Connect AI to governed data and clearly bounded actions.
Diginno designs AI-assisted workflows that retrieve approved business context, call the required tools and keep high-impact decisions reviewable by people.
AI is a probabilistic component inside an operating system. The implementation must define evidence, access, approval, evaluation and fallback—not only the prompt.
Common failure patterns
> Why an AI demo can fail in real operations
01
The assistant cannot access current business context
A generic chatbot can answer broad questions, but it cannot reliably use the records, permissions and definitions your operation depends on.
02
Answers cannot be traced to a source
Users receive a confident response without knowing which document, record or query produced it.
03
AI actions have no approval boundary
Drafting, lookup and high-impact actions are treated the same, creating avoidable operational and security risk.
04
The demo has no operating model
There is no owner for prompts, access, evaluation, failures, model changes or user feedback after launch.
Implementation workstreams
> Ground the assistant before giving it tools
The scope follows the defined job, data boundary, consequence of error and required human oversight.
Knowledge and retrieval
Give assistants access to approved documents and business context with source references and access boundaries.
- Source and permission inventory
- Content ingestion and update rules
- Retrieval and citation behavior
- Unknown-answer and fallback policy
Structured data access
Allow bounded questions over databases, LarkBase or operational systems without exposing unrestricted query capability.
- Approved question and metric definitions
- Read-only data tools where appropriate
- Query validation and result limits
- Audit context for data access
Tool use and workflow actions
Connect AI output to operational tools only through explicit schemas, permissions and human approval where impact requires it.
- Tool and action contract
- Input validation
- Approval and confirmation steps
- Idempotency and recovery behavior
Evaluation and AI operations
Define how quality, safety, cost and failures are reviewed as prompts, data and models change.
- Evaluation examples and acceptance criteria
- Feedback and incident workflow
- Usage and failure monitoring
- Owner runbook and change process
Delivery process
> Evaluate the job before expanding capability
01
Define the job
Identify the user, decision or task, approved context and consequence of a wrong response.
02
Map data and access
Confirm sources, freshness, ownership, permissions and sensitive fields before connecting AI.
03
Prototype safely
Build a bounded read-only or draft-first workflow with realistic examples and visible sources.
04
Evaluate
Test expected, ambiguous, unsupported and adversarial inputs against agreed criteria.
05
Integrate
Add approved tools, confirmations, monitoring and recovery behavior for the production scope.
06
Operate
Review feedback, failures, access and model changes through an accountable improvement process.
Guardrails
> Match controls to the consequence of being wrong
Low-impact drafting and high-impact system actions should not share the same approval or access model.
Least-privilege access
The assistant receives only the data and actions required for its defined job.
Source visibility
Answers that rely on business context expose the relevant source or state that evidence is unavailable.
Human approval
High-impact actions remain drafts or require confirmation before execution.
Structured tool inputs
Actions use validated schemas rather than passing untrusted free text directly to downstream systems.
Evaluation set
Representative examples are retained to detect quality regressions when prompts, data or models change.
Failure and fallback
The workflow can refuse, escalate or fall back safely instead of inventing an answer or action.
Engagement modes
> Start with a bounded job and realistic examples
Model, hosting, timeline and pricing decisions follow use-case, data, risk and operating requirements.
Mode 01
AI readiness and use-case design
For teams with many AI ideas but no agreed priority, data boundary or success criteria.
- Use-case and risk assessment
- Data and access inventory
- Human-in-the-loop design
- Prioritized prototype scope
Mode 02
Bounded AI assistant
For one defined user group and job using approved knowledge or read-only operational data.
- One assistant workflow
- Approved retrieval or data tools
- Evaluation examples
- Monitoring, runbook and handover
Mode 03
AI-enabled operational workflow
For an established assistant that needs approved actions, cross-system orchestration and stronger operating controls.
- Multiple tools or systems
- Approval and action controls
- Shared evaluation and monitoring
- Governance and improvement roadmap
Handover
> Know what the assistant may access, answer and do
The handover includes the operating boundary and evaluation context, not only prompts or model configuration.
Frequently asked questions
> Questions before connecting AI to operations
>How is this different from adding a chatbot to our website?
The work begins with a defined operational job, approved business context, access rules and evaluation criteria. A chat interface may be included, but it is not the system design by itself.
>Which systems can an AI assistant connect to?
Feasibility depends on the system API, authentication, data contract, permissions and reliability requirements. We confirm each connection during discovery instead of promising an unlimited connector catalogue.
>Can the assistant write back to our systems?
Yes, when an action is clearly defined and authorized. High-impact or ambiguous actions should use confirmation, human approval, strict input validation and idempotency controls.
>How do you reduce hallucinations?
No design can guarantee that a generative model never makes a mistake. We reduce risk through bounded tasks, approved sources, citations, structured tools, explicit refusal behavior, evaluation and human review.
>Can we use our preferred AI model or hosting environment?
Model and hosting choices are evaluated against the use case, data policy, latency, quality, cost and operating requirements. The architecture should avoid unnecessary lock-in where practical.
>Who maintains the assistant after launch?
Your team receives the agreed configuration, evaluation examples and operating runbook. We define business and technical owners and can separately scope ongoing monitoring or improvement support.
Start with one AI job
Define the user, context and consequence of being wrong.
Share the task, approved data sources, current workflow, required action and where human review must remain.