AI-native engineering workflows · Shanghai

Make your engineering team
truly AI-native

Everyone talks about "coding with AI," but no one tells you how to make it stick across a real team. Zhida wires AI into every stage — spec, code, review, test, ship. You're not buying a tool; you're rebuilding the workflow.

Book a free diagnosis → See how it works

// First conversation is free — we look at your reality before we talk scope

AI-native pipeline · agents across the whole delivery chain
Spec intake · spec
AI
Code coding agent
AI
Review review
AI
Test test gen
Ship ship
Works with the stack you already run
Claude / GPT / DeepSeek GitHub · GitLab Feishu · DingTalk · WeCom Volcengine · Alibaba Cloud On-prem / data stays in-house
Why now

AI coding isn't short on tools —
it's short on someone to wire it in

Coding agents broke out in 2026, yet most teams are stuck at "a few people use Copilot on their own." The tools got bought, the workflow never changed, and the gains never showed. The gap was never the model — it's the workflow, the standards, and the measurement.

95% of enterprise AI pilots never scale — most die because they were never wired into a real workflow (MIT, 2025)
23% only about a fifth of companies run agents at scale in even one function — the window is still open
120% CAGR of China's enterprise AI-agent market, 2023–2027 — now is the time to move
Flagship

AI-native engineering workflow

Not a plugin — a redesign of your whole delivery chain around humans and agents working together. We come in, diagnose, and rebuild alongside you until the team is actually running it and the metrics actually move.

01

From request to spec, an agent goes first

Vague requests get broken into executable specs and task cards, cutting back-and-forth so every task is clear before coding starts.

02

Coding agents that live in your real repo

Tuned to your standards, architecture, and history — so the AI writes code in your team's style, not generic boilerplate.

03

Review and tests as an automatic safety net

First-pass PR review and generated test cases free your people for the judgment calls that actually need a human — without cutting quality.

04

Measurable results, no hand-waving

Cycle time, rework rate, review hours — a metrics dashboard from day one proves exactly what the workflow saves.

zhida.workflow.yaml
# what happens automatically when a request lands on: issue.created pipeline: - spec: agent break down request → task cards - code: agent implement to team standards - review: agent static checks + arch alignment - test: agent generate cases → run green - ship: human sign-off → merge → deploy metrics: # every step logged & measurable cycle_time: "-38%" rework_rate: "-45%" review_hours: "-52%" # ↑ illustrative — measured against your baseline
Four steps · light to heavy

See clearly first, then act — you control the risk

We don't sell an all-at-once fantasy. Start with a low-cost diagnosis, move forward only once the value is visible — and you can stop at any step.

Diagnose

2–3 days to map your engineering reality and deliver a roadmap with an efficiency estimate. No value in sight? No need to continue.

Pilot

Pick one squad for a 4–6 week model rollout, get the workflow running, and capture the first real numbers.

Scale

Replicate the proven workflow to more teams, with standards, templates, and training rolled out alongside.

Retainer

Ongoing tuning and measurement, month to month. Tools keep changing; we keep your workflow ahead.

Capabilities

Built around the engineering workflow, extensible on demand

The engineering workflow is our home turf. Once your team is running smoothly, these two enterprise agents bolt on seamlessly — same team, same methodology.

Engineering workflow agent · core

The full AI-native chain: spec, code, review, test, ship. Our deepest expertise, and where every engagement begins.

Extension

Data analytics agent

Ask questions in plain language, get charts and root-cause analysis. We don't build another BI — we make it fit your real metrics and data.

Extension

Knowledge-base agent

Enterprise Q&A and document assistants. The value isn't "wire up a RAG" — it's deep integration with permissions, compliance, and process.

Packages

From one diagnosis to a long-term retainer

Pricing scales with scope; figures below are starting references. Shanghai companies may lower their real spend substantially via subsidies such as the "model voucher."

Diagnose
¥15k from / engagement

For: teams that want clarity and a feasibility read first

  • 2–3 day engineering health check
  • Codebase and process review
  • Roadmap + efficiency estimate
  • Ready-to-approve rollout plan
Book a diagnosis
Most popular
Implement
¥150k from / project

For: teams committed to change who want an expert alongside

  • Everything in Diagnose
  • Pilot-squad workflow build
  • Standards / templates / prompt library
  • Hands-on team training
  • Results dashboard
Discuss my project →
Retainer
¥5k from / month

For: teams live and wanting to stay ahead

  • Monthly optimization retainer
  • Toolchain update tracking
  • Templates & workflow subscription
  • Priority support
Explore retainer

// Every quote includes a scope assessment — start with a diagnosis to confirm the value

For Shanghai companies

Use public subsidies to halve your outlay

Shanghai's "Moliang Shanghai" program subsidizes enterprise purchases of vertical LLM applications. For qualifying projects, your real cost can drop substantially — and we help you organize and prepare the application.

  • Model voucher: up to 50% of contract value, capped at ¥5M, for third-party vertical LLM applications
  • Compute / corpus vouchers: extra subsidies on compute and data, thinning the cost further
  • Application support: we know the windows and criteria and help get your materials in order
50% max subsidy

* Subsidies are subject to the latest official rules of the Shanghai Municipal Commission of Economy and Informatization and actual review. This site makes no guarantee of any subsidy being granted.

FAQ

You might be wondering

The tighter your engineering headcount, the higher the marginal return on efficiency. Start with a diagnosis and let real data decide — if we can't see the value, we won't push you to continue.

Tools solve "an individual can use it." We solve "the team actually runs it." The hard part is workflow redesign, team standards, context tuning, and measurement — none of which a tool license gives you. It takes someone embedding it into your repo and process.

We support on-prem deployment and data-stays-in-house setups, and integrate with your existing compliance requirements (audit logging, access isolation, etc.). The exact design is set during diagnosis, against your security baseline.

The engineering workflow is our core and our starting point. Data analytics and knowledge-base agents are extensions, usually added after the workflow is live and trust is built — delivered by the same team with the same methodology.

Diagnosis delivers a roadmap in 2–3 days; a pilot typically produces the first real efficiency numbers (cycle time, rework rate) in 4–6 weeks. We believe in showing data, not promising overnight miracles.

We're familiar with the windows and criteria of programs like "Moliang Shanghai" and can help organize and prepare application materials. Approval rests with the authorities; we make no guarantee of funds being granted.

Give us 30 minutes to see if it's worth doing

Leave your details and we'll set up a free intro call to understand your engineering reality and gauge how much headroom an AI-native workflow could unlock.

Email us → Add us on WeChat

// Contact: hello@example.com · WeChat Zhida (placeholder — replace before launch)