Use case and data
We define which decision the model will improve, with which data and how the result will be measured.
Deliverables
- Use case with a business metric
- Assessment of available data
- Baseline for comparison
Machine learning and MLOps
Machine learning that actually reaches production and keeps working
Forecasting, classification and recommendation models trained on your data, with automated deployment, monitoring and retraining, and compute costs under control.
- First call at no cost
- Kickoff in 2 weeks
- Models and data in your cloud
Team engineering proven at
- Dow Jones
- LATAM Airlines
- TelevisaUnivision
- Deloitte
- Google Cloud
What we solve
When the model works on a laptop but not in production
It's the most common problem with machine learning in companies. These are the symptoms.
Models that never leave the pilot
They work in a notebook, but nobody knows how to put them into operation.
Compute costs that spike
Training and inference that cost more than they deliver.
Models that lose accuracy
Data changes over time and nobody notices in time.
Every model is a special case
Hand-built pipelines that can't be reused.
No tests before release
A new version ships without knowing if it's better than the last.
Forecasts in spreadsheets
Inventory, demand or pricing decisions made by eye.
What we do
From use case to a model in production
Model and pipelines
We train the model on your data and build modular, reusable training and inference pipelines.
Deliverables
- Trained and validated model
- Training and inference pipelines
- Components reusable across models
MLOps in production
Deployment, monitoring, evaluation before every release and retraining, with compute cost in view.
Deliverables
- Evaluation before every release
- Accuracy and drift monitoring
- Compute cost control
Team experience
MLOps the team already runs at scale
Machine learning platforms our team designed and operated in media and financial information.
Media · financial information
MLOpsChallenge
The cost of training and serving models grew with every new model.
What was built
Modular, reusable MLOps components, with pipelines designed not to repeat work.
Media · financial information
ML migrationChallenge
The data and machine learning platform had to change clouds.
What was built
Migration of the data and ML footprint from GCP to AWS with Snowflake, with orchestration rebuilt on MWAA.
Media · financial information
EvaluationChallenge
Each new version of an agent could get worse without anyone noticing.
What was built
LLM-as-a-judge evaluators and golden datasets inside the deployment pipeline.
How we work
Short sprints, weekly demos, no black boxes
- 1
First call
no costA conversation with a solutions architect to understand your challenge and tell you honestly whether we are the right fit.
- 2
Assessment and plan
in writingWe review your systems and goals and deliver scope, technology, timeline and price. You approve before we start.
- 3
Sprints with demos
every weekYou see working progress, not slide decks. We adjust priorities with you along the way.
- 4
Delivery and handover
documentedCode, documentation and sessions with your team so it can operate without us.
Security
Your data and your code, under your control
We work inside your accounts and by your rules. When we finish, everything stays with you.
NDA before we start
We sign a confidentiality agreement before seeing any system or data.
The code and credentials are yours
Repositories, cloud accounts and keys in your company's name.
Least-privilege access
We only ask for what each task needs, and you can revoke it at any time.
Documentation included
Architecture, operations and technical decisions in writing with every delivery.
Engagement models and pricing
How to work with us
The first call is at no cost. Then you choose: an assessment to define the plan, a fixed-scope project or a team that moves forward with you month by month.
01 · Assessment
Assessment
On request
Billed hourly
- Sessions with a solutions architect
- Review of your systems, data and goals
- Written plan with scope, technology and price
02 · Per project
Per project
On request
Fixed price
- Fixed scope and price
- Payments per delivered milestone
- Weekly progress demo
- Documentation and handover
03 · Dedicated team
Dedicated team
On request
Fixed monthly fee
- 2-week sprints
- Cloud, data, software and AI in one team
- Scale up or pause with no penalty
- One assigned technical lead
Prices in USD, before VAT. The final price depends on scope.
FAQ
Frequently asked questions about machine learning
What is MLOps?
The practices to take models to production and keep them there: automated deployment, monitoring, evaluation before every release and retraining when the data changes.
Do we need a lot of data to start?
It depends on the case. In the assessment we review your data and tell you honestly whether it's enough or what's missing.
How do you keep compute costs from spiking?
With modular pipelines that reuse work across models and cost measured from the start. On one of the team's projects that cut compute by up to 90%.
Which cloud do the models run on?
Yours: Vertex AI on GCP, Snowflake or AWS, wherever your data already is.
Who owns the code and infrastructure?
You do. Everything lives in your accounts and repositories from day one, with documentation so your team can run it.
How much does it cost?
The first call is at no cost. The assessment is billed hourly and ends with a written plan with a price; then you can choose a fixed-scope project or a dedicated team.
Tell us which decision you want to improve with machine learning
On the first call, at no cost, we review your case and your data and propose the next step: an assessment with a written plan, a project or a dedicated team.
- First call at no cost
- Reply within 1 business day
- NDA if you need it