Technologies We Work With
We stay at the frontier, working with the latest and most powerful AI frameworks and infrastructure tools across the entire stack.
Large Language Models
Orchestration Frameworks
Vector & Memory
Infrastructure
Our Process
A proven, repeatable methodology that takes you from initial idea to intelligent production-ready AI systems.
Discovery
We begin every engagement with a deep discovery phase designed to uncover not just the obvious pain points, but the underlying systemic opportunities that AI can unlock. Our discovery process combines structured stakeholder interviews across technical, operational, and executive levels with a hands-on technical audit of your data, systems, and existing tooling. We map your current workflows, identify bottlenecks, and model the potential ROI of AI interventions with financial rigor. Discovery typically takes 1–2 weeks and results in a clear opportunity landscape report that guides everything downstream.
- Stakeholder interviews across all levels
- Technical audit of data and infrastructure
- ROI modeling and business case development
Strategy
With discovery insights in hand, we design a tailored AI architecture and phased implementation roadmap that aligns with your technical constraints, risk tolerance, and business timeline. Strategy is where we make the difficult prioritization decisions: which use cases to pursue first for maximum ROI, which to defer, and which to avoid entirely. We evaluate build vs. buy decisions for every component, recommend the appropriate LLM stack for your requirements, and design the security and data architecture. The strategy deliverable is an actionable blueprint that your internal team can understand and contribute to — not a black-box plan that creates vendor dependency.
- Architecture design and technology selection
- Use-case prioritization and phased roadmap
- Risk assessment and mitigation planning
Build
Our build process is structured around two-week delivery sprints, each ending with working, testable software rather than documentation or slide decks. We pair our AI specialists with your engineering team to ensure knowledge transfer happens continuously throughout delivery — not just at a training session at the end. Every sprint begins with a planning session and ends with a demo and retrospective, creating a fast feedback loop that lets us course-correct early rather than late. We maintain rigorous engineering standards throughout: code review, automated testing, security scanning, and performance benchmarking are non-negotiable, not nice-to-haves.
- Two-week agile sprints with working demos
- Paired development for knowledge transfer
- Automated testing and security scanning
Launch
Production deployment is the beginning, not the end. Our launch process uses blue-green deployment strategies to eliminate downtime risk, with automated rollback triggers if performance degrades. We set up comprehensive observability from day one: latency dashboards, error rate alerts, model quality metrics, and business KPI tracking all go live before your users do. The first 30 days post-launch are our hypercare period, with daily standups and a dedicated on-call engineer available around the clock. We do not consider an engagement complete until your team is confident running the system independently — and our documentation and training standards ensure they always are.
- Blue-green deployment with automated rollback
- Full observability and alerting setup
- 30-day hypercare with daily standups