vk-gramatvediba/docs/methodology.md

Methodology — vk-gramatvediba transcription pipeline

Context

The Valsts Kase (State Treasury) together with the Vienotais Pakalpojumu Centrs (VPC) publishes a normative description of public-sector bookkeeping in Latvia — Grāmatvedības uzskaites procesu apraksts — as a set of six function-group spreadsheets (FG1–FG6) spanning planning, expenditure, settlement, fixed assets, payroll and reporting. Each register row is a process step with an explicit responsible actor (RACI), the IT system used, an SLA, the data produced, and predecessor/successor references to other steps in the same or adjacent registers.

This workspace, vk-gramatvediba, is a UAPF 2.2.0 transcription of the FG3 register (saistību un izdevumu uzskaite — obligations and expenditure accounting) into executable process artefacts. It is one of the projects accepted into the Latvian AI regulatory sandbox (MIC), with PPPA as institutional applicant and KISC as pilot partner.

The transcription is not a one-off translation. It is a methodology — a repeatable two-pass pipeline from published normative documents to signed, runnable process packages. This note describes the methodology, shows how the same pipeline applied to two sub-processes (3.5.2 and 3.5.3) produced the curated FG3-4 and FG3-5 packages, and reports the final validation pass over the workspace.

The pipeline

Transcription has two passes with deliberately different epistemic status.

Pass 1 — mechanical transcoding. A deterministic tool reads the register and emits a BPMN skeleton: one task per register step, swimlanes from RACI, sequence flows from the register's own predecessor/successor columns, synthesised start/end events at the fragment's real boundary. The skeleton is faithful to the register — including its inconsistencies — and is isExecutable="false". This pass lives in tools/register-transcoder/.

Pass 2 — curated refinement. A human curator, optionally AI-assisted, takes the skeleton and resolves it into a Level 4 executable: explicit gateways, decision logic extracted into DMN, resource roles/agents/mappings, metadata (ownership, lifecycle, policies), and a package manifest. The result is a processes/<id>/uapf.yaml package that validates against the UAPF 2.2.0 schemas and against uapf-cli, and is runnable on the uapf-engine.

The separation is the methodology's load-bearing claim. Pass 1 is deterministic and traceable — re-running the transcoder on the same register produces identical output, and every node has a mechanical provenance to a register row. Pass 2 is interpretive and signable — it adds judgement the register does not contain (rule tables that the register describes only in prose, branches the register implies in footnotes, metadata the register does not carry), and the curator is identified in the package's ownership metadata. The two passes are mechanically distinguishable, and the workspace makes that visible.

Pass 1 in detail — the transcoder

The transcoder, tools/register-transcoder/transcode.py, is a single-file Python tool with one external dependency (openpyxl). It locates the worksheet and header row by content rather than by position, so it tolerates the leading title rows the registers carry and applies unchanged to any of the FG1–FG6 registers. It expects the standard register columns: the predecessor block (FG-group and step-number in adjacent cells), the step's Nr.p.k., Process, apakšprocess, the RACI block split across the three actor sub-columns (Nodarbinātais / Iestāde / VPC), Darbību apraksts, Izmantotā IS, Izpildes termiņš, Sagatavotie dati, and the successor block Uz procesa darbības soli.

Rows that carry a number and a name but no description and no RACI entry are treated as sub-process headers; rows with description or any RACI entry are treated as steps and assigned to the most recently encountered header. For a requested sub-process the transcoder emits a single BPMN process containing one bpmn:userTask per step, with the step's description, system, SLA, RACI cells and any cross-sub-process or cross-FG references preserved in bpmn:documentation; swimlanes for each actor that has steps in the sub-process, with each step placed in the lane of its Responsible actor; sequence flows reconstructed from the union of predecessor and successor references whose endpoints are both inside the sub-process; and one bpmn:startEvent per entry step (no in-group predecessor) and one bpmn:endEvent per exit step (no in-group successor), so the fragment's real boundary is visible rather than hidden behind synthesised gateways.

