What We Do
Building effective AI isn't just about picking a model—it's about the data, the integration, and the governance. We help you narrow down your data requirements, choose the right open-source base models, and design solutions that seamlessly integrate with your existing tools.
Identify high-value use cases and the data needed
to fuel them Prioritize open-source models for cost control and
ownership Deploy securely in your VPC or on-prem environment Recommended Base Models
We prioritize open-source architectures that give you full control. While we can work with proprietary APIs, starting with open weights offers the best long-term value and security.
LLaMA Family
Meta's state-of-the-art open models (e.g., Llama 3). Excellent general-purpose performance for chat, reasoning, and summarization.
Mistral / Mixtral
High-efficiency models known for speed and strong reasoning capabilities. Ideal for cost-sensitive, high-throughput applications.
Code & Specialized Models
Models fine-tuned for code generation (e.g., CodeLlama, DeepSeek) or specific domains like healthcare and law.
Security & Governance
The biggest barrier to AI adoption is data privacy. By using self-hosted local models, your sensitive data never needs to leave your infrastructure.
Data Sovereignty: Keep training
and inference data within your VPC or on-prem
servers. Access Control: Implement granular
role-based access to AI tools, just like any
other enterprise app. Auditable: Full logging of
prompts and completions for compliance and safety. Secure by Design
We help you design safe prompts and guardrails to prevent hallucinations and leaks.
VPC / On-Prem Deployment
Deploy models using Docker/Kubernetes in your own controlled environment.
Project Scoping & Delivery
Discovery
We identify your high-value use cases and audit available data sources (documents, logs, tickets).
Data Mapping
We classify and clean the data, determining what is suitable for RAG (Retrieval) vs. Fine-tuning.
Pilot
We build a proof-of-concept using a base model to validate the approach and define success metrics.
Scale-up
We optimize the model for production, deploy to your infrastructure, and integrate with your apps.
Employee Enablement
Technologies are only as good as the people using them. We provide advisory services to upskill your team.
Prompt Engineering
Best practices for getting consistent, high-quality outputs from LLMs.
AI Ethics & Safety
Understanding the limitations of AI, when to rely on it, and how to verify results.
Confidentiality
Dos and Don'ts for sharing information with AI models and tools.
Use Cases
R&D Labs
Problem: Scattered research data.
Solution: RAG system to query past
experiments.
Outcome: Faster hypothesis
generation.
Manufacturing
Problem: Complex machine manuals.
Solution: Chatbot for technicians
to troubleshoot.
Outcome: Reduced
downtime.
Professional Services
Problem: Repetitive report writing.
Solution: Auto-summarization and drafting
tools.
Outcome: Hours saved per
report.
Start Your AI Journey
Ready to build an AI solution that actually works for your business?