SAMDAMMission Partners
How it works · Commercial

How Sourcine produces source-governed AI outputs.

Sourcine treats the model as an untrusted component. Facts come from your approved records; the model may improve wording, never author a fact, number, citation, or contract term. Here is the pipeline every governed output passes through — and why each step exists.

The pipeline

Six steps from approved source to defensible output.

The order matters: facts are fixed before the model is ever invoked, and nothing unsupported leaves the pipeline by default.

1

Bind to authorized sources

Before anything runs, the facts the output needs are retrieved from your approved records — systems of record, answer libraries, policy documents, contract repositories. The model has no say in what is true.

Why: AI can only work from information you have already approved — not the open web or model memory.
2

Assemble deterministically

Critical facts, identifiers, and required fields are placed into the output by software, directly from those sources. This is where names, numbers, dates, control statements, and contract terms come from — assembled, not generated.

Why: The fields that carry legal and contractual weight are never left to a probabilistic model.
3

Refine style — optional and constrained

Only after the facts are fixed does an LLM optionally smooth the wording. It runs behind a fail-closed gate, may change phrasing, and is barred from introducing new facts, entities, or figures. In many workflows this step is suppressed entirely.

Why: You get readable output without handing the model authorship of the facts.
4

Validate protected fields

The candidate output is checked back against its source. Protected fields — names, dates, IDs, numbers, citations, control statements, contract terms — must match exactly. Any drift is a failure, not a rounding error.

Why: A paraphrased control ID or altered figure is caught mechanically, before a human ever sees it.
5

Fail-closed gate

If validation fails, the output is blocked and routed for review — it is not published. The default state is “do not ship,” not “ship and hope.”

Why: Nothing unsupported reaches a customer, an auditor, or a contract without a human decision.
6

Audit commit

The accepted output is recorded with the source it used, the version of that source, and the validation result — a tamper-evident record you can produce on request.

Why: When legal, audit, or a customer asks “where did this come from,” the answer already exists.
Why deterministic-first

Retrieval reduces hallucination. It doesn’t eliminate it.

Retrieval-augmented generation still lets the model form the final answer — so a wrong or altered fact remains possible. Deterministic-first inverts the flow: the answer is composed from approved sources before any model runs, and the model is reduced to an optional, gated style pass. The difference is where authority lives.

Model-authored (plain LLM / RAG)
  • The model forms the final answer
  • Facts and phrasing are entangled
  • Hallucination is reduced, not prevented
  • Provenance is reconstructed after the fact, if at all
Source-governed (Sourcine)
  • Facts are assembled from approved sources first
  • The model refines style only, behind a gate
  • Unsupported output is blocked, not published
  • Source and validation are recorded by construction
Start here

See it on one of your workflows.

We scope a single high-value workflow against an approved source set — you see governed output, protected-field validation, and an audit trail before committing to more.