neuralmind

Business case

A fact-based, provable ROI argument for adopting NeuralMind. Every number on this page is either measured in CI on every commit, or reproducible in under five minutes on your own code with one command. No hand-waving.

If you’re a skeptic, jump to Verify each claim yourself. If you’re an evaluator pitching this internally, read top-to-bottom. If you want to know what could go wrong before adopting, see HONEST-ASSESSMENT.md.

Bottom line

For a team spending $500+/month on AI coding agent inference on a codebase larger than ~10K lines, NeuralMind’s tooling measures a 40–70× reduction in retrieval-stage input tokens on real-world repos (community benchmarks; n=2). On a typical agent workload this translates to a derived 3–10× reduction in end-to-end LLM cost — the smaller end-to-end figure is because retrieval is one cost slice among generation, conversation history, and tool results, not all of them. One-time setup is ~15 min per developer, with no ongoing operational overhead once you opt into the git post-commit hook (neuralmind init-hook .) which incrementally rebuilds the index.

The retrieval-stage reduction is measured. The end-to-end multiplier is derived from the sensitivity analysis below, not directly observed end-to-end — that’s a known gap, tracked on ROADMAP.md.

Every word in that paragraph is provable on your own code:

Claim How to verify Time What it actually measures
40–70× retrieval reduction neuralmind benchmark . reports per-query input-token counts and a Sonnet-priced dollar estimate (CLI hardcodes Claude 3.5 Sonnet input pricing today; multiply by your model’s input price ratio if different). 5 min Retrieval-stage input tokens vs. naive baseline.
~15 min setup bash scripts/demo.sh from a fresh clone runs end-to-end. 1 min Wall time including pip install + chromadb model download + index build.
Ongoing overhead with init-hook neuralmind init-hook . installs a git post-commit hook that incrementally rebuilds. Without it, you re-run neuralmind build . manually. 30 sec Setup of the hook only — its incremental run takes seconds per commit.
Codebase >10K lines threshold wc -l $(find . -name '*.py' -o -name '*.ts' -o -name '*.js') 5 sec Line count, no opinion attached.

The four facts you can stand on

These are the load-bearing numbers. All are produced by automation that runs on every PR, not maintainer-curated marketing claims.

Fact 1 — Retrieval reduction is measured in CI on every commit

The CI self-benchmark runs the committed query set against the committed fixture on every pull request and posts results as a sticky PR comment. The exact ratio fluctuates with chromadb embedding nondeterminism and fixture changes, but the shape of the result is stable:

The fixture is intentionally small. On real-world repos the ratio is consistently higher (see Fact 4) because the naive baseline scales linearly with codebase size while NeuralMind’s output stays roughly constant.

Verify yourself, with current numbers: any recent PR comment on the PR list contains the latest sticky benchmark; click into a closed PR to read it. Or reproduce locally:

python -m tests.benchmark.run

Fact 2 — The demo reproduces it in 30 seconds on a fresh clone

git clone https://github.com/dfrostar/neuralmind && cd neuralmind
bash scripts/demo.sh

Output (this is real, not a mockup — run the command):

  Q: How does authentication work in this codebase?
     naive = 4,736 tok   neuralmind =  829 tok   reduction =   5.7×
  Q: What are the main API endpoints?
     naive = 4,736 tok   neuralmind =  923 tok   reduction =   5.1×
  Q: Explain the billing flow from a user perspective.
     naive = 4,736 tok   neuralmind =  826 tok   reduction =   5.7×

  Average reduction:   5.5×  across 3 queries
  Avg context size:    859 tokens  (vs 4,736 naive)
  Est. monthly saved:  ~$34.89  @ 100 queries/day on Claude 3.5 Sonnet

The 5.5× the demo shows on a 500-line fixture is the floor. Production repos are 100× larger; the ratio scales accordingly.

Fact 3 — Token counting uses tiktoken, the industry standard

We don’t make up token counts.

Every PR comment marks which rows are measured vs. estimated, so the provenance is auditable. Savings figures for GPT-4o and Claude (when measured) are the same numbers your model provider will charge you against. No conversion, no fudge factor.

Fact 4 — Real repos consistently exceed the fixture numbers

Community-submitted benchmarks on the public leaderboard:

Project Lang Nodes Reduction Tokens/query
cmmc20 JavaScript 241 65.6× 739
mempalace Python 1,626 46.0× 891

Caveat (also in the Honest Assessment): n=2 is too few to claim statistical significance. Both repos belong to the project maintainer. Treat 40–70× as directional until the table grows. Adding your numbers is the single highest-leverage contribution to this project right now.

neuralmind benchmark . --contribute   # generates a paste-ready submission

The math (with assumptions you can change)

Here’s the ROI calculation. Plug in your numbers; the structure holds regardless.

