helix-grounding

Catch the numbers an AI made up — without asking another AI.

A language model states a figure that isn't in its source data. In anything touching money, specifications, or deadlines, that's not a quality issue — it's a liability.

The usual defence is to check the output with another model: LLM-as-judge, semantic entailment, embedding similarity. All three are probabilistic, all three cost an inference call, and all three can be wrong themselves.

This doesn't do that. It extracts every currency amount, measurement, identifier, quantity, percentage and date from generated text and checks each against values computed from your source data. For that class of claim the answer is decidable: a value is in the set or it isn't. No model call, no judgement, no confidence score.

pip install helix-grounding
helix-bom demo          # see it catch something, no file needed

from helix_grounding import Verifier, GroundTruth, ClaimKind

truth = GroundTruth().allow_many(ClaimKind.CURRENCY, [18.00, 22.00, 40.00])
report = Verifier().verify(model_output, truth)

if not report.is_grounded:
    print(report.summary())
    # -> UNGROUNDED: 1 of 4 claims not found in source data — $36.00 (currency)
    retry = base_prompt + report.correction_note()

The correction names the specific invented value and quotes the sentence it appeared in, because a blind re-roll reproduces the same error at roughly the same rate.

See it work on your own file

The package ships a complete worked example: a bill-of-materials reviewer built on the library. helix-bom demo runs it against a bundled sample; point it at your own CSV export when you want a real answer.

helix-bom review my_bom.csv --budget 10

BOM total: $13.81  (budget $10.00)

Findings:
  [CRITICAL] BOM total ($13.81) exceeds stated budget ($10.00) by $3.81.
  [WARNING]  ARM Cortex-M4 MCU has a stated lead time of 120 days — this is a
             real supply-chain risk that can silently become the critical path.

NOT CHECKED (3):
  physical fit
      no component dimensions in the submitted data — standard EDA exports
      carry footprints, not millimetres

  These are not passes. Supply the missing columns to check them.

It reads what KiCad, Altium and spreadsheets actually export: preamble lines before the header, semicolon delimiters, do-not-populate rows, and 1.234,56 versus 1,234.56 decided per file rather than per cell.

A check that couldn't run is never reported as a pass. --strict makes "couldn't check" a non-zero exit; --json emits the same for a machine.

Your BOM never leaves your machine

A bill of materials exposes a design, its costs and its suppliers. Reading and reviewing one here is entirely offline — no upload, no telemetry, no account, no network call of any kind.

That is not a promise, it is a test. tests/test_offline_guarantee.py disables Python's socket layer outright and then runs the real code path end to end, so any attempt to reach the network by any library at any depth is a hard failure rather than a quiet one. It also asserts the block itself works, because a guard that silently stops guarding is worse than none.

The only component that can reach out is the optional narrative writer, and it defaults to a model running on your own hardware.

A real fabrication, caught

Not a demo — a model actually wrote both of these while reviewing a BOM:

"the Bosch BME680 at $3.10 is slightly cheaper than your current part at $2.40"

"The ESP32-S3 module at $3.40 has a lead time concern"

The second is caught: $3.20 is the real price, and $3.40 appears nowhere in the source data.

The first passes the check — and should. Both numbers are real. What's false is the word cheaper, a claim about the relation between two values. No value-checker can see that, and a library claiming otherwise would be misrepresenting its own scope. It's prevented a different way: the comparison is computed in Python before the prompt is built, so the model is only asked to phrase an answer that's already correct.

Reproduce both yourself:

python scripts/reproduce_d036.py

Full write-up: docs/CASE_STUDY.html.

What it can't do

Stated plainly, because a validation layer that quietly misses a category is worse than none — it manufactures false confidence.

• Judgement claims are out of scope. Whether advice is good, whether a summary is complete, whether a conclusion follows. Those need an LLM-as-judge layer. This is not one.
• Identifiers need a vocabulary. No lexical rule separates a part number from a standards name — RS485 and BME280 are the same shape. A default vocabulary of known non-identifiers ships in, and is meant to be extended.
• Amounts written as words ("thirty-six dollars") aren't caught. Symbol and suffix forms are: $36, 36 dollars, 36 USD.
• Relative dates are out of scope ("next Tuesday", "in 30 days") — those depend on what now means, which makes them judgement claims.

Your own data

A domain adapter turns your data into a GroundTruth. The core never learns what your data is — two ship as reference implementations, and a third is a new file, not a change to the verifier.

from helix_grounding.domains.invoice import ground_truth_for_invoice

report = Verifier().verify(summary, ground_truth_for_invoice(invoice))

Invoices exercise a shape a BOM never does: a chain, where line totals feed a subtotal, the subtotal feeds a discount, the remainder feeds a tax. Every intermediate is a figure a model will quote, so the adapter permits the whole working — not just the answer.

Adding that second domain is what forced date support into the core. Before it, a fabricated due date produced no claim at all and passed straight through.

Zero runtime dependencies, deliberately. The argument for this library is that checking a model's output shouldn't require another model. A dependency on an inference client would undercut that.

Development

Python 3.10+.

pip install -e ".[dev]"
pytest

161 tests, nothing skipped, no database and no services required.

src/helix_grounding/   the library
    domains/           bom.py, invoice.py — add a vertical here
src/helix_bom/         the worked example: ingest, checks, CLI
src/helix_llm/         optional model client (local Ollama, or Anthropic)
docs/                  decision log, architecture, business model, case study
                       PUBLISHING.md — GitHub + PyPI release checklist
                       FIRST_USERS.md — how to get the first users

docs/DECISION_LOG.md is 45 decisions with the reasoning attached, including the bugs that produced the design above. It is the most useful file here for understanding why rather than what.

Licence

MIT.