Precision
Resolve the correct establishment, reporting period, classification, geography and record version before analysis.
Business + technical discovery
We started with the industrial record lifecycle, not with an AI feature list
DES-Prime was modelled as a statistical organisation responsible for maintaining industrial establishment records, scrutinising periodic returns, resolving exceptions, consolidating accepted records and producing controlled statistical outputs.
Engagement mandate
Modernise industrial record keeping without weakening statistical control
The engagement mandate was to reduce the effort required to find the authoritative record, understand discrepancies and prepare statistical decisions while preserving version history, source authority, role separation and human approval.
Assessment focus
Establishment identity
Annual industrial returns
Classification & metadata
Validation & scrutiny
Corrections & versioning
Historical series
Publication lineage
Simplicity
Give officers one governed investigation path across fragmented operational sources.
Security
Keep tool and dataset authority outside the language model and enforce role-scoped access.
Human governance
AI may retrieve, validate, explain and prepare; authorised officers retain correction, acceptance and publication authority.
Discovery findings
What the assessment found and how each finding changed the implementation
The solution decisions were tied to concrete operating risks in industrial statistical record keeping.
| Assessment areaWhere we looked | Evaluation questionWhat we needed to understand | Implementation decisionWhat this means for the solution |
|---|---|---|
| Establishment master | Can the same enterprise or unit appear under changed names, unit suffixes or historical classifications? | Resolve identity through controlled IDs, registration attributes, geography and reporting relationship; never merge on semantic similarity alone. |
| Annual returns | How do officers distinguish initial, corrected, accepted and superseded submissions? | Treat reporting period and record version as mandatory retrieval dimensions and expose status in every answer. |
| Validation & scrutiny | Where are rule failures, officer remarks and clarification outcomes recorded? | Make validation and correction history first-class evidence; AI cannot silently repair a failed value. |
| Classification metadata | Can sector/NIC definitions or applicability vary by period? | Version metadata and validate compatibility before comparison or aggregation. |
| Historical analysis | How are trends affected by corrections, unresolved returns and classification changes? | Trend tools consume accepted/version-qualified observations and disclose exclusions. |
| Authority & publication | Which actions alter authoritative records or official outputs? | Separate READ, PREPARE, APPROVE and PUBLISH permissions; protected actions require authenticated human approval. |
Primewayz implementation decision
Selected architectureAdd a governed intelligence layer around existing statistical systems
Primewayz did not replace the industrial register with an AI-owned datastore. The selected design preserves authoritative systems and introduces purpose-specific tools for identity resolution, retrieval, validation, comparison, evidence assembly and controlled preparation.
First implementation scope
- Read authorised industrial masters and returns
- Resolve identity, period, geography and classification
- Run deterministic validation and comparison rules
- Prepare evidence-backed explanations and working tables
- Create correction/clarification packages for officer review
- Capture request-to-evidence audit traces
Kept outside autonomous execution
- Autonomous alteration of authoritative establishment records
- Silent correction of submitted statistical values
- Autonomous acceptance of returns for aggregation
- Unapproved release of official statistical tables
- Model-generated replacement for departmental statistical methodology
Target operating process
Industrial records move through a controlled evidence chain
AI shortens investigation and preparation while preserving the same statistical decision points.
- 01
Receive / import
Industrial return enters with establishment identity, reporting period, source and version context.
- 02
Resolve identity & classification
The system verifies establishment, geography, sector/NIC and reporting relationship against controlled masters.
- 03
Validate
Completeness, range, relationship, historical-variance and metadata rules produce explicit pass, warning or exception states.
- 04
Investigate exception
Agent retrieves previous returns, corrections, remarks and related evidence and explains the discrepancy without changing the source value.
- 05
Clarify / correct
Officer obtains clarification; a corrected version is recorded with reason, actor and timestamp while the original remains preserved.
- 06
Scrutinise & accept
Authorised statistical role decides whether the record is fit for aggregation.
- 07
Analyse & prepare
Accepted records feed controlled comparisons, tables and analytical notes with lineage retained.
- 08
Approve & disseminate
Official output follows departmental approval and publication controls; AI does not bypass them.
