Using AI for Grant Research, Drafting, and Project Readiness

How Research Turns Into a First Draft for Cities and Counties

For cities and counties, the most time-consuming part of the grant process often comes after an opportunity is selected.

Once a NOFO is understood, grant teams must gather data, validate community need, align projects with funder priorities, and translate all of that into a clear, credible narrative. This research and project readiness phase is where many applications slow down, especially when staff capacity is limited and expectations for data, equity, and outcomes continue to rise.

Much of this work happens before a single paragraph is written. Teams are assembling background materials, tracking down statistics, and trying to connect local plans to funder priorities.

As more local governments use AI at this stage, teams are finding they can move from research to a usable first draft more efficiently.

How Local Governments Use Free AI Tools for Grant Research

Most cities and counties begin with tools they already have.

Grant staff commonly use general-purpose AI tools to summarize background documents, explain technical reports in plain language, and answer questions about grant programs or policy goals. AI-powered search tools are often used to surface statistics from public sources such as the Census, Department of Transportation, EPA, HUD, or USDA.

A typical workflow looks like this:

• Manually gather reports, plans, and datasets
• Paste excerpts into an AI tool
• Ask for summaries, explanations, or rewritten text
• Copy outputs into Word documents, spreadsheets, or notes

Teams also use free tools to brainstorm alignment with funder priorities. They may describe a proposed project and ask how it connects to goals like safety, resilience, or equity.

As teams move closer to writing, free tools are sometimes used to draft rough paragraphs based on assembled research. These drafts are rarely final, but they help teams get started.

Where Free Tools Help and Where Extra Care Is Required

Free tools are helpful for speed and orientation.

They reduce the time it takes to understand unfamiliar subject matter, translate dense reports, and assemble early background notes. For lean grant teams, that time savings is meaningful.

At the same time, teams are careful about how outputs are used.

General-purpose tools can occasionally introduce inaccuracies, overgeneralize data, or fill gaps with plausible-sounding language when inputs are incomplete. Grant teams typically mitigate this by treating AI outputs as working notes, not final content, and by verifying statistics and claims against original sources.

Most of the effort still happens between research and writing.

Research outputs live across prompts, documents, and folders. Context must be reintroduced repeatedly. Local plans, public data, and funder priorities are connected manually.

As a result, free tools often accelerate research without reducing total effort. Time saved early is spent later organizing, validating, and reshaping content into a coherent draft.

Turning Structured Research Into Drafts and Readiness Signals

Grant-native platforms approach this stage by treating research, drafting, and readiness as part of the same workflow.

Instead of working from disconnected prompts, the system maintains context about the jurisdiction, the project, and the funding opportunity over time. Uploaded documents, public data, and grant requirements inform writing together, rather than being stitched together later.

Avila supports this by working directly from:

• Local plans, studies, and prior grant materials
• Public web and government data
• An internal government knowledge base with census-tract-level statistics

Because research is grounded in known sources, outputs stay tied to underlying documents and data. This reduces the risk of fabricated details and makes it easier for teams to trace claims back to original inputs.

Turning Drafting Into an Extension of the Research Itself

Required sections are defined. Character limits are enforced. Draft content is generated directly from the research, documents, and grant requirements already in the system.

Because drafts are constrained by source material and requirements, unsupported claims surface quickly. Gaps in data, unclear scopes, or weak alignment are visible before teams are deep into writing.

Teams can see:

• Where supporting data is still needed
• How well a project aligns with scoring criteria
• Which sections require additional departmental input

This allows grant teams to adjust early and engage departments more effectively, rather than discovering issues late in the process.

Reducing Rework Without Replacing Judgment

At this stage, the goal of AI is not to replace expertise.

It is to reduce rework while preserving accuracy.

Free tools help teams move faster through research and early drafting when used carefully. Grant-native platforms help ensure that work stays grounded, traceable, and aligned as it moves toward submission.

When research feeds directly into constrained drafts and readiness checks, teams spend less time correcting errors and more time strengthening competitive applications.

In the next post, we will look at how this work connects to pre-award coordination, timelines, and internal grant management, and how AI supports collaboration across departments as submissions come together.