Custom ML models for prediction, forecasting and detection, taken to production and kept accurate with MLOps.
Stack we work in
Figures as of Oct 2026
Trusted by
We build machine learning models that predict demand, score risk, classify records, recommend products and flag anomalies in your own data.
Then we put them into production with MLOps: automated pipelines, a model registry, monitoring for drift and scheduled retraining. You get models your systems call every day, not a notebook that stops at the proof of concept.
Most ML work does not fail on algorithms. It fails on data, deployment and upkeep.
A promising proof of concept never reaches production because nobody owns packaging, deployment and integration.
History is patchy, outcomes are not recorded and features live in several systems, so models cannot learn the right signal.
Customer behavior, prices and processes change, and a model with no monitoring keeps making worse predictions without anyone noticing.
Nobody can say which data, code and settings produced the model in production, which makes fixes and audits slow.
Business teams ignore a score they cannot explain, so the model never changes a decision.
Outcomes
The decision the model supports, the success metric, the data sources and any gaps to fix before the build.
The selected model with its evaluation report, tested against business metrics and a simple baseline.
The model deployed as an API or batch job and connected to your systems, with versioning in a model registry.
Automated, repeatable pipelines that rebuild, test and release the model from source code and data.
Dashboards and alerts for drift, accuracy and service health, plus a retraining pipeline with approval steps.
Repositories, feature definitions, model cards and a runbook handed over to your team.
Forecast sales, stock and staffing needs by product, store or region so planning starts from data.
Score which customers are likely to leave or grow, and route them to the right retention or sales action.
Score applications and transactions, and flag unusual patterns for review before losses happen.
Rank products and content for each user based on behavior, purchases and similar customers.
Use sensor and maintenance history to predict failures and schedule work before equipment breaks down.
Sort incoming documents, emails and tickets by type, priority or team so work reaches the right queue.
Built with
Built with
AWS SageMaker
Azure Machine LearningWe check data quality and coverage first. If the data is not ready, you get a data plan rather than a model that fails quietly.
Models are scored offline against the business metric you care about, then checked in shadow or A/B tests before they replace the current process.
Training and deployment pipelines, a model registry in MLflow and drift monitoring are part of the first delivery, not a later phase.
SageMaker, Azure ML, Vertex AI or Databricks, built by a team with 20+ certified cloud and data platform engineers.
Code, pipelines and documentation are handed over under NDA, with an optional retainer for monitoring and retraining.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We agree the decision the model will support, the metric that defines success and the data available. A short data audit shows whether the signal is there before any build starts.
You receive Use case brief and data readiness report
We build a simple baseline and a first model on historical data, so you see realistic accuracy early and can decide whether to continue.
You receive Baseline results and go or no-go recommendation
We engineer features, compare algorithms and tune the best candidate, tracking every experiment. Results are checked against business metrics, not only statistical scores.
You receive Validated model and evaluation report
We package the model as an API or batch job, add it to the model registry and connect it to your systems through an automated deployment pipeline.
You receive Model in production with CI/CD pipeline
We set up monitoring for data drift, prediction quality and service health, with alerts and a retraining pipeline that runs on a schedule or when drift is detected.
You receive Monitoring dashboards and retraining pipeline
We document the pipelines, train your team and stay on for maintenance, new features or the next model if you need us.
You receive Runbook, documentation and support plan
Tech stack
AI and machine learning
AWS SageMakerAzure Machine LearningData platforms
Pipelines and orchestration
Backend and APIs
Languages
Cloud and DevOps
Other tools
Data first
Good models start with reliable data pipelines. We can start with a short data readiness check and tell you what to fix before any model work begins.
Cost depends on the state of your data, the number of models, how often they must run, how they connect to your systems and how much MLOps the use case needs. We price a short discovery and feasibility phase first, then give a fixed estimate or a monthly team cost for the build. Cloud running costs for training and serving are estimated separately. As a guide, a focused AI pilot usually costs $10,000 to $50,000, and a full custom AI build $40,000 to $150,000 or more (USD).
It depends on data readiness and integration. Most projects take a few weeks for a feasibility baseline and a few months to reach a monitored model in production. We share a plan with milestones after discovery.
Often yes, but it depends on the problem. The data audit at the start checks volume, history, quality and whether the outcome you want to predict is recorded. If the data is not ready, we tell you what to collect or fix first rather than building a model that will not hold up.
MLOps is the engineering that keeps models working after launch: automated training and deployment pipelines, a model registry, monitoring and retraining. Without it, models are hard to update and lose accuracy quietly as your data changes. We set up the level of MLOps your use case needs, from a scheduled batch job to full CI/CD for models.
We monitor input data and predictions in production with tools such as Evidently AI, compare them with the training data and track accuracy once real outcomes arrive. Alerts fire when drift crosses agreed limits, and a retraining pipeline produces a new model that is tested before it replaces the old one.
Yes. We build on the platform you already use, such as Databricks, SageMaker, Azure Machine Learning, Vertex AI or your own Kubernetes cluster, and work alongside your data engineers and analysts. Code goes into your repositories and follows your review process.
Models are trained in your cloud account or an environment you approve, access is role-based and data is encrypted in transit and at rest. We use only the fields a model needs and mask personal data where possible. Data handling is designed around GDPR and the local data protection rules that apply to you.
You own the code and IP we build for you, including trained models, pipelines, feature code and documentation. Everything is delivered into your repositories and cloud accounts.
Yes. We use explainability methods such as SHAP values and feature importance so you can see which factors drove each prediction. This helps business owners trust the output and supports reviews in regulated areas such as credit or insurance.
We offer monitoring, retraining, bug fixes and improvements on a monthly retainer, or a handover to your team with documentation and training. Many clients start with support and move to in-house ownership over time.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. The first two weeks are a data audit and a baseline model, so you know early whether the data supports the use case.
Beyond one-off models, we build and run the systems that keep predictions useful in production.
Predictive analytics and forecasting
Demand, revenue, cash flow and capacity forecasts with Prophet, gradient boosting or deep learning, refreshed on a schedule and shown in your BI tool.
Recommendation systems
Product, content and next-best-action recommendations from behavior and purchase history, with A/B testing to measure uplift.
LLMOps and AI monitoring
Tracing, evaluation, cost and drift monitoring for LLM and agent applications in Langfuse or Arize Phoenix, with alerts when quality drops.
Model monitoring and retraining
Data and prediction drift checks with Evidently AI and automatic retraining pipelines that ship only when the new model scores better.
Testing AI before release
Evaluation and red-team testing for LLM features, so changes are checked before users see them.
Learn moreBook a free consultation. We will review your use case and data and suggest a first model to pilot.
