Duanex — AI Agents Studio

Duanex was professional and delivered high-quality work within a short timeframe. Their developers worked hard to meet our requirements and handled the challenging project very well.

Our dispatchers were buried in exception handling — delayed pickups, route changes, driver check-ins — across multiple regional hubs, leaving little time for actual planning. Duanex built an agent to catch these exceptions, pull context, and either resolve simple cases or hand dispatchers a ready-to-act summary. The project took about 9 weeks to reach a working pilot. We hit a point where the exception-detection logic needed retraining once we realized each hub operated differently, but instead of forcing a generic solution, they adapted it to work properly across all our regional variations. Once that was sorted, dispatchers noticeably spend less time on routine triage now. Weekly check-ins kept us aligned throughout. The main lesson: regional differences should have been mapped out during discovery. That gap surfaced partway through and created some rework, but aside from that, they handled operational complexity well and stayed committed to getting it right for our workflow.

Our finance team was manually reviewing every flagged expense report, but volume had outpaced what our small team could handle. Duanex built an agent to scan submissions, detect policy anomalies, and automatically route clear approvals while flagging unusual cases for human review. We reached a working pilot in about seven weeks, mostly spent tuning anomaly thresholds to get the sensitivity balance right. They communicated well and adjusted quickly based on our real usage feedback—they didn't rush the process and stuck with it until the agent worked exactly as we needed. A few multi-currency edge cases slipped through initially and required a follow-up fix after launch; more explicit test coverage for those scenarios before go-live would have prevented that.

We were a small stealth-mode team with no dedicated sales function, and inbound interest from investors and early customers was piling up unanswered. Duanex built us an agent that reads incoming messages, researches who's reaching out, and drafts responses for us to review before sending. What mattered most was their flexibility. Our priorities shifted constantly week to week, and we honestly didn't know what we needed until we saw something working. They adapted without friction instead of pushing back on scope changes. They kept the build lean and got us something usable in about five weeks—realistic for a bootstrapped team. The tradeoff was a few edge cases in message classification that needed manual fixes after launch, which is an expected reality when you prioritize speed on a lean build. If you're early-stage and need something more hands-off from day one, that's worth considering when deciding if this approach fits your timeline.

Our compliance team handles a steady stream of vendor security questionnaires, and most of the work was repetitive—the same handful of questions worded slightly differently each time. Duanex built an agent that drafts first-pass answers from our knowledge base with source citations, and routes anything it's not confident about to a human instead of guessing. Getting to a working pilot took about 8 weeks, with extra tuning rounds early on to cut down false-confidence answers—worth the time given the stakes. Communication was consistently strong, and they pushed back constructively when a request would've traded accuracy for speed. The main gap was handoff documentation for our team, which arrived later than expected and needed several revisions before it was complete. Having that ready earlier would've gotten us up to speed faster post-launch.

We had a clear problem: our reps were spending too much time manually triaging claims and qualifying leads. We needed an AI agent that could handle the repetitive first pass without sacrificing accuracy. Duanex stood out because they focused on autonomous agents integrated into our existing systems, rather than a generic chatbot solution. The discovery phase went smoothly—we had our claims criteria and lead qualification rules documented, so they mapped those quickly. Development happened in two-week sprints with weekly syncs and a shared Slack channel keeping everything transparent. The agent works well: it pulls policy data, applies our auto-approval logic, and routes anything ambiguous to a human with full context attached. That's significantly cut down manual triage time. The main friction was CRM integration taking longer than expected, but once we clarified legacy-system compatibility upfront, everything stayed on track.

We hired Duanex to build an AI agent that handles two key problems for our dispatch team: managing the high volume of routine shipment-status inquiries from customers and carriers, and flagging exceptions like delayed shipments, missed pickups, and customs holds. Our dispatch team was spending way too much time answering repetitive status questions instead of actually resolving exceptions, so we needed the agent to absorb that routine workload and free them up for work that actually requires judgment.

We brought Duanex in to build an AI agent for first-notice-of-loss intake that collects incident details, verifies policy coverage, and routes claims to adjusters or processes them automatically. As a smaller carrier without internal engineering capacity, we needed a partner who could work independently while keeping us informed—pulling engineers away from core product work wasn't an option. They started with a pilot scoped to renters' claims before expanding to auto and pet once that was validated. Their team was upfront about what the agent could and couldn't handle reliably at each stage, which helped us set realistic expectations internally. Once live, the agent noticeably cut our average FNOL processing time and freed adjusters to focus on complex or disputed claims that needed human judgment.

We brought Duanex on to handle a growing volume of customer support tickets—account questions, transaction disputes, and early-stage fraud flags—without scaling our support team proportionally to our user growth. As a fintech, compliance and data handling were critical concerns, so vendor trustworthiness was essential to how they approached sensitive workflows.

We worked with Duanex over three months to build an AI agent for patient intake triage and support automation. The agent handles initial triage of scheduling requests and routes complex cases to our staff—work that was consuming significant time on routine, repetitive inquiries. Since we lacked in-house AI expertise, Duanex guided the technical approach while keeping us informed on trade-offs at each step.