Free desktop software · Local AI · v2.2.86

Funding intelligence — and a tiny local AI — that lives on your machine.

TIGER Funding Radar brings current funding opportunities, faculty evidence, NSF/NIH awards, LSU SPA analysis, historical funded-project discovery, and built-in on-device AI for research-idea matching into one focused local workspace.

✓ Local-first workflow ✓ Windows · macOS · Linux ✓ No SPA workbook bundled ✓ On-device MiniLM AI for Idea Match
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TIGER Funding Radar dashboard
One radar. Multiple funding signals.Opportunities · evidence · awards · matching · discovery
v2.2.86
Product walkthrough

See TIGER in action.

A guided walkthrough of the real v2.2.86 interface, including readiness states, opportunity workflows, sponsor awards, SPA analysis, Idea Match, Discovery, and matching.

Created by Ibrahim (Abe) Baggili. The demonstration is packaged locally with this site.Approx. 5 minutes
What it does

From opportunity discovery to evidence-backed matching.

TIGER is designed around the real decisions research leaders and faculty make when identifying calls, understanding historical funding, and building credible teams.

Opportunity radar

Refresh and filter current calls by sponsor, department, deadline, award size, priority, tags, and other criteria.

Faculty evidence

Maintain a local roster and update scholarly evidence independently through supported scholarly sources such as OpenAlex and Crossref.

NSF & NIH awards

Analyze sponsor-side funded-project history separately from institutional SPA accounting and compare institutions, colleges, or departments.

LSU SPA intelligence

Import an authorized SPA workbook locally, validate it, compare colleges and units, and inspect fiscal-year, sponsor, and credited-funding patterns.

AI Idea Match & Discovery

Use TIGER's built-in neural MiniLM model to compare an abstract or research concept against current opportunities, then explore a local historical NSF/NIH discovery index.

Evidence Fit matching

Combine scientific evidence with bounded historical-funding signals, de-duplicate award evidence, and identify Best Fit and Novel Bridge teams.

TIGER opportunity filters
Search and filter funding callsUse the opportunity workspace to narrow the radar around the research question that matters.
TIGER SPA comparison view
Compare SPA colleges and unitsExplore cumulative, new, and continuing credited funding without mixing sponsor-side award data.
TIGER Idea Match interface
Match ideas to opportunitiesUse the local MiniLM workflow to analyze an abstract or specific aims against current opportunities.
TIGER readiness dashboard
Know when TIGER is readyReadiness indicators make it clear when opportunity, faculty, and funded-project prerequisites are usable.
Tiny AI inside TIGER

Small model. Focused job. Local by design.

Idea Match uses TIGER MiniLM-L6-v2 INT8, a compact neural embedding model that runs locally in the browser inside the TIGER desktop app. After a one-time model setup, TIGER can reuse the local model without an AI API key.

Multi-view semantic matchingTIGER analyzes the whole idea, its scientific core and methods, and program identity instead of relying on a single keyword score.
Full open-catalog coverageThe built-in neural model supplies semantic similarity across the current open opportunity catalog before TIGER applies deeper funding checks.
AI plus explicit evidenceNeural similarity is combined with scientific-objective, domain, program-intent, and funding-mechanism checks so the result is not just an opaque AI score.
Purpose-built, not generativeThe local AI is used to help rank semantic fit between a research idea and funding opportunities. Proposal writing, award prediction, and final funding judgments remain outside this model's role.
What this AI is — and is not.It helps rank idea-to-opportunity fit. It does not write your proposal, does not predict whether an award will be funded, and does not replace the evidence and mechanism checks built into TIGER. In built-in mode, the pasted research idea is not sent to an AI provider.
Built-in Neural AI
TIGER Idea Match with built-in local neural AI
Local MiniLMNo AI API key required for built-in Idea Match
Core workflow

A repeatable funding-intelligence loop.

TIGER separates data preparation from matching so you can see exactly what is ready before generating results.

01
Refresh opportunities

Bring current calls into the local opportunity workspace.

02
Verify faculty evidence

Maintain the roster and update scholarly evidence.

03
Refresh NSF / NIH

Update sponsor-side funded-project history.

04
Import SPA

Optionally import an authorized LSU SPA workbook.

05
Perform matching

Run when the required readiness states are satisfied.

06
Analyze & discover

Review opportunities, teams, comparisons, and ideas.

Download

Choose the build for your computer.

Version 2.2.86 is free to use under the TIGER Free Use License below. The packages contain no source code and no LSU SPA Excel workbook.

Recommended

Windows

Windows x86-64 · GUI + diagnostic build
16.6 MB · unsigned executable
Download Windows
Recommended

macOS · Apple Silicon

For Apple M-series Macs
16.7 MB · unsigned / not notarized
Download Apple Silicon
Recommended

macOS · Intel

For Intel-based Macs
17.4 MB · unsigned / not notarized
Download Intel Mac
Recommended

Linux

Linux x86-64 · portable local app
9.0 MB · run with included shell launcher
Download Linux
Need more than one platform?

Download any combination of the four platform packages above. Each package already includes the latest user manual.

