Outcomes
- Prompt contracts
- Retrieval pattern
- Reviewer UI spec
- Approval checkpoints
- Handoff model
Service package
Human-Reviewed AI Workflow Design for Regulated and High-Impact Work
A six-to-eight-week pilot for one bounded workflow where AI can draft or classify work, but evidence, reviewers, blocked actions, and escalation must remain visible.
Review-first pilot
Starting from $55k.
Buyer fit: Teams with one internal workflow where AI output could help but cannot be trusted blindly.
Timeline: Typical duration: 6–8 weeks.
Scope boundary: Not a fully autonomous system; high-impact outputs stay human-reviewed.
Sample artifact: Working pilot or technical prototype path with reviewer-visible evidence.
Deliverables
- workflow design
- output schema
- review-state model
- pilot implementation plan
Sample artifact template
Human-Reviewed AI Workflow Spec
A pilot specification for AI output that is drafted, reviewed, accepted, rejected, or blocked before downstream use.
Prompt contract
- allowed sources
- output schema
- non-goals
- review expectations
Review queue
- draft
- needs source check
- approved
- blocked
Fallback rule
- uncertain
- source conflict
- regulated data
- human escalation
Questions answered
What this package resolves before implementation
Use the one-pager and sample artifact to decide whether this scope fits your current risk.
What is AI allowed to draft?
Who approves output?
What states should the workflow expose?
Which actions must be blocked by default?
Service detail
Who this package is for, what it covers, and how acceptance is reviewed
The page separates buyer fit, technical scope, integration, governance, client responsibilities, and proof so a technical evaluator can assess the package without relying on generic claims.
Who this is for
- Teams with a specific internal workflow and identifiable reviewers.
- Organizations that need AI assistance without autonomous production authority.
- Departments that need traceable drafts, approvals, and blocked states.
Who this is not for
- Autonomous agents with unrestricted production access.
- A generic chatbot without a defined workflow, owner, or evidence source.
- Replacing human accountability for high-impact decisions.
Systems and workflows in scope
- Documentation review
- Claims or case-file summarization
- Triage and classification
- Migration-note generation
- Policy or SOP review
- Analyst assistance with source visibility
Problems this package answers
- What can AI draft safely?
- Which sources must support each output?
- Who may approve, reject, or escalate?
- What happens when evidence is missing?
- Which actions are always blocked?
Technical approach
Implementation depth without unsupported guarantees
The exact architecture depends on the system, evidence, access, and risk. These sections show the normal design surface and the boundaries buyers should expect to review.
Technical design
- Prompt contract and allowed-source definition
- Typed output schema and validation rules
- Draft, source-check, review, approve, reject, block, and escalate states
- Reviewer workbench or UI specification
- Audit event and evaluation baseline
Integration and data handling
- Model access can be local, private, or managed depending on risk and operational constraints.
- Downstream writes are separated from generation and require explicit approval.
- Identity, role, and source permissions are designed around the existing environment.
Security, review, and governance
- No secret or PHI submission through public forms
- Least-authority service access
- Evidence-visible review states
- Blocked-action logging
- Fallback and manual-completion path
Timeline and responsibilities
What the client provides and what acceptance means
The published timeline assumes timely access to the agreed evidence, system owners, reviewers, and decision makers. Delays in access, source ownership, regulated-data handling, or review can change delivery sequence without changing the public price floor.
Client inputs
- One bounded workflow and named owner
- Representative source documents or approved examples
- Reviewer roles and approval rules
- Known failure scenarios
- Target downstream system and authority constraints
Acceptance criteria
- A working or testable review-state workflow
- Approved prompt and source contract
- Defined blocked and escalation states
- Reviewer-visible evidence
- Pilot evaluation plan and handoff artifacts
Example artifacts
- Workflow map
- Prompt contract
- Typed output schema
- Reviewer-state model
- Audit-event map
- Pilot acceptance checklist
Package FAQ
Questions to resolve before the engagement begins
What does human-reviewed AI mean?
AI output remains proposed work until a named reviewer accepts, rejects, edits, or escalates it under defined workflow rules.
Can the workflow use a local model?
Yes when the environment, task, latency, and security constraints support it. Model choice follows the risk profile rather than a vendor preference.
Can AI write directly to production?
Not by default. High-impact writes require a separate, explicitly authorized and observable control path.
Next step
Confirm fit before sharing private system details.
Use the fit call for an early conversation or request assessment scope when the buyer, system, and decision are already clear.
Next step
Start with a short fit call, then scope the assessment.
The first conversation should decide whether the next step is a fixed-scope assessment, modernization blueprint, governed AI pilot, or reliability review.
Book a 20-minute fit call