Feasibility & baselines
Check labels, coverage, and data leakage. Define success criteria and compare against a simple reference approach.
DATA INTELLIGENCE / Custom Machine Learning
Machine learning with a clear job to do.
From natural language processing and computer vision to forecasting and recommendation systems, we develop models around your data and operating requirements, from feasibility to deployment.
Discuss your requirementsWHERE WE CAN HELP
Recognize the challenge?
- 01A generic model that does not fit your domain
- 02A promising prototype without a deployment plan
- 03Model quality that changes as production data evolves
WHAT WE BUILD TOGETHERA practical scope.
A practical scope.
Clear deliverables.
We shape the engagement around your current systems, priorities, and operating constraints.
Model development
Prepare data, train appropriate models, and evaluate performance on representative held-out examples.
Deployment & monitoring
Integrate inference into your application or pipeline, with versioning, operational monitoring, and a plan for quality review.
AN ILLUSTRATIVE APPLICATION
Route incoming documents
Train and evaluate a classifier for your document categories, with a review path for uncertain or unfamiliar inputs.
A possible engagement, rather than a completed client project.
BEFORE WE START
A few useful questions.
How much training data do we need?
It depends on the task, model, and variation in the inputs. We assess representative examples and label quality before making a feasibility recommendation.
How do you define a successful model?
Together we choose task-specific metrics, operational limits, and the cost of different errors. An aggregate accuracy number rarely captures all three.
CONNECTED CAPABILITIES