EasyZ · Research-Driven AI Engineering

Hard Problems, Researched Solutions

Complex AI Implementation · A Research-Driven Team from Top Universities

We do one thing

Bring AI into real business. Non-standard scenarios, complex data, uncertain technical paths — that's exactly what we're built for.

The Problems

You Come to Us Because of Problems Like These

01

Your business processes don't fit any standard SaaSOff-the-shelf SaaS is designed for generic scenarios. Your business has unique approval flows, data logic, and compliance requirements — forcing standard products onto them is like fitting square pegs in round holes.

02

Your data is too complex for generic modelsProduct manuals, technical docs, internal knowledge bases... messy formats, domain-specific jargon. Plugging them straight into an LLM gives terrible results — it requires dedicated knowledge engineering.

03

You want to use AI but don't know where or if it'll workYou've heard plenty of AI success stories, but back in your own business, you can't tell where Agents make sense, where RAG fits, and where AI shouldn't be used at all.

04

You've tried other solutions but results fell shortYou've attempted solutions before, but accuracy didn't improve, costs didn't drop, and maintenance became a nightmare — you need a team that can rethink from first principles.

Our Approach

How We Work

01

Understand the Problem

We don't take requirement documents — we work with you to define the problem boundary

We don't start coding on day one. First we take the problem apart with you: what actually needs solving, where the boundaries are, what counts as success. Most projects fail not because the tech is hard, but because no one nailed down what was wanted. We'd rather be slow here and stay aligned.

02

Research Solutions

Evaluate technical approaches, run small-scale validations — no guessing

Once the direction is set, we evaluate several technical paths and validate them on small-scale data instead of betting on a guess. Where an Agent fits, where RAG fits, where AI shouldn't be used at all — decided by experiment data, surfacing risk early.

03

Engineer & Deliver

Deliver running systems, not PowerPoints

Research findings have to become a system that runs inside your business. We do real engineering: deployable, maintainable, logged, with fallback mechanisms. What we deliver ships — not a demo deck or a flaky prototype.

04

Iterate Continuously

Going live is when the real test begins — we stick around to tune it

The real test starts once the system is live. Real data exposes problems you couldn't foresee, and quality needs ongoing tuning. We don't ship and vanish — we stay to watch the data, adjust strategy, and iterate until it hits the bar.

Case Studies

Selected Cases

All cases are anonymized. Data from real deliveries.

Case 01Tech / Developer Tools

Programming Language Ecosystem Migration

For emerging languages with scarce training data, used RAG + Agent to automate cross-language library migration, breaking through the manual translation bottleneck.

See Research Story
80%+
Migration Automation Rate

·Single-library migration time reduced by 80%+, from weeks to hours

·Compilation pass rate 85%+, auto-corrected after multi-round self-checks

·Full language documentation indexed, version updates auto-synced

RAG PipelineAST ParsingCompiler IntegrationSelf-Check AgentMulti-Round Iteration
Case 02FinTech

Securities Mid-Office AI Transformation

Covered three core scenarios: intelligent authorization, intelligent tweets, and intelligent integration — all non-intrusively layered on existing systems.

See the Transformation
70%+
Permission Cycle Reduction

·Financial product tweet production: from 1-2 hours to under 10 minutes

·Business query resolution: from avg. 30 minutes to under 1 minute

·Three scenarios delivered modularly, can go live independently

Multi-source RAGLLM Content GenPermission Rule EnginePrivate Deployment
Case 03Manufacturing

Laser Equipment Enterprise Smart Customer Service

Connected 30+ product documents, 24/7 auto-response for procurement and troubleshooting queries.

75%+Auto-Resolution Rate
RAGPrivate LLMTicket System IntegrationMulti-Turn Agent
Case 04Cross-border E-commerce

Overseas Customer Acquisition Automation

End-to-end coverage: target customer identification, personalized outreach, automated touch and lead follow-up.

3x+Efficiency Improvement
OpenClawMultilingual LLMAutomation WorkflowCRM Integration
Live Demo

Click Around and See How It Works

Four interactive product demos, each simplified from a real case. Front-end scripted simulation showing the shape of the capability — not backed by a live model.

Source (Python)
python
1def quick_sort(arr):
2 if len(arr) <= 1:
3 return arr
4 pivot = arr[len(arr) // 2]
5 left = [x for x in arr if x < pivot]
6 mid = [x for x in arr if x == pivot]
7 right = [x for x in arr if x > pivot]
8 return quick_sort(left) + mid + quick_sort(right)
Target (Cangjie)
cangjie
Click "Start Migration" to watch the source rewrite into the target language…
Parse AST
RAG Retrieval
Generate Target
Compile Self-Check
Part A

Who We Are

A research-driven team from top universities. Core members have deep academic research backgrounds and engineering delivery capabilities. Flat collaboration — everyone faces the problem directly. No layers, no information loss.

Part B

Where Big Teams Get Stuck

PM relays requirements, engineers interpret them second-hand
Business, research, and engineering are the same people — direct access
Changing requirements means restarting the approval process
Decisions made face-to-face, adjustments same day
Decisions wait for weekly meetings, reports, sign-offs
No one to report to — decisions are instant
Knowledge lives in PMs' heads, engineers just execute
Everyone solves the problem, everyone understands the context
Part C

Our Commitment

To ensure every project gets enough research depth and engineering rigor, we actively limit how much we take on:

Concurrent projects ≤ 3
Annual projects ≤ 8
Capability assessment before accepting — we don't take what we can't deliver

Let's Talk About Your Hard Problem

Tell us your problem. Let's figure it out.

Or email us at: 18781630574@163.com

Partners

Partner Organizations

Organizations exploring AI implementation with us.