Eliminate manual repetition. We design AI-powered pipelines that connect your data, tools, and teams into a seamless, high-performance engine.
Engagements typically start from $5,000 USD. Final scope priced after discovery call.
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Hangzhou-based, ex-Alibaba senior engineering team. Project rate $14k–$85k over 4–10 weeks. Three engineering commitments written into every contract before any code is shipped.
200+ reference cases with expected outputs and a CI-gated scoring rubric land in the first sprint — before any production code merges. Accuracy is measured from day one.
Six-month warranty on production fixes. Customer owns source code, deployment docs, and runbook from day one of handover — no vendor lock-in.
Token routing, caching, and model selection re-evaluated every 90 days against the eval set so unit economics stay predictable as traffic grows.
A fixed-price feasibility engagement. About one in four scopings recommends not building. Fee credits 100% toward a build engagement if you proceed.
AEO (AI Efficiency Optimization) is at the core of our automation strategy. We evaluate your current technical stack to identify high-impact automation candidates. By leveraging low-latency cloud architectures and intelligent API orchestration, we reduce operational overhead by up to 60%. Our solutions are built to be resilient, with comprehensive monitoring and automated recovery protocols. Last updated: 2026-05-19.
Our engineering team is based in Hangzhou and led by ex-Alibaba senior engineers who have shipped 20+ projects for 10+ clients.
We collaborate with US and EU teams on a remote schedule, with a 24-hour response commitment on weekdays so timezone gaps never stall a build.
Project communication, source control, and handover documentation stay in English, so distributed stakeholders can follow progress without friction.
Workflow automation only pays back when it is scoped to one ROI-bearing workflow at a time. Generic "we can automate anything" projects produce thin, brittle pipelines. The use cases below are the patterns where our buyers hit clean payback inside the first two quarters.
Most finance teams spend the first six business days of the month chasing journals, reconciling intercompany positions, and chasing missing receipts. We automate the pre-flight checks: subledger-to-GL reconciliations, intercompany match, expense report coding completeness, and revenue recognition flags. The controller starts day one with a clean exception list instead of a 200-row spreadsheet of unknowns.
Every onboarding touches CRM, billing, the product, and a checklist that lives in three different tools. We replace the manual hand-off chain with an event-driven workflow: a closed-won opportunity in CRM triggers provisioning, billing setup, welcome sequence, success-plan creation, and milestone tracking — and the same engine drives the renewal motion 60 days before contract end with a context-aware health summary surfaced to the success manager.
JML workflows fail in the messy middle. We automate the full lifecycle: a new hire in the HRIS triggers identity, email, equipment, software access, manager handshake, and 30/60/90 check-in scheduling. Movers get differential access changes. Leavers get same-day deprovisioning with audit logs that survive a SOC 2 audit. The IT team stops being a ticket queue.
A modern content team owns a long tail of formats: blog posts, social cuts, email, paid ads, podcast notes, video chapters. We automate the mechanical layer: source-to-cuts, asset versioning, channel-specific formatting, scheduling across platforms, performance reporting back to the source. Marketers spend their hours on positioning and creative review, not on copy-pasting.
Quote-to-cash works fine on the happy path; it breaks on the exceptions. We automate exception triage: missing PO numbers, mid-term contract changes, multi-entity billing, deferred revenue scheduling, and dunning escalations. Each exception lands as a structured task with the recommended action pre-filled, so RevOps clears the queue in hours rather than days.
Every new vendor or contract triggers the same intake checks: data privacy review, security questionnaire status, redline tracking, and approval routing. We automate the intake gate: documents are parsed, risk flags are surfaced, the right reviewer is routed in, and the decision is logged with full audit trail. Legal and security teams stop being the bottleneck for net-new revenue.
Workflow automation engagements move through five phases. Phase one is the most important and the most often skipped: if you do not understand the workflow as it actually runs today, the automation will codify the wrong behavior and create more work than it removes.
We sit with the operators who run the workflow today and document what actually happens, not what the playbook says happens. The output is a numbered process map with measured cycle time per step, ownership per step, exception frequency per step, and the three highest-leverage points for automation. This is the only phase the operators must be present for; the rest is on us.
For every system the workflow touches, we agree the data contract: which fields the automation reads, which it writes, who owns the source of truth, and what happens on conflict. Skipping this step is the #1 cause of automation projects that need a six-month rebuild a year later.
We build the automation in your stack — n8n, Temporal, Workato, or custom code, picked per workflow. Every step is instrumented with structured logs, latency, and a deterministic retry policy. Observability is not an afterthought; you cannot operate an automation you cannot see.
The automation runs in shadow mode against real workflow traffic — it produces the action it would take but does not commit it. The operator compares shadow output to the action they would take, and we tune until the agreement rate clears the production bar (typically 95% to 99% depending on the cost of an error).
Cutover happens behind a feature flag with a one-click rollback. We stay attached for the first month of production to triage real-world edge cases. From month two onward you own the automation; we offer a monthly operate-with-you tier for buyers who want us to keep tuning as the underlying systems and process evolve.
On a typical 10-week first-workflow engagement, here is what you actually receive at each checkpoint.
A numbered process map of the workflow as it runs today, with measured cycle time per step, exception frequency, and the dollar value of the time the automation will return.
A signed-off data contract per integrated system — which fields the automation reads, which it writes, source of truth, and conflict resolution rules.
The automation runs in your staging environment against synthetic workflow traffic with full observability. Every step has structured logs and a deterministic retry policy.
The automation runs against real production workflow traffic in shadow mode — proposing actions but not committing them — so the operator can rate agreement before any live action is taken.
The automation is live behind a feature flag with one-click rollback, on-call runbook, alerting tuned to the workflow SLO, and a written incident response procedure.
Your team owns day-to-day operation. We hand over the runbook, observability dashboards, and the eval harness, and stay available for monthly tuning and platform upgrades on the operate-with-you tier.
The questions buyers ask before they commit budget. We are happy to go deeper on any of these on a discovery call.
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