BuiltAI · governance
Governed AI workflows, not uncontrolled experimentation.
BuiltAI helps construction, FM and M&E teams use AI safely across tenders, RAMS, commercial control, reporting and service desk workflows.
Why governance matters
AI is already being used. The question is whether it is controlled.
Many teams are already using AI informally to draft emails, write scopes, produce RAMS, summarise contracts, prepare tenders or review documents.
That can create value quickly, but it can also create risk when there is no clear operating model.
BuiltAI is designed to turn informal AI use into a governed operating capability.
- Unsupported claims in client-facing documents
- Inconsistent outputs between users
- Missing source references
- Unclear approval responsibility
- Sensitive data being entered into the wrong tools
- No audit trail showing who reviewed or approved the output
- Over-reliance on AI-generated content
- Uncertainty around procurement disclosure or client expectations
Five non-negotiables
Practical controls built into each workflow.
BuiltAI governance is designed for real operational environments, not theoretical policy documents.
Human oversight before issue
AI can assist, but competent people still review, approve and take responsibility for high-risk outputs.
Source-led delivery
Critical outputs are tied back to the source documents, evidence, assumptions and client instructions used to create them. Tender, RAMS, variation and reporting work links to its source.
Approval gates before issue
Client-facing, safety-critical, commercial or contract-sensitive outputs are not issued without defined review and sign-off.
R/A/G data classification
Information is handled according to sensitivity, with clear rules on what can and cannot be used in different tools.
Professional boundaries
Missing, unclear or assumed information is identified, logged and reviewed, never silently filled, and teams can always explain where AI was used, how outputs were reviewed and what controls applied.
Data classification
Red, Amber and Green data handling.
BuiltAI can help clients apply a practical data classification model to AI workflows. The labels are explicit so colour is never the only signal.
Green
Low-risk or generic information.
Examples
- Public website copy
- Generic templates
- Non-sensitive boilerplate
- Training examples
- Internal process wording
- Standard operating headings
Typical approach
Green data can usually be used in lower-risk AI workflows, subject to internal policy.
Amber
Business or project information that needs controlled handling.
Examples
- Tender documents
- Project scopes
- Commercial assumptions
- RAMS drafts
- Operational reports
- Client-specific workflows
- Non-public but non-highly-sensitive information
Typical approach
Amber data should be handled carefully, minimised where possible and used only within approved workflows and tools.
Red
Sensitive, restricted or high-risk information.
Examples
- Personal data
- Security-sensitive site information
- Confidential client commercial data
- Restricted contract information
- Highly sensitive pricing or margin data
- Data covered by specific NDA or client restrictions
Typical approach
Red data should not be entered into public AI tools without explicit approval, defined controls and appropriate data-processing arrangements.
Try It: Classify a Data Example
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Indicative only. A transparent keyword heuristic to show the rail in action, not a DLP engine or legal advice. Your own policy governs the final call.
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Approval gates
Different outputs need different levels of control.
BuiltAI workflows can include approval gates based on the type of output and the risk involved.
Tender and bid outputsBidroom in a Box®
Typical review by
- Bid Lead
- Estimator or QS
- Commercial Reviewer
- Operations Reviewer where required
Controls may include
- Requirements traceability
- Assumptions and exclusions review
- RFI review
- Pricing validation
- Pre-submission QA
RAMS and compliance outputsRAMS Factory®
Typical review by
- Competent H&S reviewer
- Project Manager
- Operations lead
- Site supervisor where relevant
Controls may include
- Task-based hazard review
- COSHH review
- Permit and isolation checks
- Evidence checklist
- Operative briefing requirement
Commercial and contract outputsCommercial Control Kit™
Typical review by
- Commercial Manager
- QS
- Contract Manager
- Director or legal adviser where required
Controls may include
- Notice review
- Evidence bundle check
- Valuation review
- Cause / effect / entitlement review
- Contract-specific escalation
Service desk and FM triage outputsThe Dispatch Desk™
Typical review by
- Service Desk Manager
- Duty Manager
- Operations lead
- Account Manager
Controls may include
- Approved scripts
- Safety screening
- Human takeover triggers
- Priority mapping
- QA sampling
- Escalation logs
Margin and reporting outputsMargin Cockpit™
Typical review by
- Finance lead
- Commercial lead
- Operations lead
- Senior leadership
Controls may include
- Finance data validation
- WIP owner review
- Invoice blocker checks
- Action log review
- Board or QBR approval
Operating boundaries
Governance is embedded into the way the workflow operates.
The aim is to make AI faster and more useful without making it uncontrolled.
Workflow controls
Depending on the workflow, controls can include:
- Human approval gates
- Source traceability
- Assumptions registers
- Evidence indexes
- QA checklists
- Risk registers
- Version control
- Audit logs
- Data classification
- Escalation rules
- Controlled templates
- Approved scripts
- Output disclaimers
- Clear usage boundaries
Clear boundaries prevent misuse.
AI can help with
- Structuring information
- Drafting first-pass content
- Summarising documents
- Extracting requirements
- Identifying missing information
- Creating registers
- Building checklists
- Categorising blockers
- Preparing review-ready outputs
AI should not
- Invent missing scope
- Assert compliance without evidence
- Replace competent H&S review
- Replace QS or estimator judgement
- Provide legal advice
- Make autonomous safety-critical decisions
- Override emergency procedures
- Issue client-facing outputs without approval
- Process sensitive data outside approved controls
This distinction should be clear across every BuiltAI workflow.
Built for clients who need AI to be defensible.
Procurement-safe adoption
Construction, FM and M&E businesses often work in environments where procurement, client assurance, data handling and contract compliance matter.
If a client asks how AI is being used, governed and reviewed, your team should be able to answer clearly and confidently.
- AI usage policies
- Data classification rules
- Approval matrices
- Output review processes
- Audit trail discipline
- Procurement disclosure wording
- Role-based usage guidance
- Client-facing governance summaries
Across the workflow suite
One governance layer across every workflow.
Each pack carries the same governance discipline, applied to its own output type. The same controls persist across every engagement route, Operational Intelligence Audit™, Focused Workflow Pilot and Built to Order™, so a workflow does not lose its discipline once it is live.
BuiltAI helps produce structured, detailed and review-ready outputs. Final review, validation, approval and reliance remain with the client's competent people.
Bidroom in a Box®
Governance focus
- Source traceability
- Requirements mapping
- Assumptions and exclusions
- RFI discipline
- Commercial review
- Pre-submission QA
RAMS Factory®
Governance focus
- Task-based controls
- Competent review
- Permit checks
- COSHH linkage
- Evidence packs
- Approval gates
Commercial Control Kit™
Governance focus
- Notice tracking
- Evidence bundles
- Cause / effect narratives
- Valuation review
- Contract-sensitive approval
Margin Cockpit™
Governance focus
- Finance validation
- Data source checks
- WIP ownership
- Action logs
- Board / QBR review
The Dispatch Desk™
Governance focus
- Approved scripts
- Safety screening
- Escalation triggers
- Human takeover
- QA sampling
- Audit logs
