Where Open Models Fit in DigDeepIQ's Acreage-Reporting Workflow

An acreage value can be numerically correct and still belong to the wrong field.

That single failure explains why "use a local model for the easy calls" is not enough for DigDeepIQ. A model can read 83.4, emit 83.4, pass a numeric validator, and damage the report if it attached the value to the neighboring farm, tract, field, crop, share, or practice.

Document automation is not a bag of extracted values. It is a custody chain.

This is part 4 of a four-part series:

1. Hybrid AI Model Routing Is an Operating Boundary
2. A Private GPT-OSS Service on Cloud Run
3. Cloud Run GPU or Model API: The Break-Even Math
4. Where Open Models Fit in DigDeepIQ's Acreage-Reporting Workflow

This post is a public reference architecture, not a disclosure of DigDeepIQ's production implementation, customer data, model prompts, or internal thresholds. The business framing comes from DigDeepIQ's public site and launch story; the reporting requirements come from public USDA Risk Management Agency material. Example records and thresholds are synthetic.

The Business Problem Is Repeated Truth#

DigDeepIQ describes its products as practical, secure AI tools informed by agricultural insurance experience. Its public launch story begins with a USDA Form 578 and a crop-insurance acreage report, and with office work that could take anywhere from minutes to days depending on the case.

USDA explains why the two documents are related. The Acreage Crop Reporting Streamlining Initiative exists because producers often report similar acreage information through FSA and crop-insurance channels. The common data can prepopulate and accelerate the second report, but producers still validate information, provide program-specific details and maps, and sign the required reports.

The repeated information includes consequential details. RMA's insurance-cycle guidance names planted or prevented acreage, planting dates, share, acreage location, farming practice, and type or variety. Its current policy material also emphasizes land identifiers and limits around revising submitted acreage information.

The product opportunity is therefore not generic PDF summarization. It is moving repeated facts through a controlled transformation while preserving the evidence needed to review differences.

The Custody Chain#

I would divide the workflow into seven links:

Every link should emit an artifact the next link can inspect. If the system jumps from PDF bytes to a completed report without an intermediate evidence model, reviewers can see the output but cannot explain the transformation.

Link 1: Intake and Document Identity#

The first task is not extraction. It is deciding what arrived.

A package may contain a Form 578, an acreage report, maps, continuation pages, blank pages, signatures, or unrelated supporting material. A private text model can classify pages after OCR when the categories are known and the evidence is explicit.

{
  "document_id": "package-example-17",
  "page": 3,
  "candidate_type": "fsa_578_continuation",
  "confidence": 0.97,
  "supporting_spans": ["Farm No.", "Tract No.", "Field No.", "Reported Quantity"],
  "model_route": "private_gpt_oss_20b",
  "requires_review": false
}

The model is eligible because it chooses among a bounded set and cites visible text. A deterministic preflight still checks file type, page count, encryption, size, and whether OCR produced usable text.

Use a hosted multimodal route or manual review when the page is image-only, handwriting-heavy, rotated, damaged, or structurally unfamiliar. GPT-OSS 20B is a text model; pretending OCR removed every visual dependency would create a capability gap.

Link 2: Page and Row Evidence#

Extraction should produce evidence coordinates, not only values.

For every candidate row, retain:

source_document: fsa_578
page: 3
row: 11
cells:
  farm_number:
    text: "0012"
    bounds: [72, 418, 118, 438]
  tract_number:
    text: "004"
    bounds: [121, 418, 162, 438]
  field_number:
    text: "0003"
    bounds: [165, 418, 210, 438]
  crop:
    text: "CORN"
    bounds: [214, 418, 286, 438]
  reported_acres:
    text: "83.4"
    bounds: [512, 418, 558, 438]

The values are synthetic. The contract is the useful part.

If 83.4 appears twice on the page, coordinates disambiguate the claim. If a reviewer changes the crop, the system can show exactly which source cell originally supported the candidate. If a model copies a neighboring value, geometry-aware validation can catch the mismatch.

