AI Support Triage Agent
For companies with high-volume support and operations queues.
Turns messy inbound queues into ranked, owner-ready work with context attached.
Forward-deployed AI workflow systems
Become AI-native, one production workflow at a time.
We help companies become AI-native by identifying and deploying the workflows where AI creates measurable business value. We combine workflow discovery, agent architecture, custom retrieval, governance, and production engineering to turn AI from experiments into operating leverage.
AI workflow operating model
production-readyWorkflow discovery
Company context
Agent design
Sandbox
Model routing
Human approval
Evals
Audit trail
Production rollout
measurable leverage
Built for production-minded teams
Built by engineers from Meta, Amazon, and D. E. Shaw.
Production systems • Workflow redesign • Governance and reliability
Packaged workflows
Start with a workflow the team already feels every week.
The best first deployments are easy to recognize: there is manual work today, real context to wire in, and a before-and-after the team can actually measure.
Internal Knowledge Brain
For teams with scattered docs, Slack decisions, SOPs, and customer context.
Makes operating knowledge usable in the moment instead of buried across tools.
Engineering Backlog Agent
For product and engineering teams under constant delivery pressure.
Removes engineering drag around investigation, drafting, and review preparation.
How we work
From one painful workflow to a production-ready operating pattern.
The first engagement should leave the team with more than a prototype. It should make the next decision obvious: build, expand, or stop with confidence.
01
Bring the workflow
Start with one queue, handoff, or decision path that is expensive, repetitive, or slowed down by missing context.
02
Map the operating layer
We define the agent, retrieval, permissions, approvals, and success metric required to make that workflow safe and measurable.
03
Ship it where the work lives
We build the first workflow in the real environment so the system runs inside the tools the team already uses.
04
Leave with the next decision
Track ROI, tighten reliability, and decide whether to scale the pattern into the next workflow or stop with confidence.
“Demos are not production systems. Context is the product.”
We build where autonomy needs boundaries.
Example transformations
Show the organization what AI can actually change.
The first deployment should be specific enough to measure, but strategic enough to become a repeatable operating pattern.
From
Scattered docs
To
An operating brain that answers with provenance and routes the next action. What good looks like: ~80% less time lost hunting for the right answer.
From
Manual triage
To
Agent-routed requests with context, priority, owner, and recommended next step. What good looks like: ~60% less time spent on manual triage.
From
Backlog drag
To
Background agents that draft PRs, run checks, and leave review artifacts. What good looks like: ~15 engineer-hours back every month.
From
Unsafe pilots
To
Governed workflows with sandboxes, permissions, evals, and human control. What good looks like: every action auditable, every boundary enforced.
First engagement
AI Workflow Opportunity Assessment
A focused first engagement to identify the workflow where AI can create the clearest leverage, design the operating layer around it, and leave the team with a plan that is ready to act on.
You bring
- One workflow that already feels expensive, slow, or repetitive
- The tools and systems that workflow depends on
- A process owner who can describe what 'better' looks like
You leave with
One workflow to test first
A clear ROI hypothesis
Architecture and governance boundaries
A practical next-step recommendation
Why this first
By the end, the team should know what to build first, what not to automate yet, and what conditions need to be true before scaling further.
The builders
Architected by engineers from the world's most demanding systems.
We have built distributed systems at scale and understand what it takes to move AI from a demo to a survivable production workflow.
Rishabh Garg
Co-Founder • ex-Meta, Amazon, D. E. Shaw
Built AI and backend systems across Meta, Amazon, and D. E. Shaw, with a focus on production reliability, experimentation, data pipelines, and turning complex workflows into durable products. At Meta, worked on ads and business systems where correctness, scale, and operational trust were non-negotiable.
Generative AI & LLMsDistributed SystemsBackend Engineering
Sumati Anand
Co-Founder • AI strategy and workflow transformation
Driving AI strategy and workflow transformation to help companies build measurable operating leverage.
AI StrategyWorkflow OptimizationOperations
Start small, build seriously