Workflow operations
50+ automation and AI workflows delivered across client environments
Across many engagements, we've led AI, agent, and automation work spanning discovery, solution design, implementation, and delivery oversight — turning manual, fragmented processes into systems that run.
Typical problems
- Manual reporting and admin-heavy processes
- Slow follow-up and fragmented internal workflows
- AI experiments that never reached production
Delivery pattern
- Identify the highest-friction workflow first
- Build the smallest useful operator or automation
- Add guardrails, approvals, and measurable outcomes
Enterprise data + AI
Modernized analytics and ML delivery for operations-heavy environments
In enterprise settings, we've helped build and scale cloud data pipelines, dashboards, and ML-enabled workflows used for operational insight and decision support — the foundations that production AI depends on.
What changed
- Faster access to operational data
- Better visibility for stakeholders via dashboards and reporting
- Stronger foundations for predictive and AI-assisted use cases
Why it matters
- Proves delivery beyond demos and prototypes
- Connects AI work to data quality and business operations
- Supports implementation in complex, real-world stacks
AI products & operators
We build the technology, not just the slides
Our own AI products — Yupiter and Evidloom — push the boundary of what agents and operators can do on a real machine. That R&D flows directly into client work, so engagements are built on current technology, not a stale playbook.
Representative themes
- Whole-computer operators across apps, browser, and files
- Scheduled, multi-step autonomous execution with logging
- Security-aware delivery with approvals where they matter
What clients get
- A fast first win instead of a long strategy deck
- Operator-style systems that work across real tools
- A partner that's building the frontier, not chasing it