The output is isExecutable="false" and deliberately unembellished: no inferred gateways, no synthesised decision logic, no compensation for register-side inconsistencies. Reproducing register defects is a feature — it makes the refinement step's contribution explicit.

Pass 2 in detail — the refinement

Pass 2 takes a skeleton and authors the material the register implies but does not encode. The operations, in roughly the order they apply:

• Link reconciliation — the skeleton may show reciprocal edges, short cycles, or disjoint fragments where the register's predecessor and successor columns disagree. The curator decides the intended topology and rewrites flows accordingly.
• Gateway promotion — a task whose semantics implies a decision is split into the evaluating step plus an explicit gateway (typically exclusiveGateway) with named branches; multiple register steps that represent alternative outcomes collapse into branches.
• DMN extraction — where the register's prose implies a rule table, the decision is lifted into a separate .dmn file with FIRST or UNIQUE hit policy and named inputs/outputs. The BPMN gets a businessRuleTask whose decision reference points to the DMN decision.
• Resource authoring — resources/roles.yaml, agents.yaml and mappings.yaml enumerate the responsible parties, bind roles to the systems named in Izmantotā IS (HoP, RVS Horizon, ePNS, etc.), and link BPMN tasks to roles and agents.
• Metadata authoring — metadata/ownership.yaml, lifecycle.yaml and policies.yaml record who curated the package, its UAPF lifecycle stage, and policy constraints (retention, signing, jurisdictional applicability).
• Manifest assembly — the package's uapf.yaml lists kind, id, name, level (4 for atomic executables), cornerstones referencing the BPMN, DMN, resources and metadata, exposed inputs/outputs/artifacts, and any MCP exposure.

The refined package validates against the UAPF 2.2.0 schemas and against uapf-cli validate. Once validated, the package is signable.

Concrete comparison — 3.5.2 / FG3-4

Sub-process 3.5.2 (Saimnieciskie norēķini un to kustība) has three register steps. The transcoder's sample-output/3.5.2.skeleton.bpmn contains three userTasks in two lanes (Nodarbinātais for 3.5.2.1 and 3.5.2.2, VPC for 3.5.2.3), four sequence flows, one start event and one end event. It makes two register-side artefacts visible. Step 3.5.2.1 (the advance request) has only external successor references — it routes out to FG2/2.3.2 (budget commitment) and back, and is not directly linked to 3.5.2.2 in the register's columns, so the skeleton shows it as a small linear fragment of its own. Steps 3.5.2.2 and 3.5.2.3 have reciprocal predecessor/successor entries in the register — each lists the other in both directions — so the skeleton renders a two-task cycle.

The curated processes/fg3-4 package resolves both. Its BPMN Process_SaimnieciskaNorekina has 14 nodes and 14 flows across all three lanes, a single clean entry and exit, and a businessRuleTask linked to a separate DMN. The DMN Decision_AvansaNorekins is a FIRST-hit decision with five rules; its inputs are avansaSituacija and avansaVeids, and its output norekinResultats takes one of four values — slegts, atmaksa, papildu-izmaksa, parnesums — the four outcomes the register describes in prose but does not encode in a table. The gateway downstream of the DMN routes to the finalisation task for each outcome (close, reclaim from employee, additional disbursement, carry forward).

Three tasks in the skeleton; 14 nodes plus a 5-rule DMN in the executable. None of what the executable adds is in the register's columns. It is in the register's prose, in the SLA cells, and in the normative documents the register cites — Grāmatvedības likums, MK noteikumi Nr. 749, MK noteikumi Nr. 877. Pass 2 is where that material enters.

Concrete comparison — 3.5.3 / FG3-5

Sub-process 3.5.3 (Komandējuma (darba brauciena) dokumenti un to kustība) has four register steps. The transcoder's sample-output/3.5.3.skeleton.bpmn contains eight nodes and six flows.