A hosted vision model may be justified for complex visual extraction, especially during initial parsing. The private open model can still supplement later calls by operating on the extracted row evidence rather than repeatedly receiving the whole PDF.

That distinction saves more than tokens. It narrows the context and gives every later step a stable, reviewable input.

Link 3: Candidate Normalization#

Normalization is an excellent private-model target when its authority is limited.

Suppose a source row contains:

Crop: CORN
Intended use: GR
Practice: IRR
Reported acres: 83.4

The private model can map descriptive text into candidate vocabulary entries supplied in the prompt:

{
  "crop_candidate": "corn",
  "intended_use_candidate": "grain",
  "practice_candidate": "irrigated",
  "reported_acres_candidate": 83.4,
  "unsupported_values": [],
  "source_row": 11
}

Deterministic code then checks the candidates against versioned code tables, valid combinations, numeric ranges, crop-year rules, and the source evidence.

The failure mode is asking the model to supply missing values. If practice is blank, the correct candidate may be null, not the most common practice for that crop and county. A language model's plausibility prior is exactly what the workflow must resist.

Link 4: Source-to-Target Reconciliation#

Reconciliation belongs primarily to deterministic code.

Once source and target values are structured, equality, normalization, tolerances, totals, and missingness should not require a language model.

type CandidateRow = {
  farm: string | null;
  tract: string | null;
  field: string | null;
  crop: string | null;
  acres: number | null;
};
 
type Difference = {
  field: keyof CandidateRow;
  source: string | number | null;
  target: string | number | null;
  material: boolean;
};
 
export function reconcile(source: CandidateRow, target: CandidateRow): Difference[] {
  const differences: Difference[] = [];
 
  for (const field of Object.keys(source) as (keyof CandidateRow)[]) {
    if (source[field] !== target[field]) {
      differences.push({
        field,
        source: source[field],
        target: target[field],
        material: ["farm", "tract", "field", "crop", "acres"].includes(field),
      });
    }
  }
 
  return differences;
}

Real normalization may handle leading zeros, source-specific identifiers, decimals, shares, dates, and code tables. Those rules should be named and tested. A model can help draft or propose a rule during development; it should not dynamically redefine equality per request.

The reconciliation artifact should classify:

• exact agreement;
• normalized agreement;
• source missing;
• target missing;
• conflicting supported values;
• unsupported candidate;
• ambiguous row association;
• duplicate or collision risk.

Only some classes are auto-resolvable. Conflict and ambiguous association should remain visible.

Link 5: Exception Explanation#

Exception explanation is another strong private-model task because the inputs can be verified facts.

Instead of giving the model raw documents and asking "what happened?", give it a constrained difference record:

{
  "row_id": "synthetic-row-11",
  "facts": {
    "source_acres": 83.4,
    "target_acres": 38.4,
    "source_page": 3,
    "source_row": 11,
    "target_page": 1,
    "target_row": 7
  },
  "allowed_labels": ["likely_transposition", "source_target_conflict", "insufficient_evidence"]
}

The model can return:

{
  "label": "likely_transposition",
  "reviewer_summary": "Source row 11 shows 83.4 acres while target row 7 shows 38.4 acres. The digits appear transposed. Confirm against the source image before correction.",
  "recommended_action": "review",
  "facts_used": ["source_acres", "target_acres", "source_page", "target_page"]
}

The model did not choose the corrected acreage. It made a reviewed discrepancy easier to understand.

This can improve business throughput even when it does not eliminate a review step. A reviewer who sees the conflicting values, page locations, materiality, and a concise explanation can make a faster decision than one who must reconstruct the whole comparison.

Link 6: Acreage-Report Generation#

The final report should be generated from an accepted structured record, not from free-form model output.

accepted structured record
  -> deterministic report writer
  -> rendered PDF
  -> field-level verification
  -> reviewer preview

The writer owns field placement, formatting, pagination, continuation pages, totals, and required blank behavior. The model may draft a note or explanation that is clearly separated from the official fields. It should not paint values directly into arbitrary PDF coordinates without a document contract.

