A requirements-driven secure delivery and runtime assurance platform for mission environments. Facts are bound to authorized sources before any model runs; the model may refine style — never author a fact, citation, or identifier.
Critical systems are scanned for vulnerabilities monthly, as required by RA-5(2) and defined in the authorized SSP §3.11.
Sourcine starts from approved requirements, so the delivered system doesn’t have to be AI-enabled. The same gated discipline applies whether or not a model is ever invoked.
Dashboards, workflow automation, data pipelines, and APIs delivered through Conductor’s gated architecture, build, validation, and release — with audit-ready evidence.
Model-assisted retrieval and synthesis governed by Veritas: deterministic resolution, citation enforcement, fact/synthesis labeling, and audit commit before display.
Deterministic application logic handles core workflows; Veritas governs only the AI-assisted portions that must be source-attributed and audit-defensible.
Runtime principle. Facts originate from governed source systems, deterministic selectors, compilers, validators, and audit records. The model is an untrusted synthesis component inside a controlled workflow — useful for explanation, never the authority of record.
The system attempts structured lookup, identifier binding, and source-backed assembly before any LLM call. The model is invoked only when synthesis is necessary — and only after context and authorization are validated.
Facts originate from approved systems, structured catalogs, policy repositories, and lineage-tagged records.
Queries bind to records through explicit IDs, controlled vocabularies, role-aware filters, and stable retrieval rules.
Structured answers and protected fields are assembled before any LLM call.
The LLM is invoked only for synthesis or explanation over already-grounded context.
Output is checked for citation completeness, groundedness, protected-field preservation, and boundary violations.
Final disposition is committed with query hash, context package, model/version metadata, citations, and gate results.
Each stage produces the artifact the next consumes. Skip a stage and the chain that makes the final system auditable breaks.
Converts plain-language mission needs into structured, reviewable engineering and compliance artifacts — approved before any build begins.
Executes six sequential phases, each closed by a named gate with explicit pass/fail criteria. A failed gate blocks progression.
Deterministic-first runtime governing AI-assisted output inside delivered systems, via a non-bypassable G0–G5 sequence.
Security and compliance defects surface at design time — when remediation is cheapest. A build that fails a gate cannot proceed until corrected or formally dispositioned.
Extract trust boundaries, sensitive data flows, and P0/P1 failure modes before code.
Map requirements to components, controls, and tests; assign IDs that persist downstream.
Register risks, owners, dependencies, and blockers. No silent TBDs.
Load STIG/SRG, CIS, cloud policy, and compliance criteria as mandatory gate inputs.
Deterministic tooling plus human-approved AI assistance build under gate constraints.
Validate controls, vulnerabilities, evidence, and operational acceptance before release.
Each gate produces both a pass/fail disposition and a machine-readable trace record — for every output channel carrying in-scope AI-assisted content.
Sanitize queries; primary prompt-injection boundary.
Resolve entities against authoritative reference data.
Query approved sources; tag every result with origin.
Unsupported claims trigger block, re-query, or clarify.
Separate retrieved fact from AI-generated synthesis.
Write the full chain to a tamper-evident log before display.
Passed gates; shown with citations and audit metadata.
Insufficient citation or boundary crossed; not shown.
Shown with review status, per customer policy.
System asks the user to constrain the query.
Applicable requirements are ingested, traced to deployed-service configuration, and evidenced — not maintained as a separate parallel paperwork track.
Controls mapped to gate criteria, cloud baseline policies, access controls, audit logging, and evidence artifacts.
Traceability, baseline, validation, release, and residual-risk evidence accumulate throughout delivery.
Approved regions, service baselines, encryption, and boundary controls aligned to your impact level.
Hardening requirements translated into configuration tasks, validation tests, and exception workflows.
The same deterministic-first discipline, productized for real markets.
Extracts every requirement from a federal solicitation, grounds each response in supplied evidence, verifies compliance against FAR, DFARS, NIST & CMMC, and produces an audit package with full requirement traceability and provenance.
Turns unstructured documents — PDF, XLSX, CSV, and images via OCR — into machine-readable JSON through a deterministic extraction core, with optional AI enhancement: the same deterministic-first discipline, applied to data capture.
Turns plain-language mission needs into structured, reviewable BRD / PRD / SRS / TDD and a candidate compliance framework — with traceable requirement IDs, before any build begins.
A practical entry point for each stage of adoption — evaluation, pilot, production, or authorization support.
Evaluate mission, data, governance, source readiness, and AI/non-AI assurance requirements.
Demonstrate Conductor delivery controls — and Veritas controls if AI is in scope — against an approved source set.
Implement the full governed application, analytics capability, or hybrid system inside your boundary.
Support security review, ATO workstreams, oversight, or compliance evaluation.
Source attribution is not source verification. A cited, gated output traces which authorized source supported a claim; it does not verify that the source is current, correct, or complete. Source quality assurance remains a customer governance responsibility.
Gate completion is not a vulnerability-free guarantee. It evidences that defined architecture, control, test, scan, review, and release criteria were executed. Assurance depends on the quality of the customer-approved gate criteria.
Veritas governs only mediated AI paths. Any output channel carrying in-scope AI-assisted content must route through the G0–G5 sequence, or be covered by a documented, customer-approved equivalent control.
Start with an assessment: we characterize your mission, data sources, query profile, and assurance requirements — then scope a pilot against an approved source set.