u/Sam-ZenGrants

Following on from my previous posts in here about trying to build an AI grant writing assistant that doesn't output generic corporate fluff... I wanted to start by saying a massive thank you to everyone in this community who joined our private beta and sent over feedback. Your input over the last months genuinely shaped the entire platform, and we couldn't have built this without you!

When Innovate UK announced their big overhaul to how grants work, rumors started floating around about shifts toward interview-led scoring and revamped assessment models. As someone who has spent the last year building and tweaking software specifically aligned with Innovate UK’s evaluation frameworks, I genuinely thought that months of development work was about to be rendered obsolete.

The irony of Innovate UK unintentially destroying a fledgling startup is not lost on me! But luckily that reality never materialised.

I spent the last few days doing a deep dive into the new assessor guidance for the newly launched competitions (like Breakthrough Next Wave) to see what had actually changed compared to the old SMART format.

The good news? It’s mostly business as usual. The core underlying requirements haven't disappeared. But there are definitely a few interesting shifts in how they are evaluating projects now:

1. A distinctly "VC-Style" focus on the team
Under the old SMART format, team details were lumped together into a broad delivery question alongside project management and risk registers. In the new format, Innovate UK has elevated "Team and Resources" into its own standalone 10-point scored question. And, for the first time, they require a dedicated 2-page PDF appendix exclusively summarising the key people. It feels much more like a VC pitch deck team slide, where who is building the project is given equal weighting to what is being built.

2. Deconstruction into 10 questions (from 6)
Instead of 6 broad questions, the application is now split into 10 distinct sections. For example, "Potential Market" is now split into two independent sections: Market Awareness (drivers & UK position) vs Outcomes & Route to Market (business models & commercialisation). Each section requires much more specific, targeted data rather than high-level summaries.

3. Work Package costing & subcontractor scrutiny
Project management and risks now have their own dedicated sections and individual 2-page appendices (GANTT chart and Risk Register). On top of that, applicants now have to explicitly break down the exact financial cost of every single work package and provide heavy justification for why subcontractors are critical rather than doing work in-house. ZenGrant scoring rubric already required these details, but it's interesting to see them specified in the question now.

4. A sneaky 43% increase in total written content
While individual questions are now capped at 400 words each (under the old SMART calls, major sections like Innovation or Impact allowed up to 600 words), the new 10-question layout actually increases the total narrative length significantly. The old SMART scored sections totaled 2,800 words; the new layout requires 4,000 words. You are writing 1,200 more words in total, but you have less room in each section to explain complex technical ideas, forcing extremely dense and concise writing. Oh and if you actually want to find the word counts, you need to begin an application in the portal. Why aren't they listed on the call pages and docs?!

Combining your beta feedback with these new Innovate UK evaluation frameworks gave us the kick up the backside we needed to rebuild our core scoring engine and officially launch. ZenGrants is now live as a full platform, with features designed specifically for these stricter rules:

• Strict assessor rubric: The Discovery Assistant evaluates your inputs through the lens of a strict Innovate UK assessor, starting from a critical baseline and looking specifically for concrete data, quantified targets, and risk management before giving a pass mark.
• Transparent score deductions: Instead of just giving a black-box percentage, it shows you a breakdown of what gaps and weaknesses exist in your current draft so you know precisely what's lacking.
• Defer gaps to Deep Research: If a question requires heavy market analysis or details you do not have on hand, you can now defer it directly to the Deep Research stage. This waives the warning in Discovery so you can focus on your core plan.

We have officially transitioned out of private beta and launched ZenGrants as a paid platform! But as a huge thank you to this community for supporting the build from day one, I want to give back.

If you are currently writing or preparing an Innovate UK grant and want to test out the new and improved engine, send me a DM and I’ll activate a full 2-week free trial for your account.

Thanks again to everyone who helped us get to this point, and good luck with the new grant rounds!

