Document-to-Dashboard Workflow
Turn grant reports, applications, PDFs, and program records into structured review queues, dashboards, and human-reviewed narrative drafts. AI-assisted — not autonomous. Program staff stay in the decision loop.
The 4-step pipeline
Same pattern OlenArc deployed for ASCF grant operations, reused per program.
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1 · Document ingestion
Drop folder, API endpoint, or upload portal. PDF / DOCX / scanned. OCR fallback. Virus scanning + CUI marking propagated through the pipeline.
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2 · LLM-assisted field extraction
Structured field extraction tuned to your program’s schema. Confidence scoring. Low-confidence fields route to manual review queue.
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3 · Role-based dashboard rollup
Program-staff view with role-based access. Side-by-side document + extracted-fields view. Edit-and-approve workflow with audit log.
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4 · AI-drafted narrative review
Narrative generation using approved extractions as RAG context. Reviewed and signed off by program staff before any external use. No autonomous publishing.
If we built it for your program
The point isn’t the AI — it’s that your team spends two weeks reviewing instead of two months re-keying. Three working surfaces a sprint actually leaves you with.
Paper, email, scanned PDF — one queue your team can actually work
Right now documents land in ten different inboxes, a shared drive, and a printer tray. Replace that with one queue: drop it in, get a status, see when it’s ready for review. Your staff stops being a router.
- One queue replaces ten inboxes — email, Drive, scanned paper, fax-to-PDF all land in the same place
- Per-document status — Processing · Needs review · Approved — no “did anyone do anything with this?”
- Audit trail from the moment a document lands — IG-ready by default, not by rebuild
See what the AI saw — and change what it got wrong
Document on the left, extracted fields on the right with confidence scores. Anything under 80% is automatically flagged for human review. Staff approves what’s right and fixes what isn’t — nobody re-keys 22 pages of PDF from scratch.
- Confidence-scored — low-confidence flags are explicit, not vibes
- Edit-and-approve instead of re-key from scratch — the AI does the prep, the human makes the call
- Audit log of every field change — for IG / CPARS / state monitor purposes
First draft of your narrative — with the citations already attached
AI drafts the program narrative from the approved extractions. Every paragraph carries a source citation linked back to a specific document and page. Staff signs off; nothing leaves your system without a human read. The end-of-quarter all-nighter goes away.
- Every paragraph cited — audit-ready by default, not by post-hoc rebuild
- Human sign-off mandatory — no autonomous publishing, ever
- SF-PPR / SF-425 shape — drops into the federal reporting templates you already file
Common RFP shapes we respond to
If your procurement language looks like this, we ship that. Each shape below is a real RFP form we’ve seen — from foundation grant operations, federal civilian grants offices, state HHS, or a prime BD pulling a capture brief together.
AI-enabled grant application processing
RFP language: “Automated grant application intake and review pipeline with LLM-assisted field extraction and reviewer dashboard.”
What we ship: Ingestion · LLM extraction · review queue · role-based dashboard · audit log.
Reference: ASCF grant platform →
Document-to-data pipeline · NIST 800-171
RFP language: “Document-to-data extraction pipeline with CUI handling, NIST 800-171 alignment, audit log, and accuracy benchmarks per field type.”
What we ship: Pipeline + GovCloud-inheritance posture + per-field accuracy benchmark + auditor-friendly logs.
Maps to: Compliance posture →
Automated program narrative drafting
RFP language: “AI-assisted narrative drafting for quarterly program reports, with source citations and staff sign-off workflow.”
What we ship: RAG over approved extractions · AI draft · inline source citations · staff sign-off workflow.
Reference: Reference design →
Federal grant quarterly reporting automation
RFP language: “Automate SF-PPR / SF-425 narrative generation across [N] sub-recipient grantees with human sign-off before submission.”
What we ship: Sub-recipient roll-up · SF-PPR / SF-425 narrative draft · signed-off submission packet.
FedRAMP-inheritance AI pilot
RFP language: “3–6 month pilot for AI document processing on FedRAMP-Moderate inherited cloud, with accuracy benchmark and procurement-ready spec.”
What we ship: Pilot scope + AWS GovCloud / Azure Gov deploy + numeric accuracy benchmark + spec for follow-on production.
Section 508 document processing
RFP language: “Document processing pipeline producing 508 / WCAG 2.1 AA conformant outputs for agency external publication.”
What we ship: Pipeline + accessible output formats (tagged PDF / accessible HTML) + 508 conformance test report.
Risk discipline we bake in
- Human review at every stage — extraction, classification, narrative, publication. No autonomous decision-making on program data.
- Hallucination testing — representative document set tested per engagement; accuracy benchmarks documented.
- Privacy & consent — explicit consent capture in intake flows; PHI / PII handled per NIST 800-171 / HIPAA BAA where applicable.
- Data residency & retention — AWS GovCloud or Azure Government when CUI handling required. Retention + destruction per client policy.
- No model training on client data by default unless client explicitly opts in.
Deployed proof
Arctic Slope Community Foundation
AI navigator chatbot (community-tuned RAG over local knowledge base) + AI-summarized impact reporting layer. Deployed in production across all 8 North Slope Iñupiat villages. Multi-year operating agreement.
AI Document-to-Dashboard reference design
Capture-phase reference design (not deployed) modeling the 4-step pipeline for civilian-agency grant operations. Use it as a scoping starting point for a Tribal, state, foundation, or federal-sub engagement.
Have a document-heavy program workflow?
Send us a representative document set. We'll come back with extraction accuracy estimates, scoping options, and a pilot proposal.