01
Multi-source intake
Turn scattered information into one traceable operating record.
Typical situation: Documents, spreadsheets, and business web pages need to be brought together with their sources preserved.
Explore scenarios and projectsEnterprise AI Systems Design & Delivery
AI systems that solve real business problems.
Turn manually connected, error-prone business workflows into AI automation systems designed around how the company actually operates.
01
Business input
02
AI processing
03
Human review
04
Delivery
State · exception · recovery · review · evidence
Brand facts · Direct answer
What is SolveReal Systems?
SolveReal Systems, also known in Chinese as 实解智能, is led by fengc and provides enterprise AI system design and delivery. Work starts from a real workflow that consumes staff time, fails easily, or is difficult to sustain—not from a fixed industry template or a generic feature list.
About SolveReal Systems- Who it serves
- Owners and operations leaders at small and medium businesses with repetitive manual workflows that may benefit from AI or automation.
- How work starts
- Start with workflow diagnosis, identify automation opportunities, human-review boundaries, failure recovery, and deliverable outcomes, then build a working custom system.
- Delivery principle
- The system handles stable steps; people retain key decisions and high-risk actions, with state, exceptions, recovery, and delivery evidence preserved.
01 / Capabilities
Four system directions, four enterprise capabilities
02
Repeated decisions and entry
Give stable rules to the system while people retain exceptions and high-risk decisions.
Typical situation: Similar information is classified and checked every day, then entered into another system.
Explore scenarios and projects03
Handoffs between systems
Carry state across files and business systems with fewer manual transfers and gaps.
Typical situation: Files, tasks, and results repeatedly move between different tools.
Explore scenarios and projects04
Recovery and human review
Record exceptions and recovery points so the system can keep operating.
Typical situation: When work stops, the team needs to know where it stopped, who takes over, and how to continue.
Explore scenarios and projects02 / Project evidence
See where work was draining time—and what the system took over.
Start with the business problem, human boundary, and verifiable deliverables—then open the full evidence.
SELECTED PROJECT
Deployable systemFreight Lead Agent
Google Maps prospecting and personalized outreach
Before
Sales teams must find target companies on Google Maps, verify each official website, understand the business, locate public contacts, and prepare a different message for every company, which is difficult to scale manually
System takeover
The system collects Google Maps companies by keyword and region, extracts business context and public contacts from each website, generates a personalized message, and sends it to sales for review, delivery, and follow-up
Human control
Review the target company, website context, public contacts, and personalized message for accuracy
Recovery and boundary
Invalid records are preserved with explicit reasons instead of being deleted.
Key limitation
Processes public business information only; it does not crawl authenticated pages or personal profiles.
03 / Delivery method
How AI becomes a working system
Diagnose the current workflow
Map real inputs, processing steps, owners, exceptions, and deliverable outcomes.
Design the human boundary
Define what AI may handle, who reviews critical decisions, and which actions require confirmation.
Validate the real workflow
Validate inputs, exceptions, recovery, review, and outputs with real samples rather than an idealized demo path.
Deliver and improve
Deliver a working system, operating boundaries, and traceable evidence, then improve it from real use.
Have a repetitive, fragile workflow?
Start from the real business problem, not a feature list.