Operational change
From multi-file reconciliation to evidence-led officer review
Employee-led investigation
- Search establishment master and reporting files
- Locate correct year and submission version
- Check classification and geography manually
- Open validation workbook and remarks
- Find correction history
- Recalculate comparison
- Prepare explanation and references
- Seek supervisory decision
Employee-validated resolution
- Ask the operational question in natural language
- Agent resolves entity, period and authority scope
- Bounded tools retrieve the evidence chain
- Deterministic rules reproduce validation/calculation
- Agent explains discrepancy and uncertainty
- Officer reviews sources and chooses controlled next action
Operating roles
AI changes preparation effort, not statistical accountability
Each role keeps a defined decision boundary.
| Stakeholder | Role in the AI-assisted process | Retained ownership |
|---|---|---|
| Industrial Statistics Officer | Investigates returns, reviews evidence and prepares statistical decisions. | Interpretation, clarification and working acceptance recommendation. |
| District / Field Statistical User | Provides or verifies unit-level clarification and supporting records. | Source clarification within assigned jurisdiction. |
| Supervisory Statistical Officer | Reviews material corrections, acceptance and analytical outputs. | Approval for controlled statistical state changes. |
| Publication / Reporting Team | Consumes approved aggregates and prepared tables. | Official release workflow and publication controls. |
| System Administrator | Maintains identity, roles, connectors and audit retention. | Technical access and platform administration, not statistical decisions. |
Implementation requirements
What had to exist for the agent to be trustworthy
The reference implementation treats data contracts and governance as prerequisites, not later hardening.
Data contracts
- Stable establishment identifier
- Versioned reporting period
- Geography master ID
- Industry classification code/version
- Submission state and source authority
Tool contracts
- Explicit parameters
- Role-scoped datasets
- Deterministic calculations
- Failure/ambiguity behaviour
- Evidence references returned
Governance
- Authenticated identity
- Role separation
- Approval evidence
- Immutable source/version history
- No model-granted permissions
Evaluation
- Known-answer corpus
- Adversarial ambiguity cases
- Numerical fidelity checks
- Access-control tests
- Regression suite
Operational proof cases
Three operational situations demonstrate the selected approach
These cases show why the first implementation focuses on investigation, explanation and recommendation rather than unrestricted automation.
Why did employment collapse while output increased?
What the investigation found
A current return contained 31 employees versus 326 previously. The agent surfaced a -90.5% variance, found no approved status change and routed clarification. The corrected version recorded 310 with the original preserved.
Business value
Open this case Turns a suspicious number into an auditable exception package instead of an undocumented manual correction.
Are two similar establishment names the same reporting unit?
What the investigation found
Registration, address, unit relationship and classification evidence showed Unit-II was separately reportable. Semantic similarity alone would have produced an incorrect merge.
Business value
Open this case Protects establishment-frame integrity and prevents double counting or accidental consolidation.
Which sectors explain a district manufacturing-employment decline?
What the investigation found
The agent selected accepted records for matching periods/classifications, excluded unresolved submissions and calculated sector contributions with source lineage.
Business value
Open this case Reduces preparation effort while keeping the analytical result reproducible by an officer.
Human intelligence remains authoritative
The agent can prepare evidence; DES-Prime decides statistical state
Authority is enforced at the tool/action boundary, not left to prompt wording.
- Search authorised records
- Resolve controlled dimensions
- Run approved validation rules
- Compare compatible periods/geographies
- Draft clarification and analytical notes
- Prepare reviewable tables
- Correct authoritative values
- Approve master-data changes
- Accept returns for aggregation
- Approve methodology exceptions
- Release official statistics
- Override or reject AI recommendations
Controlled progression
Automation expands only after repeatable evidence
Recurring low-risk preparation can be automated while exceptions and official state changes remain governed.
- 01
Assist
Retrieve and prepare evidence for officer review.
- 02
Act with approval
Execute a bounded correction/package action only after an authorised approval event.
- 03
Automate bounded preparation
Run scheduled validation or monitoring where scope and failure handling are deterministic.
- 04
Improve through regression
Use reviewed failures to extend rules, mappings and evaluation coverage.
Primewayz progression gate
A recurring task moves toward automated execution only when all of the following are clear:
- Source and record scope is explicit
- Permission is independently enforceable
- Expected result is testable
- Failure routes to a human owner
- Action is observable and reversible where applicable
- Official publication remains separately authorised
Explore the case
See the operational proof or go deeper into the implementation
Primewayz industrial statistical intelligence