03 Implement · Governed Implementation
Build the system and the evidence at the same time.
We implement the AI and automation workflows your firm needs - and design the controls, human review points and audit trail as part of the build, not as a later remediation project.
Built into every workflow
- Defined control points before the model acts
- Human review at agreed thresholds
- Decision logs with reviewer and rationale
- Version records for prompts, models and rules
- Exception handling and escalation routes
- Retention aligned to the firm's obligations
Who this is for
Firms that want the operational benefit without inheriting the exposure.
- Practices with a manual process that is expensive, slow and repetitive
- Firms whose pilot works but cannot be signed off for production use
- COOs consolidating processes across offices after a merger
- Risk leaders who need to see the control design before a system goes live
- Innovation leads who need delivery capability alongside governance
Outcomes
What changes for the firm
Capacity released
Repetitive preparation, checking and routing work handled by the system, with people on judgement.
Production confidence
A workflow that risk and compliance can approve because the control design is explicit.
Evidence by default
Every run leaves a record - inputs, controls, reviewer and outcome - without extra effort.
What Alacrix does
One team designs the control environment and delivers the build.
Splitting these across a consultancy and a build partner is where evidence gaps appear. We scope the use case, agree the control design with risk owners, build the workflow and hand it over documented and monitored.
- Use-case scoping with a clear view of value, risk and review burden
- Control design agreed with risk and compliance before build starts
- Workflow build using tooling the firm can maintain or contract for
- Human-in-the-loop review steps with thresholds and fallbacks
- Decision logging, versioning and retention configured from day one
- Testing against failure modes, not just the intended path
- Documentation, handover and a monitoring routine with named owners
Examples
What we typically implement
Workflows chosen because they are repetitive, high volume and reviewable - the places where governed automation pays back quickly.
Document extraction
Structured data pulled from engagement letters, contracts, statements and correspondence, with confidence thresholds and review on exceptions.
Structured email workflows
Inbound client mail classified, summarised and routed with a defined human checkpoint before anything leaves the firm.
Validation and reconciliation
Automated checks across ledgers, schedules and source documents, with exceptions raised to a named reviewer rather than silently resolved.
Exception analysis
Anomalies surfaced with the underlying evidence attached, so the reviewer sees why an item was flagged.
Management commentary drafting
First-draft narrative generated from agreed figures, clearly marked as draft and gated behind review and sign-off.
Routing and classification
Matters, requests and documents categorised and assigned consistently, with the classification recorded and reversible.
CRM and finance mismatch detection
Cross-system differences identified and evidenced before they become billing, reporting or client-service problems.
Client-request routing
Requests triaged to the right team with service commitments tracked and an auditable record of handling.
The evidence line
Governance should live inside the workflow.
AI input
Document, message or data enters the workflow.
Control
Permitted-use, data and confidentiality checks apply.
AI action
The model extracts, drafts, classifies or reconciles.
Human review
A named person reviews at a defined threshold.
Decision
The outcome is approved, amended or rejected.
Evidence
Inputs, versions, reviewer and rationale are logged.
Deliverables
What you leave with
Governed, not generic
What makes it governed rather than just built.
A working automation proves that it works. A governed implementation also shows what it did, who checked it and why the outcome was accepted.
- Controls execute inside the workflow, so they cannot be skipped under pressure
- Review is a designed step with a threshold, an owner and a recorded outcome
- Model, prompt and rule versions are recorded against each run
- Unresolved risks are surfaced explicitly rather than absorbed by the system
- The record is written for a reviewer who was not there at the time
Engagement process
How the work runs
- 01
Scope
Select the use case on value and risk, map the current process and agree the target control design.
- 02
Build
Implement the workflow with controls, review points and logging in place from the first iteration.
- 03
Operate
Test, document, train the team and hand over with monitoring and clear ownership.
Next step
Build something you could show a regulator.
Start with an AI Risk & Readiness Audit, or bring us a specific process you want implemented properly the first time.