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
Informed by the AI navigator chatbot and AI-summarized reporting OlenArc operates for ASCF. The full document pipeline shown on this page is a reference design — it has not been deployed in full for any client.
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1 · Document ingestion
Drop folder, API endpoint, or upload portal. PDF / DOCX / scanned. OCR fallback. Virus scanning included.
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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 solicitation shapes
These are solicitation shapes this reference design may be scoped against — forms we’ve seen from foundation grant operations, federal civilian grants offices, state agencies, 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.”
Possible scoped output: Ingestion · LLM extraction · review queue · role-based dashboard · audit log.
Reference: ASCF grant platform →
Automated program narrative drafting
RFP language: “AI-assisted narrative drafting for quarterly program reports, with source citations and staff sign-off workflow.”
Possible scoped output: 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.”
Possible scoped output: Sub-recipient roll-up · SF-PPR / SF-425 narrative draft · signed-off submission packet.
Section 508 document processing
RFP language: “Document processing pipeline producing 508 / WCAG 2.1 AA conformant outputs for agency external publication.”
Possible scoped output: Pipeline + accessible output formats (tagged PDF / accessible HTML); accessibility testing incorporated into delivery.
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 handling requirements are confirmed per engagement in the SOW.
- Data residency & retention — Hosting environment and data-residency requirements are confirmed per engagement in the SOW; retention and destruction per client policy.
- No model training on client data by default unless client explicitly opts in.
What is deployed today — and what is not
Arctic Slope Community Foundation
Deployed client engagement: AI navigator chatbot (community-tuned RAG over a local knowledge base) and an AI-summarized impact reporting layer, in production across all 8 North Slope Iñupiat villages under an ongoing operating engagement. Not deployed: the full OCR → extraction → dashboard → narrative pipeline shown above is a reference design.
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.