Verification should compare the accepted record with the rendered artifact. A correct database record and an incorrect PDF are still a failed report.

I would retain:

• the accepted structured input;
• the writer version;
• the generated PDF checksum;
• text or geometry extracted from the rendered PDF;
• the comparison result;
• the reviewer decision.

That closes the custody chain.

Link 7: Review, Sign, and Submit#

USDA's public guidance makes the human boundary clear: producers validate information and sign required reports. Automation can prepare evidence and reduce repetition. It does not erase that responsibility.

The final workflow should make these states distinct:

generated != reviewed
reviewed != signed
signed != submitted
submitted != accepted by downstream system

A green model response must not collapse them into "done."

The reviewer needs a presentation designed around consequence:

• material differences first;
• source and target evidence side by side;
• model-generated explanations labeled as explanations;
• unresolved blanks and ambiguities visible;
• an explicit record of every accepted correction;
• a separate authenticated sign or submit action.

The Routing Table#

Here is the architecture in one table:

This table prevents a common architecture drift: a model starts as an assistant, then gradually becomes the hidden decision-maker because each new prompt is easier than adding a proper state or rule.

The Business Metrics#

The private route should be evaluated against business flow, not token count alone.

I would track:

The public DigDeepIQ story describes a very wide completion-time range. That makes p95 and exception age more informative than an average alone. A fast normal report can hide one difficult package sitting in a queue for days.

The Promotion Experiment#

I would promote one bounded task first: exception explanation from verified difference records.

Why not raw visual extraction? Because explanation lets the team test the private service, structured output, trace ledger, reviewer usefulness, and cost without placing the model at the beginning of the custody chain.

The experiment:

task: reconciliation_exception_explanation
mode: shadow
inputs:
  source: verified_difference_records
  production_documents_sent_to_model: false
comparison:
  baseline: current_explanation_path
  candidate: private_gpt_oss_20b
metrics:
  - schema_validity
  - unsupported_fact_rate
  - reviewer_usefulness
  - reviewer_seconds
  - latency_p95
  - cost_per_accepted_explanation
promotion_requires:
  zero_unsupported_material_facts: true
  reviewer_time_not_worse: true
  accepted_cost_below_baseline: true
rollback: disable_private_explanation_route

The thresholds are intentionally described rather than fabricated as DigDeepIQ production numbers. The team should set them from its actual release standard and reviewed corpus.

If the route passes, move to page classification or candidate normalization. Visual extraction and ambiguous row ownership should come later because the consequences and evaluation surface are larger.

The Business Case#

The strongest business case for a private model is not "free tokens."

It is a controlled capacity lane for repeated, bounded work:

• lower marginal dependence on hosted calls during concentrated processing windows;
• private handling for eligible text transformations;
• more predictable throughput for known task classes;
• faster reviewer comprehension through grounded explanations;
• a fallback path that preserves frontier capability for hard cases;
• an evaluation asset that becomes more valuable with every reviewed exception.

The tradeoff is ownership. DigDeepIQ would own model loading, runtime patching, evaluation, capacity, observability, and rollback for the private lane. Part 3's cost analysis shows why that ownership is justified only when utilization and accepted-result economics produce a real margin.

The model boundary should make the product safer as it gets cheaper. If cost pressure encourages the system to infer blanks, hide conflicts, weaken provenance, or bypass review, the architecture has optimized the wrong unit.

Start with verified facts and bounded language work. Keep exact rules in code. Keep difficult perception on the route that passes the evaluation. Keep sign and submit with the authorized person. Price the entire accepted report.

That is how an open model supplements DigDeepIQ without becoming an unaccountable copy of the workflow it was meant to improve.

Sources#

• DigDeepIQ
• DigDeepIQ: About Us
• Introducing DigDeepIQ
• USDA RMA: Acreage Crop Reporting Streamlining Initiative
• USDA RMA: Insurance Cycle
• USDA RMA: Getting Acreage Reporting Right
• USDA RMA: Requirement to Report Field Location
• USDA RMA: 2027 Common Crop Insurance Policy Basic Provisions
• GPT-OSS 20B model card