Build AI-powered workflows that handle the repetitive so you can focus on what matters.
As many as 95% of AI projects fail to deliver a positive ROI. The reason is rarely the technology itself—it's that AI gets dropped into systems built for human-centric work. We help you find the right opportunities before you spend the money.
Most companies rush to deploy AI. They pick a workflow, bolt on some automation, and hope for results. Sometimes it works. Usually it doesn't—or the gains are so incremental they're hard to measure.
The issue isn't the AI. It's the approach.
AI works best when workflows are redesigned around it, not when it's patched onto processes built for humans. The best early wins are usually boring: fewer handoffs, fewer checks, less rework, fewer escalations. But finding those wins requires understanding how work actually flows through your organization.
We start from the bottleneck, not the technology. We map your workflows end-to-end, baseline what they cost today, and rank automation opportunities by ROI and difficulty.
Each opportunity gets a simple one-page brief: current cost, target lift, controls, and time-to-value. No 200-slide decks. No 18-month discovery phases.
We built Optic to make this process repeatable. It's an analytics tool for tracking, identifying, and assessing AI automation opportunities.
Map how work actually gets done—across people, systems, and handoffs. Understand where time goes and where it gets wasted.
Translate that understanding into a ranked backlog of automation opportunities. Each one prioritized by ROI, difficulty, and readiness.
Measure before and after. Track the impact of automations over time. Keep the backlog current as the business changes.
Optic is deeply integrated with your data, workflows, and operations. We embed with your team to deploy and maintain it.
Map workflows, baseline cost, identify bottlenecks, produce a ranked ROI backlog. We usually surface 15-30 plausible opportunities quickly, then narrow to 3-5 high-confidence bets.
Ship 1-2 automations quickly with clear measurement on cost and speed. No pilot theater—real workflows, real metrics.
Turn success into a repeatable playbook. Expand to more workflows and, when it makes sense, more business units.
Continuously measure performance and revise the opportunity set. AI capabilities evolve fast—your automation strategy should too.
When we find the right bottlenecks, the numbers speak for themselves:
These aren't hypotheticals. They're the ranges we see when the bottleneck is real and the measurement is honest.
Invoice reconciliation at scale. A back-office team was spending hours a day reconciling mismatched invoices because three systems didn't agree on a single field. The highest ROI wasn't "automate invoice coding." It was eliminating the mismatch with automated validation and clean upstream fixes—which cut rework and shortened the close cycle.
Support triage that actually triages. A support organization thought the problem was "agent productivity." The bottleneck was actually triage and handoff. Automating categorization, pulling context, and drafting first responses reduced time-to-first-response and lowered escalations, which let them handle more volume without adding headcount.
Deal velocity through evidence collection. A revenue ops process kept slowing down deals because approvals required manual packaging of evidence across tools. Automating evidence collection and generating a review-ready packet cut cycle time and reduced back-and-forth loops.
Let's map your workflows and identify where AI can move the numbers.