Enterprise AI Value Assessment Platform
The AIM AI Value Assessment Platform turns a consulting method into software, so every figure in a client deliverable can be reproduced from the inputs that produced it.
The numbers in a client deliverable had to be defensible.
AIM runs AI value assessments and presents the result to a client executive audience. Before this platform, the method lived in the consultants who ran it and in the documents they produced, which left four problems.
- Assessment output depended on who ran it. No single ratified methodology version stood behind a score.
- A figure in a finished deliverable could not be reconstructed from its inputs once the engagement had moved on.
- AI-drafted estimates and consultant judgement read the same way on the page, so a reviewer could not tell which was which.
- Nothing forced review and approval before a deliverable left the practice, and nothing recorded who changed a figure.
Ratify the method, then build the boundary.
AIM’s response addressed each problem directly: ratify one methodology version, enforce it in the architecture rather than in habits, and prove the guarantees with automated gates.
Assess and ratify
The constants for value, feasibility, zones and financials were ratified as methodology version aim-assessment-v1, and 130 requirements were written down with permanent identifiers and a stated source-of-truth precedence.
Build the boundary into the architecture
The model estimates inputs. Server-side code computes every output. Client scoping sits in the data layer rather than the interface, and each write is paired with an append-only audit record inside the same transaction.
Prove it with gates
A hermetic test suite runs with no database, container or network egress. A separate PostgreSQL suite carries the guarantees only a real database can prove, and migrations are applied by the pipeline rather than by hand.
Seven stages, one evidence chain.
Each stage feeds the next: client profile, stakeholder map, discovery, data assessment, opportunity engine, optional technical debt, executive report. Six workstreams delivered it.
Client lifecycle and scoping
Tenant-scoped client records with duplicate detection and archive-not-delete.
Create, search, read with portfolio counters, update under optimistic concurrency, and archive instead of delete. Scope comes from the validated Entra tenant claim and is never accepted from the caller, so a record belonging to another tenant returns 404 rather than 403. A matching name or domain returns a duplicate warning with the matches and an explicit override rather than a silent second client.
Discovery and evidence capture
Twelve seeded departments, 122 versioned interview questions, evidence stored in Azure Blob.
Twelve departments seeded on client creation, each with its own interview guide, 122 questions in total, superseded by a new version on edit rather than mutated in place. Stakeholder mapping, document upload and transcript capture run through Azure Blob Storage, with artifact reads and deletes pinned to the owning client’s folder.
Data and security assessment
Seven security dimensions and thirteen maturity dimensions, scored server-side and editable by the consultant.
Seven security posture dimensions with four criteria each, and thirteen maturity dimensions on a one to five scale that map business outcomes to their technical enablers and place each pairing in a quadrant. Generation runs server-side against the Azure OpenAI gpt-4o deployment, and every score stays editable by the consultant.
Opportunity engine
Weighted value and feasibility scoring routes items to Act now, Invest to enable, Fill in or Park.
Value is a weighted sum of net benefit (40 percent), benefit class (25), confidence (20) and time to value (15). Feasibility weighs data readiness, technical complexity, change adoption, risk and dependencies. A 3.0 threshold on each axis places an item in Act now, Invest to enable, Fill in or Park. Three-year NPV discounts a 55, 90 and 100 percent benefit ramp at 12 percent a year, reported with payback and ROI. An opportunity above the benefit threshold cannot reach Accepted on an unedited AI estimate without a baseline metric, a measurement method, a confidence level and written rationale.
Technical debt ROI
A five-component legacy cost model built on sourced, dated configuration, not code literals.
A five-component annual legacy cost model covering maintenance, productivity loss, developer drag, security and risk, and opportunity cost. Industry and stack multipliers, programme unit costs and recovery rates are configuration records carrying a cited source and an effective-from date, not code literals. Every figure can show its arithmetic, and AIM unit pricing stays out of client-visible output.
Executive report and deliverables
Word and PowerPoint deliverables gated on preparer, reviewer and approver sign-off.
Six report sections generated from stored evidence and persisted as each one completes, so a mid-run failure keeps the finished work. Word and PowerPoint generators produce the deliverables. Export is blocked until preparer, reviewer and approver are populated, with reviewer and approver distinct from the preparer, and approval held to the practice lead role.
An assessment that holds up under review.
Every figure in the assessment traces back to a ratified method, an enforced scope and an audited change, proven by a test suite that needs no live infrastructure to run.
One methodology version stands behind every figure. Identical inputs produce identical value, feasibility, zone, NPV, payback and ROI, independent of model, prompt version or date.
Client isolation is enforced where it cannot be bypassed. Repository functions take the scope first and fold the tenant into every query, a service that reaches the database client directly counts as a defect, and the PostgreSQL suite proves the scoping at SQL level.
Every create, update and delete carries its audit row, written in the same transaction as the change itself, so the record of who changed a figure cannot drift from the figure.
Twenty-six test files, with the default run needing no database, container or network egress. The database-only guarantees report as skipped with a stated reason rather than quietly passing.
Outcomes that hold up.
Defensible numbers
A figure in front of a client executive traces back to the opportunity record and the stored inputs that produced it.
Controls are structural
Scoping, append-only audit and server-side model access are properties of the architecture, not habits of the team.
Repeatable at practice scale
A ratified methodology, seeded departments and versioned interview guides make the second assessment look like the first.
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