We build generative AI that is accurate, evaluated and safe: RAG, domain-specific models and agents, with guardrails and human review.
What We Deliver
Domain-Specific Models
Choose the right foundation model, adapt it with your data and test it against your requirements.
Generative AI Consulting
An adoption strategy and the right solution for your use case.
Data Analysis
Patterns and recommendations from structured data and unstructured text, traceable to sources.
How We Build
- 1
Requirement Assessment
Needs, objectives and constraints.
- 2
Strategy Building
Model choice and deployment plan.
- 3
Design & Development
Architecture and a tuned model.
- 4
Testing
Evaluation, red-teaming, guardrails.
- 5
Deployment & Optimization
CI/CD with observability.
- 6
Integration & Support
Fit into your ecosystem.
- 7
Upgrade & Maintenance
Regular upgrades keep it sharp.
Techniques We Use
Machine Learning Algorithms
Decision trees, regression, random forests, SVM and KNN.
Deep Learning
Neural networks, CNNs, RNNs and autoencoders.
Supervised Learning
Curated labeled data and tuned model architectures.
Unsupervised Learning
Patterns and relationships found in unlabeled data.
Transfer Learning
Pre-trained models fine-tuned on curated datasets.
Reinforcement Learning
Agents trained on environmental feedback using Markov Decision Processes.
Natural Language Processing
NLTK and SpaCy: tokenization, stemming and lemmatization.
Computer Vision
Feature extraction and image processing with OpenCV and TensorFlow.
AI Technology Stack
Cloud Platforms
AWS
Google Cloud
Azure
AI Frameworks
TensorFlow
PyTorch
Integration & Deployment
Docker
Kubernetes
Databricks
Airflow
Programming Languages
Python- SQL
Java
JavaScript
TypeScript
Visualization
- Tableau
Looker- Power BI
Collaboration
GitLab
Jupyter Lab
Jira
Confluence
Frequently Asked Questions
What is RAG?
Retrieval-augmented generation lets a model look up your documents before answering, so responses are current, specific and traceable to their sources.
How do you evaluate generative AI?
Automated test sets, regression suites and red-teaming measure quality and safety before and after each release.
Which models do you use?
Leading proprietary and open-weight language models, chosen per use case for accuracy, cost, latency and data requirements.