate
Drift detection, automated retraining, runbooks, and an optional retainer for tuning.
“
They didn't just ship a prompt. They built evals, instrumented latency, and caught two prod regressions before our customers did.
VP Engineering
Series-B SaaS, US
Tools
The stack we wield.
PythonPyTorchTensorFlowscikit-learnHugging FacevLLMRayMLflowWeights & BiasesAirflowPrefect
FAQ
Questions teams actually ask.
Do you train models from scratch or fine-tune foundation models?
We start with the cheapest, most capable foundation model that clears the eval bar — Claude, GPT-4o, Llama 3, Mistral. We only fine-tune when evals demand it, and only train from scratch for problems foundation models genuinely cannot solve (rare, but real).
How do you handle ML model drift in production?
Drift monitors run on inputs (data drift), outputs (prediction drift), and ground-truth feedback (concept drift). Automated alerts trigger evaluation against a fresh test set; retraining is scheduled when the eval bar drops below threshold. Every retrain is reproducible.
Can you deploy ML models on-prem or in our VPC?
Yes. We've shipped on AWS, GCP, Azure, Cloudflare Workers, and bare-metal Kubernetes. On-prem and air-gapped deployments are supported. Models can be served on vLLM, Triton, or BentoML depending on latency and throughput needs.
What is the typical ML project timeline?
Six weeks for a focused production model (one use case, one pipeline). Twelve weeks for a multi-model platform with shared feature store and observability. Pure analytics and modelling without deployment can ship in three to four weeks.
How do you measure ML project success?
Every engagement gets a business KPI and an eval set agreed at scope. We report against both weekly. If the model doesn't beat the eval bar by launch, we keep iterating on our dime if we missed the target.
Can you take over an existing ML system?
Yes — we do takeover audits and stabilisation work routinely. Step one is reading the code, the data, and the dashboards. Step two is shipping the smallest valuable change to prove we understand it. Step three is the longer-term rebuild plan if one is needed.
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