A few months ago, I was so drained by the manual process of writing government grants (specifically Innovate UK grants, similar to Horizon (EU) or SBIR (US)) that I decided to build a tool to fix it. When you look at the stats - 97% rejection rates and grant consultants charging £10k-£15k+ per application - it's a massive pain point for founders.

I always knew getting the output quality right was going to be the hardest part, but actually forcing an LLM to consistently output high-level grant text was much harder than I anticipated. Getting an engine to understand the difference between a strong, evidence-backed 'Value Proposition' and meaningless corporate jargon required a lot of tweaking.

One of the biggest technical hurdles was managing the context to ensure that the AI is deeply grounded in the company's actual data.

I experimented with a few approaches: standard RAG, GraphRAG, and a purely file-centric context approach.

I ended up completely ditching RAG and settling on the file-centric context, and it's given the best results by far. With the massive context windows we have now, and given that users are usually only providing a specific set of project documents (whitepapers, tech specs, early drafts), RAG just seemed completely unnecessary. In fact, chunking and vector retrieval actively degraded the coherence of the final narrative and increase latency quite a bit.

Instead, ZenGrants guides the user through a structured 5-phase workflow (Setup, Gathering, Discovery, Research, Drafting). We extract the exact metrics and strategy upfront, and then feed the whole files (excluding some things) into the context window with clear guardrails. The output is now consultant-grade, and it actually sounds human.

Right now, we are running a private beta specifically focused on the UK market, but I'm looking to expand the engine to handle EU and US government grants soon once we've nailed down the core architecture.

My question for the builders here:
For those of you building AI tools for complex documents (legal, medical, government), how are you handling context windows? Would you have chosen a file-centric approach too, or would you have gone for a RAG-style approach?

Is RAG overhyped/dead now that context windows are so large? Being a bit provocative here as I know RAG isn't DEAD and is still useful and necessary in a lot of cases.

(P.S. If you want to see the workflow in action or check out what I'm building, it's at zengrants.co)

A few months ago, I posted in here about how writing Innovate UK grants genuinely drained my soul, and that I was trying to build a tool to fix the process (original post here). A lot has changed since then — including Innovate UK restructuring their approach to funding and indefinitely pausing SMART grants, which may put a dent in my subscriber base!

But back to the development of ZenGrants. I always knew getting the quality right was going to be the hardest part, but actually forcing an LLM to consistently output high-level grant text was more work than I anticipated. Building a basic AI wrapper is easy (relatively), but getting a drafting engine to actually understand the difference between a strong, evidence-backed 'Value Proposition' and meaningless corporate jargon required a lot of tweaking.

If you've ever assessed a grant, you know that generic fluff is the quickest way to get a low score. The key to using AI effectively here is to avoid giving it too much freedom; instead, you have to define clear guardrails by providing structured, detailed context that keeps the model focused on the specific requirements of the grant.

To tackle this, I've ended up building a 5-phase workflow into ZenGrants that acts more like a consultant than a text generator:

1. Setup: We extract the exact questions and headline requirements for your specific competition.
2. Gathering: We collect your project documentation (whitepapers, tech specs, early drafts) and writing samples to ensure the output actually sounds like your voice.
3. Discovery: This is the most important bit — teasing out the exact metrics, commercial strategy, and impact figures. The user is guided through this just like they would be in a discovery session with a grant writer.
4. Research: An agent conducts in-depth research into all aspects of your project based on a template that you approve.
5. Drafting: This is where the actual writing happens, including a narrative plan that ensures all the questions and appendices link together into one cohesive story.

I'm genuinely very happy with the output of the drafting engine now. It manages the strict word counts, ensures a cohesive narrative, and most importantly, it produces drafts that don't sound like a robot wrote them.

ZenGrants isn't officially launched yet, but I am running a private beta for the next few weeks. I'm looking for a small group of founders who are actively writing an Innovate UK grant (or planning to start one soon) to stress-test the system and give me some honest feedback.

If ZenGrants sounds like it might be useful for your next application, you can sign up at ZenGrants and I'll manually activate your account so you can get started.