Coming soon
Five real traps in AI-native development — we hit them so you don't
Tool choice, context management, review trust, metrics — lessons from real projects.
AI-native engineering workflows · Shanghai
Make your engineering team
Spec
intake · spec
Ship
ship
Make your engineering team
truly AI-native
There are plenty of AI coding tools — what's missing is someone to wire them into your team. Zhida connects AI into every stage: spec, code, review, test, ship. You're not buying a tool; you're rebuilding the workflow.
First conversation is free — we look at your reality before we talk scope
AI-native pipeline · agents across the whole delivery chain
AI
Code
coding agent
AI
Review
review
AI
Test
test gen
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 —
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 · Zhida Flow
Zhida Flow · 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.
# 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.
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."
Scan · Diagnose
¥30k 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
Most popular
Build · Implement
¥200k from / project
For: teams committed to change who want an expert alongside
- Everything in Scan
- Pilot-squad workflow build
- Standards / templates / prompt library
- Hands-on team training
- Results dashboard
Run · Retainer
¥20k from / month
For: teams live and wanting to stay ahead
- Monthly optimization retainer
- Toolchain update tracking
- Templates & workflow subscription
- Priority support
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.
Insights
Methodology from the trenches
We turn the traps we've hit and the paths we've proven in real projects into actionable methodology — no industry surveys, only what we've verified first-hand.
Coming soon
Wiring a coding agent into a real codebase, end to end
Standards, architecture conventions, historical context — code that reads like your team wrote it.
Coming soon
Why 95% of enterprise AI pilots never scale
Tools bought, process unchanged. The real gap is workflow, standards, and measurement.
First articles are in the works · want them first? Leave your email when you contact us
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
Send us an email or add us on WeCom — we'll set up a free intro call to understand your engineering reality and gauge how much headroom an AI-native workflow could unlock.
