We’ve spent years running multi-party, time-sensitive, high-volume B2C operations for travel-tech at scale. We’re applying that same operating model to dedicated tech-support pods, and we’re taking one to two early customers in 2026.
Most tech-support BPO pages claim FCR over 75%, MTTR under 4 hours, sub-15% Tier 2 turnover, and a comparison table that makes the writer look brilliant. We don’t have those numbers yet, and we won’t manufacture them.
What we have is an operating model that runs the entire B2C function of a Trivago-owned OTA at scale. 100+ agents on that engagement alone. Tier 1 through Tier 3 escalations. Multi-party coordination across customers, suppliers, and partners. Time-sensitive escalations. Daily reconciliation across systems that don’t agree with each other.
If that operating reality sounds like tech-support escalation architecture to you, it should. The lifecycle is similar. The failure modes are similar. The talent profile is similar. We’re now extending it into dedicated tech support, and we want to do it with SaaS, fintech, or platform companies who’d rather build the operation with us than wait for it off-the-shelf.
The operational shape of tech-support escalation is not new to us. Different vocabulary, same architecture.
Same escalation problem. Same failure modes when context is lost between tiers.
Both businesses run on operational rhythm. Both pay heavily when it breaks.
We’ve spent years operating in the gap between people who don’t share context. That’s a transferable skill.
Each will run as its own discipline.
Early engagements start with one or two functions. Nobody buys the full stack on day one. The list below is what we’d build out together over a 90-day pilot.
Where most ticket volume lives, and where AI augmentation does the most work.
Inbound triage across email, chat, voice, and in-app. Account access, billing questions, common configuration, FAQ patterns. AI-augmented for routine paths, human-handled for everything that requires judgment.
Knowledge base curation, runbook authoring, runbook gap-closing from the QA loop. The single biggest controllable driver of L1 quality is runbook depth. We treat it as a permanent operational function, not a one-time project.
Pairing AI agents with the human pod. Tuning the AI to your product, your runbooks, your tone. Defining where AI handles cleanly and where it hands off to a human. The boundary design matters more than the model choice.
Where escalations live, and where in-house teams most often break.
API errors, integration edge cases, permission and access issues, complex configuration, customer-side SDK problems. Senior support engineers, deep product knowledge, structured handoff packets from L1.
Reproducing customer-reported issues, root-causing API errors and integration problems, distinguishing bugs from configuration from user error. Drafting clear bug reports for your engineering team so they don’t have to investigate the report itself.
Every L2 resolution becomes an L1 runbook update so the same ticket type doesn’t re-escalate next time. The function most BPOs skip and the one that compounds quality over time.
The functions that matter when something is on fire, or when no one in-house is awake.
Off-hours, weekend, and holiday coverage so your in-house engineers don’t burn out on rotation. Same QA bar at 3 AM as at 3 PM.
Customer communications during outages, status-page updates, mass-ticket triage, post-incident customer outreach. Coordinated with your engineering on-call team.
Email, chat, voice, in-app, with full context preservation across channels. The customer who starts in chat and continues by email doesn’t have to start over.
We haven’t run a dedicated tech-support engagement yet. We have spent years thinking about how this kind of operation should work, watching it succeed and fail across the SaaS and platform companies we’ve sat next to. The playbook below is what we’d commit to building with the first one or two customers, not what we claim to deliver today.
Most under-performing tech-support operations have ambiguous tier boundaries. L1 escalates for safety. L2 escalates because the boundary is unclear. L3 absorbs work that L2 should own. The single highest-leverage design decision is writing the L1 / L2 / L3 boundary down per ticket category and auditing it monthly.
When L1 escalates to L2, ship a structured packet: customer state, ticket transcript, attempted resolutions, working hypothesis, expected SLA. Programs where L1 writes a free-form note lose information at every handoff. Programs with a structured packet protect FCR, MTTR, and CSAT in one design choice.
Every L2 resolution should produce an L1 runbook update so the same ticket type doesn’t re-escalate. Programs that treat runbook maintenance as a one-time project see escalation rates creep up over time. Programs that treat it as a permanent operational function see escalation rates compress.
Being early customer number one is different from being customer number fifty. The early engagement comes with things later customers don’t get.
The first dedicated tech-support engagement is run by Arbitrail leadership, not handed to a project manager. You talk to the people who’ll architect the operation, not to a sales engineer.
The pod is built around your stack, not adapted from a generic support template. Your product, your runbooks, your tone, your escalation paths. We’re learning your tech stack from you, and we adjust the model to fit what you actually do.
Every workflow we build with you gets documented and refined. You get the playbook. We get the operational learning. Both sides win.
Early customers price differently than year-three customers. We’ll lock in pricing that reflects the partnership, not the standard rate card.
No account management layer between you and decision-makers. Issues escalate to founders in hours, not weeks.
We are the right partner for one or two specific kinds.
Founder-led conversation. We map your current support operation to our operating model. We tell you what we’d commit to and what we wouldn’t.
Scoped pilot. One tier, one channel, one product area if applicable. Volumes, SLAs, and pricing agreed in writing. Helpdesk integration and runbook ingestion start here.
Dedicated pod stood up. Daily monitoring, weekly business reviews, monthly executive readouts. Real volume, real results, real numbers.
You decide. Continue and expand, continue at pilot scale, or end with a clean exit. Pilot pricing reflects the risk you’re taking on us, not the other way around.
You contract with one Arbitrail SG entity. One PM is accountable. One team delivers.
The model we built for travel-tech ops is going to translate into dedicated tech support. We’re certain of that operationally. We just need the first one or two customers to prove it together. Late customers will buy a proven service at standard pricing. Early customers will build the proven service with us, at terms that reflect the partnership.
If you’re the kind of support leader who’d rather shape what comes next than buy what already exists, we should talk.