Predictive analytics
Churn, demand, credit risk, next best action: models that score real events instead of describing them on a dashboard. Every model is validated against a baseline, so you know exactly what it adds.
AI & Data
Machine Learning Solutions
Models that earn their place in production. We prove the value on your data in weeks, then engineer the pipeline, deployment and monitoring around it.
2 wksfrom kickoff to a first working prototype, typically
4-6weeks to a production-grade pilot, measured on your data
100%senior engineers on every engagement
0lock-in: code, weights and pipelines are yours
What we build
Six capability areas, one standard: every model ships with an evaluation harness, a deployment path and a monitoring plan.
Natural language processing
Extraction from contracts and invoices, classification of tickets and mail, assistants grounded in your documents. We combine LLMs with classic NLP wherever each is cheaper and measurably better.
Computer vision
Defect detection on the line, OCR on messy scans, counting and tracking on video streams. We train on your images and test in the conditions your cameras actually see.
Deep learning
For when gradient boosting stops being enough: sequence models, embeddings, multimodal networks. We reach for deep learning where the data justifies it, and say so plainly when it does not.
MLOps & deployment
Versioned data, reproducible training, CI for models, drift alerts on live traffic. The unglamorous plumbing that decides whether your model survives month six.
Custom model development
Models trained or fine-tuned on your data, for your metric, owned by you. Weights, code and pipelines are handed over, not rented back to you.
What you walk away with
Artifacts, not slideware. Every engagement ends in things your team can run without us.
Evaluation harness & metrics report
A repeatable test suite with the metrics that matter for your case, plus a written report of how the model scores against the baseline.
Deployed model behind an API
A containerized service with authentication, versioned endpoints and load tests for your expected traffic.
Training & fine-tuning pipeline
Scripted, reproducible training from raw data to registered model. Rerun it on new data without reverse-engineering a notebook.
Monitoring & drift alerts
Dashboards for latency, cost and prediction quality, with alerts when input data or model behavior drifts from the baseline.
Model cards & documentation
What the model does, what data it saw, where it fails and where its limits are. Written for engineers, auditors and your future team.
Handover & runbooks
Step-by-step runbooks for retraining, rollback and incident response, plus handover sessions with the engineers who will own the system.
AI & Data
How we deliver
01
Discovery
We start with your business metric, not an algorithm. One or two workshops define the prediction target, the baseline to beat and what a win is worth in money.
02
Data preparation
We audit what data you actually have, then build the datasets and features the model will train on. Leakage checks and a clean train/test split happen here, not after the demo.
03
Model development
We train simple baselines first, then add complexity only where it pays for itself on the evaluation set. Every experiment is tracked, so results stay reproducible and comparable.
04
Deployment & monitoring
The model goes live behind an API with monitoring, drift alerts and a rollback path. We watch the first weeks of real traffic with you and tune before handover.
Ways to engage
Three shapes of engagement, one rule: fixed scope wherever possible, senior engineers always.
Feasibility study on your data
2-3 weeks · fixed scope
Before you commit budget, we test whether your data supports the model you have in mind. You get numbers, not opinions.
- Data audit: coverage, quality, leakage risks
- Baseline model and honest first metrics
- Go / no-go recommendation with reasoning
- Costed roadmap if the answer is go
Production pilot build
4-6 weeks
One use case taken to a production-grade pilot: trained, evaluated, deployed behind an API and measured on live data.
- Training pipeline and evaluation harness
- Deployed model with monitoring and alerts
- Weekly demos on your real data
- Rollout plan for scaling past the pilot
Embedded ML team
Monthly · ongoing
Senior ML engineers inside your team: shipping models, building MLOps discipline and raising the level of your own people.
- Hands-on delivery, not slide decks
- MLOps standards your team keeps after we leave
- Pairing and code review with your engineers
- Capacity that scales with your roadmap
Who this is for
Product teams with a validated use case
You know what the model should do and roughly what it is worth. We take it from idea to a measured pilot in weeks.
Companies with data but no ML team
Years of operational data, nobody to model it. We bring the team, build the first systems and leave you able to run them.
Teams stuck at the notebook stage
The prototype works on a laptop and dies in review. We turn notebooks into pipelines, tests and deployments that survive production.
Scale-ups that need MLOps discipline
Models are live, but every retrain is manual and nobody trusts the metrics. We install the pipelines, monitoring and process to scale safely.
Tools we work with
PyTorch
scikit-learn
Hugging Face
OpenAI & Anthropic APIs
LangGraph
Ray
ONNX
Weights & Biases
MLflow
Vertex AI
AWS SageMaker
Vector databases
Airflow
dbt
FastAPI
Docker & Kubernetes
Go deeper
Five specializations under machine learning, each with its own playbook.
Questions, answered
Build a custom model or buy off-the-shelf?
Buy when a general model solves it, build when your data or domain is the edge. We benchmark both, then recommend the cheaper path to your accuracy target. No dogma.
How do you stop hallucinations?
RAG grounds answers in your sources, backed by guardrails, citations, and automated evals on every release. We measure hallucination rate and gate deploys on it. Trust comes from tests, not hope.
Do we keep the IP and models?
Yes. You own the code, weights, and pipelines outright. No lock-in, no rent on your own models. We hand over everything and document it so your team can run it.
How do you keep models accurate after launch?
MLOps with monitoring, drift alerts, and eval suites in CI. When quality slips, we retrain or tune fast. Accuracy is a system we maintain, not a launch-day snapshot.
We don't think we have enough data. Is ML off the table?
Usually not. Pretrained models, fine-tuning and augmentation lower the bar, and a feasibility study tells you in two weeks what your data actually supports. Sometimes the answer is to start collecting the right data now, and we will say exactly that.
How do you measure success?
Before we train anything, we agree on the metric, the baseline to beat and the threshold that makes production worthwhile. Every demo reports against that harness. If the numbers are not there, you find out in weeks, not after a year of budget.
Ready to put a model in production?
Book a call with a senior engineer. We will tell you in 30 minutes whether your use case is worth a feasibility study.
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