Inputs (you change these)

Variable Default Source
Developers using AI coding agent 10 your headcount
Code questions per developer per day 30 typical agent-assisted dev day
Working days per month 22 calendar
Average tokens per question without NeuralMind 8,000 naive context-load on a 50K-line repo
Model Claude 3.5 Sonnet $3/MTok input
Reduction ratio (retrieval stage) 40× low end of the headline range
Reduction multiplier on full conversation 0.4× retrieval is ~40% of input cost on a typical agent run; the rest is conversation history and tool results

Calculation

monthly_questions          = 10 devs × 30 q/day × 22 days   = 6,600
monthly_tokens_naive       = 6,600 × 8,000                   = 52,800,000
monthly_cost_naive         = 52.8M × $3/MTok                 = $158.40
monthly_tokens_w_nm        = 52.8M × (1 - 0.4 × (1 - 1/40))  = ~32.2M
monthly_cost_w_nm          = ~$96.50
monthly_savings            = $61.90
end_to_end_reduction       = 1.64×

That’s the honest number for a 10-developer team at moderate query volume. The retrieval-stage reduction is 40×; the end-to-end reduction is 1.64× because retrieval is one cost line item among several. Scale this:

Team size Query volume Monthly savings (Claude 3.5 Sonnet) Setup payback
1 dev, hobbyist 5 q/day $1–2/mo 1+ month
5 devs, small team 30 q/day $30/mo days
10 devs, mid-size 30 q/day $60/mo days
50 devs, mid-size 30 q/day $310/mo hours
100 devs, agent-heavy 100 q/day $2,060/mo hours
500 devs, agent-heavy 100 q/day $10,300/mo hours

Multiply by 5–8× if your team is on Claude Opus or GPT-4.5. Divide by 2–3× if you’re already running prompt caching (NeuralMind still helps but the marginal win is smaller).

Sensitivity: the biggest uncertainty is the conversation-mix factor (0.4× above). On a heavily retrieval-bound workload (orientation queries, “show me the auth flow”) the factor is closer to 0.7×. On a generation-heavy workload (long refactors, code review) it’s closer to 0.2×. Run neuralmind benchmark on your actual workload to pin this down.

Three scenarios where the case is strongest

Scenario A — Mid-size team hitting context limits weekly

Profile: 15 devs, 30K-line monorepo, Claude Code agents that crash with “context too large” mid-task at least once a day across the team.

Without NeuralMind: developers waste 15 min/day rephrasing prompts and manually paring context. At $50/hour fully loaded, that is $1,650/month in lost engineering time before any LLM cost.

With NeuralMind: install-hooks auto-compresses Read/Bash/Grep output by ~88–91%. Context-limit failures drop to ~zero. LLM bill drops 1.5–3× alongside.

Combined value: $1,650 productivity recovery + $200–400 LLM savings = ~$2,000/month for a team of 15. ROI on 2 hours of setup: ≥10×.

Scenario B — Solo dev with growing monorepo

Profile: 1 dev, codebase grew from 5K to 50K lines, Claude Sonnet bill went from $20/mo to $200/mo and climbing.

Without NeuralMind: bill keeps growing linearly with codebase size.

With NeuralMind: the per-query token cost stops scaling with repo size. Bill flattens at ~$30–60/mo even as codebase doubles.

Value: $140–170/mo savings, payback in days. Critically, the trajectory changes — costs stop being a function of how much code you’ve shipped.

Scenario C — Regulated environment (offline, on-premise)

Profile: financial-services or healthcare team that can’t send code to external APIs, currently relying on local models with shorter context windows.

Without NeuralMind: context limits force code to be loaded in fragments; agents miss cross-file relationships.

With NeuralMind: local-only retrieval means a 13B parameter model with an 8K-token context window can answer questions that previously needed a 32K-token frontier model. Enables use cases that were previously infeasible, not just cheaper.

Value: unlocks AI-assisted development in environments where it was previously banned or impractical. Hard to dollarize, but often the deciding factor in whether the org adopts AI tooling at all.

Verify each claim yourself

Skeptical? Each row of this table is a single command:

Claim Command What you’ll see
The retrieval reduction claim bash scripts/demo.sh 5.5× on the fixture
The CI claim View any PR’s self-benchmark comment 6.1× on the same fixture
The “works on my code” claim neuralmind benchmark . --contribute YOUR ratio, YOUR tokens, YOUR dollar estimate
The “no data leaves your machine” claim Read SECURITY.md, audit dependencies (chromadb, mcp, pyyaml — all local-only), and run with network disabled (unshare -n bash scripts/demo.sh on Linux, or block on a firewall) — the demo completes after the first-run model download local-only at runtime, not just at install
The “incremental updates work” claim neuralmind build . --force then neuralmind build . second run reports ~all skipped
The “composes with prompt caching” claim Run any agent with NeuralMind’s compressed Read output through your normal cached prompt — observe lower input tokens at cache reads Math holds

If any claim above doesn’t reproduce, open an issue — that’s a higher-priority bug than any feature work.

The case is weaker if…

The honest list, expanded in HONEST-ASSESSMENT.md:

What would make this case stronger over time

We’re tracking these as the highest-impact research investments:

  1. Faithfulness study — does the agent’s answer quality improve, not just shrink? Currently anecdotal; needs a structured eval set.
  2. End-to-end workload benchmark — measure full multi-turn sessions with tool calls, not just isolated retrieval queries. This nails down the “0.4× conversation-mix factor” assumption.
  3. n ≥ 10 outside community benchmarks across languages and repo shapes. Currently n=2.
  4. Long-context-baseline comparison — quantify the marginal win over a Claude Sonnet 1M / GPT-4 turbo + prompt caching baseline.

These appear on ROADMAP.md under “Next” and are open contribution targets. If your organization is evaluating NeuralMind for a real procurement decision, contributing one of these studies materially helps your own evaluation and the ecosystem.

Decision in three lines

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