Find out the challenges our platform can resolve

Production Challenges

Inefficient ML Teams

ML scientists and engineers are spending too much time on plumbing and low-value tasks, such as setting up their infrastructure and tooling, hacking around data pipelines, and building basic automation, delaying projects.

Technology Uncertainty

As new hardware products (Google TPUs, latest NVIDIA chips) and software products (Google AutoML) roll out, it becomes hard for decision makers to make long term decisions about ML infrastructure.

Low Hardware Utilization

Compute infrastructure is underutilized, leading to suboptimal costs and/or ML teams to battle over scarce resources.

Heterogeneity of skills and stacks

Traditional data scientists and IT managers are slowly migrating to new technologies (advanced ML and deep learning) and need to work alongside the new generation of deep learning native scientists.

Regulation and Privacy

Data is either critical and needs constraining security, and emerging regulation creates additional challenges (e.g. HIPAA, GDPR).

Human-centered design and UX

The abstract nature of many AI services and applications makes it difficult to think about interactions with humans. Given the fear of AI replacing jobs, the right UX becomes even more important to show how the two can collaborate for stronger results.

We make ML teams more efficient by setting up their infrastructure and tooling.

  • Model Prediction
  • Model Deployment
  • Model Versioning
  • Model Architecture, Model Evaluation, Model Training
  • Data Labelling, Data Versioning
  • Data Review and Filtering
  • Data Sourcing, Data Enrichment

We enable our clients to develop networked, distributed, and collaborative robotics by asking: how can many machines collaborate to achieve a common goal?


We help you with designing a data strategy for extracting the most value from your data.


We build the right data infrastructure required for production-level AI solutions.


We train your team by building an MVP of an AI solution that suits your business needs.


We deliver production-ready AI solutions, and help your team to fine-tune and build upon it.

AI’s impact is estimated to be 7-10% of industry revenues. Our industry agnostic AI infrastructure accelerates adoption for AI Pioneers so they can benefit from this positive revenue impact.

Deep Learning Techniques
Other Techniques
Reinforcement Learning
Recurrent Neural Networks
Convolutional Neural Networks
Generative Adversarial Networks
Tree-based Ensemble Learning
Dimensionality Reduction
Regression Analysis
Statistical Inference
Monte Carlo
Markov Process
Other Optimization
Law and compliance
Hight tech
Automotive and assembly
Healthcare systems and services
Basic material
Transport and logistics
Public and social sector
Pharmaceuticals and medical products
Consumer packages goods
Media and entertainment
Oil and gas
Utilities and resource
Advanced electronics/semiconductors
Aerospace and defense

Take our Prediction Dial survey to find out where you stack up against other industry players in AI adoption

New approach to AI

The Caliber Difference

AI-first companies

you've built your AI team

  • Productize AI
  • Support internal ML team
  • We build the “AI Platform”

AI-enabled companies

you're building your AI team

  • We take practical decision whether to use ML or DL
  • We build the end to end platform

Why Caliber?

  • Production-ready platform
  • Applied knowledge of AI/ML tools
  • We thrive on solving hard problems
  • Versatile and lean
  • We know what doesn’t work and are more efficient as a result
  • We have built products not just algorithms/services