Typical operating constraints
- AI pilots remain isolated
- Knowledge sources and permissions are uncontrolled
- Demos replace business outcome evidence
YaoVector / Solution
Enterprise Intelligence & Agent Systems
Build governed knowledge, data, model, tool and evaluation layers so business agents can enter critical workflows safely.
01 / Constraint
The constraint is an operating break, not a missing tool
Cost of leaving it unresolved
- Duplicated platforms and model lock-in
- Data and compliance exposure
- Agents fail in production
02 / Outcomes
Outcomes and fit
Designed for growth-stage businesses with a clear outcome, accountable owner and a workflow available for validation.
- 01Create reusable intelligence capabilities
- 02Establish access, audit and human review
- 03Continuously evaluate quality, cost and value
03 / Review before commitment
Review real outputs before expanding commitment
These are method and deliverable descriptions, not unverified customer outcome claims. No sensitive data is needed for the first conversation.
Constraint and opportunity map
Separate verified facts from hypotheses, and clarify problems, owners and priorities.
Metric and evidence register
Baselines, formulas, sources, windows and evidence status are recorded.
Validation decision memo
Continue, adjust or stop thresholds prevent an open-ended pilot.
Start with one concrete constraintReply in 1–2 business days · Start with non-sensitive context
04 / Architecture
From shared facts to sustained operation
Operating knowledge and data layer
Establish shared facts and explicit accountability.
Model, tool and identity governance
Connect critical work into an observable operating flow.
Agent operations and evaluation
Improve operations through measures, exceptions and review.
05 / Modules
Included product modules
Operating knowledge and data layer
Create permission-aware operating context with provenance, versions and quality state.
Model, tool and identity governance
Govern model choice, tool access, secrets, data use and human review by task risk.
Agent operations and evaluation
Record task traces and continuously evaluate quality, latency, cost, safety and adoption.
06 / Six-loop delivery
Six-loop implementation
Each loop has explicit outputs, owners and criteria for moving forward.
- 01
Identify the constraint
Confirm the intended outcome, users, current flow and highest-value constraint.
- 02
Establish the baseline
Define measures, sources, current state and what remains unverified.
- 03
Design the target system
Specify architecture, workflow, accountability, controls and validation plan.
- 04
Validate in scope
Test usability, risk and real adoption inside a controlled business boundary.
- 05
Enter daily operation
Complete integrations, role enablement, exception handling and operating handover.
- 06
Measure and evolve
Review outcomes, flow efficiency and quality before expanding or adjusting.
07 / Measurement
Baseline first, value second
These are dimensions to baseline, not unverified promises of uplift.
Business outcomes
Measure the intended operating outcome against an agreed baseline without presupposing uplift.
Workflow efficiency
Measure end-to-end time, waits, rework, response and exception recovery.
Adoption and quality
Measure real use, first-pass acceptance, review burden, provenance and reliability.
08 / Evidence
Transparent evidence status
No unauthorised client case or outcome figure is published here. Evidence is shown only after disclosure, baseline, period, definition and source review.
Before committing
Questions and suitability boundaries
Use existing records to validate the problem, then agree participants and pilot scope. This is diagnostic guidance, not an outcome promise.
AI pilots do not become operating outcomes
How can we check whether this is our problem?
Define where input comes from, who uses output, which errors must be blocked and how a person takes over.
Explore the full diagnostic and validation path ↗Who should participate, and what should we prepare?
The business owner accepts results, the technical owner manages runtime and access, and a human reviewer retains takeover authority.
- Approved, redacted task samples and examples of correct handling.
- Current workflows, task time, errors and escalation records.
- Data permissions, retention requirements and actions that must not be automated.
Share only non-sensitive context at first contact. Detailed records require authorisation and redaction.
When should we not start implementation yet?
Do not automate execution without lawful data use, checkable outcomes and an accountable human owner. This path does not promise unattended replacement.
Expand only when both business and risk thresholds pass. Stop automated handling and fall back after a critical safety failure.
09 / Engagement
Start with focused validation
Designed for growth-stage businesses with a clear outcome, accountable owner and a workflow available for validation.
- Timing
- Timing is set after diagnosis and baseline confirmation
- Model
- Focused validation, expandable into forward-deployed co-creation