Cloudflare Pages-ready
SPA data is intentionally not bundled. If you use the LSU SPA module, import an authorized workbook manually from inside TIGER. Never distribute restricted institutional data inside a public software package.
SHA-256 checksums
Windows5eceaeba17e3d39fdc1ba9b47121dc29ddaf0df7710780832300f0c0e942525c
Linux48e277545f43fcbcf147c84d5aa45b6e267c76c3a213be23d4aef55460c6208c
macOS Apple Silicon7a4a9432f4cb2946fe310a912d90d0ecb635c790fe84a518f31e1cfa9aafdd3a
macOS Intel298ba60a730923f112fafe9d150cb4d80ccaf76958570a67285fc96fe758ff82
Scholarly evidence setup

Connect OpenAlex with a free API key.

TIGER can use OpenAlex to strengthen faculty identity validation and scholarly evidence. OpenAlex says creating an account is free, the API key is available from your account settings, and the free tier currently includes daily API usage without requiring a payment method.

OpenAlex

Get your free key in about a minute.

  1. Create a free OpenAlex account.Register at openalex.org.
  2. Open API settings.After signing in, visit openalex.org/settings/api.
  3. Copy the key into TIGER.Faculty → Optional OpenAlex API key → Save OpenAlex key.
  4. Refresh scholarly evidence.Choose OpenAlex or Both when you want OpenAlex included.

Why TIGER uses it

OpenAlex gives TIGER another evidence source for publication history, topics, and researcher identity. It complements Crossref; it does not replace your local faculty roster.

Keep the key private.TIGER stores the saved key locally. Do not include it in screenshots, support tickets, repositories, or public exports.
Read OpenAlex authentication guidance →
Documentation

The full v2.2.86 user manual.

Installation, first-run setup, opportunities, faculty evidence, NSF/NIH awards, SPA import and accounting, comparisons, Idea Match, Discovery, matching, source health, privacy, and troubleshooting.

19-page illustrated guide

The current manual is included with every platform package and is also available separately here.

Download the manual

Your browser cannot preview the PDF. Open the manual.

Frequently asked questions

Quick answers before you start.

The most common setup, privacy, readiness, and workflow questions are also included in the updated user manual.

What AI does TIGER use for Idea Match?

TIGER uses its built-in TIGER MiniLM-L6-v2 INT8 neural model. It runs locally in the browser inside the desktop app, creates multi-view semantic representations of the idea and the open opportunity catalog, and combines that signal with TIGER's explicit scientific, program-intent, domain, and funding-mechanism checks. The built-in mode requires no AI API key; after the one-time model setup, the local model is reused. The pasted abstract is not sent to an AI provider in this mode.

Do I need an OpenAlex API key?

Only if you want to use OpenAlex evidence. Create a free OpenAlex account, copy the key from OpenAlex API settings, and save it in the Faculty tab. Crossref can be used independently.

When does the Faculty readiness light turn green?

After a usable scholarly-evidence refresh completes for the loaded roster. Having names in the roster alone is not enough; the scholarly-evidence prerequisite must be established.

When does Perform Matching become available?

When Opportunities, Faculty Verification, and Funded Projects are all usable and TIGER detects that matching is due because relevant data changed since the last run.

Does Evidence Fit predict whether a proposal will be funded?

No. Evidence Fit is an evidence-alignment score, not a probability of award. It explains alignment based on the evidence TIGER has loaded.

Is an LSU SPA workbook included?

No. Public distributions intentionally include no SPA Excel workbook. Authorized users import their institutional workbook manually.

Does Idea Match send my full research idea to the cloud?

The built-in local MiniLM workflow is designed to keep the full idea on your machine. Review any optional external configuration before entering confidential or restricted information.

Where does TIGER store its data?

TIGER uses a portable data directory beside the executable or application bundle. Back up that directory if you need to preserve imports, local state, keys, and matching history.

Can a research office use TIGER?

Yes. TIGER is intended for both faculty and research-development or research-administration teams that need a transparent workflow for opportunities, evidence, historical awards, institutional comparisons, and team discovery.

Ibrahim (Abe) Baggili
Ibrahim (Abe) Baggili, Ph.D.Creator of TIGER Funding Radar
About the creator

Built from a problem I kept seeing in academic research.

Division Chair & Roger Richardson Professor, Computer Science & Engineering, Louisiana State University.

Finding the right funding opportunity is one of the most important parts of academic research, but it is also a surprisingly difficult problem. Faculty may have strong ideas and strong teams, yet the funding landscape is fragmented across sponsors, solicitations, deadlines, historical awards, institutional data, and scholarly profiles.

As an academic administrator, I found this challenge repeating itself for both faculty and the research offices trying to support them. I created TIGER Funding Radar to make that process more transparent, evidence-based, and usable: help faculty locate opportunities that fit their work, understand why they fit, discover potential collaborators, and give research offices a clearer view of the funding landscape.

TIGER reflects the way I think research development should work: combine good data, local context, explainable evidence, and tools that make people faster without hiding the reasoning.

Digital ForensicsCybersecurityAI / ML ForensicsResearch StrategyAcademic LeadershipBiT Lab
Policies

Free to use. Clear about privacy.

TIGER is distributed as a local desktop application. The policies below explain the software’s privacy model and the rights granted for free use.