63%
less time to first response
Support triage workflow, measured in production.
Forward-deployed AI workflow systems
Turn AI into measurable business results.
We find the workflow where AI creates measurable leverage, then build it into your tools, data, approvals, and metrics.
Built for production-minded teams
Built by engineers from Meta, Amazon, and D. E. Shaw.
Production systems • Workflow redesign • Governance and reliability
Proof snapshot
The work is judged by operating results, not by how impressive the demo feels.
These examples are taken from the same kinds of workflow changes we use to judge every engagement: response time, queue quality, and throughput in real work.
4×
fewer tickets needing escalation rework
Routing quality improved once context arrived with the ticket.
~70%
of routine NDAs cleared the same day
Legal intake shifted from queueing to policy-based triage.
Need help choosing?
Ask about your workflow.
Tell us what is slowing the team down. The copilot will point you to the right service, example, or next step.
Prefer async? Email us the workflow instead. hello@operatingleverage.org
How to work with us
Decide where AI fits, build the right system, then scale it with control.
The fastest way to understand the business is simple: we help companies choose the right workflow, ship it in production, and put the right controls around it.
Decide
Choose the first workflow worth pursuing and align the people who need to back it.
Build
Ship the workflow in production with retrieval, integrations, controls, and measurement.
Control
Make AI usable at scale with the governance, approvals, and operating boundaries it needs.
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.
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.
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.
How this plays out
What changes once a workflow gets an operating layer.
Each one follows the shape of a real engagement: what was true before, what we built, and what changed — measured the same way we measure every pilot.
Technology
A 40-person support team at a Series B vertical SaaS company
Inbound tickets sat in a shared queue for hours before anyone even looked at them. An agent now reads every ticket the moment it arrives, attaches the account history and policy context a human needs, and routes it to the right owner with a suggested first reply.
63%
less time to first response
Read the full story→
Technology
A 12-engineer product team at a growth-stage B2B platform
Every bug report started the same way: an engineer had to reproduce it, dig through logs, find the related code, and figure out where to even start. A background agent now does that first pass automatically — investigation summary, likely root cause, and a draft fix ready for review before a human picks up the ticket.
~15 hrs
engineer time returned per month
Read the full story→
Venture Capital
A 9-person investment team at an early-growth-stage venture fund
Deal history, diligence notes, and portfolio updates lived across Notion, a CRM, shared drives, and months of email threads — and finding 'what do we actually know about this company' meant pinging whoever remembered. An internal agent now answers that question directly, with sources, in seconds.
~80%
less time lost reconstructing deal and portfolio history
Read the full story→
Insurance
A regional insurance brokerage handling personal-lines claims and policy service for roughly 14,000 policyholders
Most calls came in after hours or during the lunchtime rush, and went to voicemail. A voice agent now answers every call live, takes a structured first notice of loss, checks policy status, and books appointments with an agent — handing off to a human the moment a call needs judgment.
~35% → ~95%
of inbound calls answered live, no voicemail
Read the full story→
Healthcare
A multi-site outpatient healthcare group operating six clinics with roughly 180 staff
Three AI pilots were already running quietly — one with access to patient records, one that could write into scheduling and billing systems, one drafting messages to patients — none of them governed, logged, or reviewed the same way. We brought all three under a single sandboxed, permissioned, and fully audited layer without shutting any of them down.
100%
of agent actions logged, permissioned, and reviewable
Read the full story→
Technology
A 25-person product and engineering org at a Series A startup
A constant stream of small 'can someone...' requests was flowing through Slack — pull a number, check a ticket, draft some copy — and they all landed on the same two or three people with the right access. An agent now sits in the team's Slack as a teammate: answers directly, works on bigger requests in the background, and reports back in the thread when it's done.
~25/week
ad-hoc requests handled directly in Slack, without pulling someone off their work
Read the full story→
Fintech
A payments fintech's compliance team monitoring transaction activity for roughly 120,000 active accounts
A fintech compliance team was drowning in transaction-monitoring alerts — most of them harmless, but each one took real digging to rule out. An agent now pulls account history, KYC data, and prior alerts the moment an alert fires, and hands the analyst a ready-made investigation packet with a recommended call.
~50%
of daily alerts cleared without a full manual investigation
Read the full story→
Legal
A 4-person in-house legal team supporting a roughly 300-person company with a steady stream of inbound NDAs and vendor contracts
Every inbound NDA sat in the same queue as contracts that genuinely needed a lawyer — even when it matched the company's standard terms almost exactly. An agent now checks every inbound document against the legal playbook the moment it arrives, clears what matches, and hands the attorney only the clauses that don't.
~70%
of inbound NDAs cleared the same day without attorney review
Read the full story→
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.
See what we deliver→You leave with
One workflow to test first
A clear ROI hypothesis
Architecture and governance boundaries
A practical next-step recommendation
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 Pareek
Co-Founder • Former Associate Director at UBS with experience in enterprise workflow transformation, regulatory delivery, and cross-functional program execution
Sumati spent over six years at UBS in Zurich, including as an Associate Director, working across strategic regulatory programs, HR transformation, and cross-functional delivery in high-accountability environments. Her background spans workflow design, planning, reporting, and execution across complex multi-stream programs. At Operating Leverage, she focuses on workflow discovery, execution design, and helping companies adopt AI in ways that are practical, measurable, and usable in real operating environments.
AI StrategyWorkflow OptimizationOperations
Start small, build seriously