The curated processes/fg3-5 package likewise has 14 nodes and 15 flows across three lanes. It introduces an explicit cancellation branch — a trip annulled before settlement vs a trip proceeded with — and the DMN Decision_KomandejumaNorekins whose parnesums rule reconciles an advance surplus against the next approved business trip from the same funding line. Neither the cancellation branch nor the carry-forward rule is in the register's predecessor/successor columns; both are in the register's prose and in the cited Komandējuma izdevumu noteikumi.

Final validation pass

The workspace at HEAD a608de4 contains three Level 4 executable packages, six Level 2 composition stubs, the function-group L2 manifests, the transcoder tool, and this methodology note. The validation pass run for this step:

• uapf-cli validate processes/fg3-4 → OK: package valid.
• uapf-cli validate processes/fg3-5 → OK: package valid.
• uapf-cli validate processes/fg3-1 was passed at its build session (see commit 81d32e8) and the package has not been touched since.
• All .bpmn and .dmn files in the workspace are XML-well-formed (xmllint --noout).
• All schema-validated UAPF files (uapf.yaml, resources/*.yaml, metadata/policies.yaml) pass the UAPF 2.2.0 JSON schemas.
• BPMN graph integrity: every sequenceFlow references existing sourceRef/targetRef nodes; every flowNodeRef resolves to a defined node; every incoming/outgoing reference is consistent with the corresponding flow's source/target.
• The transcoder is byte-deterministic: re-running it on the FG3 register for 3.5.2 and 3.5.3 reproduces the committed sample-output/ files exactly.

Implications for the AI regulatory sandbox

The pipeline has four properties that bear on the sandbox's evaluation. Provenance: every executable step has a mechanical trace back to a register row, and the refinement layer is recorded in ownership metadata. A reviewer can audit either pass independently. Versionability: the register is itself versioned (publication dates, changelog); the workspace is git-versioned on processgit; the packages carry lifecycle metadata. A register change triggers a re-transcode and a diffable refinement. Separability of judgement: what the register says and what the refinement adds are mechanically distinguishable — the skeleton is reproducible from the register alone, and the curator's contribution is exactly the diff. Coverage: the same tool applies unchanged to FG1, FG2, FG4, FG5 and FG6 — the parser locates headers by content. Three of FG3's nine sub-processes have curated executables in this POC (FG3-1, FG3-4, FG3-5); the remaining six FG3 sub-processes are composition stubs; the other five function groups are untouched.

The sandbox question is whether AI-assisted transcription of regulatory processes into executable, signable artefacts is feasible at production scale. This workspace is one positive existence proof — small, end-to-end, with both passes shipped and reproducible.

Limitations and next steps

The POC has known gaps. Semantic validation is structural only: the packages validate against UAPF schemas and graph-integrity rules, not yet against accounting-law semantics. There is no automated check that, for instance, the parnesums rule complies with the relevant MK noteikumi or that the deadlines align with Grāmatvedības likums 28.p(5). A second validator layer — rule coverage against the cited statutes — is the obvious next step. Engine execution is in scope for the uapf-engine in isolation but not yet integrated with a Sledger host that would run the packages against real accounting state. Coverage: the POC takes three sub-processes to L4; production-grade coverage of FG3 alone needs six more, and FG1, FG2, FG4, FG5, FG6 are still untouched. Refinement automation is currently human-driven; quantifying the AI-assisted portion of Pass 2 — applying an LLM to the skeleton and measuring the curator's remaining diff — is the natural sandbox experiment that follows.

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References inside the workspace: tools/register-transcoder/README.md for the transcoder's CLI and register-format assumptions; processes/fg3-1, processes/fg3-4, processes/fg3-5 for the curated executables; tools/register-transcoder/sample-output/ for the skeletons the comparisons in this note refer to.