Machine Learning services that ship — not just train.
Production ML, MLOps, fine-tuning, and custom models. Computer vision, NLP, predictive analytics — built, deployed, monitored, and retrained in your cloud. The 80% of ML work most teams skip, done right.
- PyTorch, TensorFlow, Hugging Face — modern stack
- Deployed to YOUR cloud — data never leaves
- Full MLOps (MLflow, monitoring, retraining)
- Fine-tune Llama 3.3 / Mistral on private data
71%
reduction in escaped defects · custom YOLOv9 defect-detection shipped in 12 weeks for $62k
“We'd been told for two years that this would take 9 months and $250k. Paisol shipped a working production model in 12 weeks for a fraction of that — labeled images, trained, deployed on-prem, monitored.”
Director of Engineering
CPG manufacturing client
80% of enterprise ML projects never reach production. The model trains, the notebook runs, the metrics look great — and then the project dies in the gap between research and engineering. We close that gap.
Paisol Technology builds production machine learning systems: training pipelines, model deployment, monitoring, A/B testing, and auto-retraining — all wired into your existing cloud. From a focused single model to a full MLOps platform to fine-tuning open foundation models like Llama 3.3 on your proprietary data, we ship what actually runs in production. For LLM-powered conversational features, see our AI agent service.
Not sure if you need ML at all? Take our free AI Opportunity Audit — we'll tell you, honestly, in 24 hours.
Is custom ML the right path for you?
Most teams don't need custom ML in 2026 — foundation models cover it. Quick honesty test.
This is for you if…
- You have real data — 2,000+ labeled examples, or budget for labeling
- You have a measurable accuracy / latency / cost target
- A foundation-model API (GPT-4o vision, Claude vision, Gemini) was already tried and isn't enough
- You need MLOps — monitoring, retraining, evals — not just a notebook
- On-prem / VPC deployment is required for compliance
This is NOT for you if…
- You could just call a foundation-model API for $0.10/request (most teams can)
- You don't have data and don't have a labeling plan
- You can't articulate the success metric with a number
- Budget is < $15k — start with a foundation-model prototype first
What we build
Four ML categories cover most production work.
Computer vision
Defect detection, document OCR, video analytics, AR / scanning. We train, evaluate, deploy, and monitor.
NLP & text intelligence
Classification, named entity recognition, summarization, semantic search, document understanding.
Predictive analytics
Churn prediction, demand forecasting, fraud detection, recommendation engines. Drives real revenue.
Custom model fine-tuning
Fine-tune open models on your proprietary data. Llama 3.3, Mistral, Qwen — trained in your cloud account.
Our 90-day delivery process
No discovery loops. No scope creep. You see working software every week.
- Weeks 1–2
Step 1
Problem framing + data audit
We map the prediction task, audit the data, set evaluation criteria. Written scope + fixed price before code starts.
- Weeks 3–5
Step 2
Baseline + iteration
Baseline model, error analysis, feature engineering, iteration. You see metrics every week.
- Weeks 6–8
Step 3
Production model + MLOps
Final model selection, deployment infrastructure, monitoring, retraining pipeline.
- Weeks 9–10
Step 4
Integration + launch
API or SDK for your product team, documentation, runbooks, on-call setup. 90-day warranty starts.
Our tech stack
Built with the same tools the best engineering teams in the world use today.
Frameworks
- PyTorch
- TensorFlow / Keras
- JAX
- Hugging Face Transformers
- scikit-learn
MLOps
- MLflow
- Weights & Biases
- Kubeflow
- BentoML
- SageMaker / Vertex AI
Data
- Spark / Databricks
- DVC
- LakeFS
- Pandas / Polars
- Dask / Ray
Deployment
- AWS SageMaker
- GCP Vertex AI
- Modal
- Replicate
- Triton Inference Server
The boring stuff, done right
Compliance, IP, contracts — the parts most agencies hand-wave.
SOC 2 architectures
Audit-ready by Day 90
HIPAA-aware builds
BAA-eligible cloud regions
GDPR + PIPEDA
EU + Canadian data residency
You own the code
IP transfer Day 1
Fixed-price contracts
Quoted in 48 hours
90-day warranty
Bugs fixed free post-launch
Calculate the ROI of an AI / ML deployment
Free calculator. Plug in your interaction volume and per-interaction cost — see annual savings, payback period, and 3-year value from a custom ML or AI agent.
Open the ROI CalculatorEstimated
2.4×
typical first-year ROI for a production ML deployment vs human-only workflows
Transparent, fixed-price engagements
No hourly billing. No surprises. Quoted in writing within 48 hours.
Single Model
One ML model, one use-case, deployed to your cloud. 6-week delivery.
- Data audit + problem framing
- Baseline + final model
- API / SDK for your product
- Basic monitoring
- 90-day warranty
ML Platform
Full MLOps platform — training, deployment, monitoring, retraining. 10-week delivery.
- Everything in Single Model
- 2–3 production models
- MLflow experiment tracking
- A/B testing + drift monitoring
- Auto-retraining pipeline
- Dedicated ML engineer 3 months
Custom Foundation Model
Fine-tune Llama / Mistral / Qwen on your proprietary data.
- Everything in ML Platform
- Open-model fine-tuning (LoRA, full)
- On-prem / VPC deployment
- SOC 2 / HIPAA architecture
- Inference cost optimization
- Dedicated team 4–6 months
Available in 8 countries, 22+ cities
View all locations →Frequently asked questions
The questions we get on every first call — answered.
- A focused production model (one use-case, one data source) runs $15,000–$40,000. A full MLOps platform with training, evaluation, deployment, and monitoring runs $50,000–$150,000.
- Most teams should start with foundation models (GPT-4, Claude, Gemini) via API — fast, no training data needed, 80% as good for most tasks. Build a custom model when you have proprietary data the foundation models don't see, need millisecond inference, or have strict cost-per-inference constraints at scale. We'll recommend on the strategy call.
- An AI agent is an LLM-powered system that takes actions in your business. A machine learning model is the math underneath — image classifiers, recommendation engines, fraud-detection scoring, churn prediction. Most production AI features use both. See our AI agent service for the agent side.
- Yes. We do full fine-tuning, LoRA / PEFT, and instruction-tuning of open models (Llama 3.3, Mistral, Qwen). Training stays in your AWS / Azure / GCP account — your data never leaves your infrastructure. SOC 2 / HIPAA architectures available.
- Yes — that's the part most teams underestimate. Training a model is 20% of the work; deploying, monitoring, and retraining it is 80%. We set up the full pipeline: experiment tracking (MLflow), model registry, A/B testing, drift monitoring, and auto-retraining.
- Computer vision (defect detection, document OCR, video analytics), NLP (classification, NER, summarization, search), predictive analytics (churn, fraud, demand forecasting), and recommendations. We turn most "we have data, what now?" conversations into a working model in 6 weeks.
Stop training notebooks. Start shipping models.
30 minutes. Zero pressure. Leave with clarity — even if we never work together.
Book My ML Strategy Call