Your best people’s AI know-how, on every task. Right first time.
NeoAI learns how your best AI work gets done and applies it across the company, on the right model with the right effort. Less rework, a smaller bill, inside your own cloud. You pay only from measured savings.
- Fewer attempts per task Pilot: to measure
- Up to 85% lower AI spend* Estimate
- Runs in your cloud behind your gateway
- No savings, no fee gainshare pricing
Four or five attempts today. One with NeoAI.
Today one task, same prompt-by-hand
With NeoAI one attempt, split into steps
Today, AI quality depends on who writes the prompt, and you pay for every retry.
Inconsistent
The same task gets different quality across teams, depending on who happens to write the prompt.
Rework
Several attempts per task, and staff hours lost to fixing and re-asking.
Overspend
The frontier model and deep reasoning get used on simple steps, on every attempt.
AI cost = people time + ( attempts × cost per attempt )
NeoAI reduces all three.
Recipes cut attempts and fixing time. Smart delegation cuts the cost of each attempt.
Your best practice, learned from your own work and improving with every run.
Pillar 1, distilled recipes. NeoAI watches how tasks play out and distils the shortest path to an accepted output into a recipe. The next similar task gets it automatically.
Repeats on every run.
- Steps
- 1 Job description → rubric · 2 Score evidence · 3 Calibrate & bias check · 4 Shortlist note
- Context
- Role brief, scoring rubric, last 3 accepted shortlists + this accepted run
- Tools
- CV parser, ATS lookup
- Check
- Every score cites evidence; no protected attributes mentioned
Example recipe Illustrative
This is not a skills library.
Scored by results
A recipe earns its place by passing your quality bar in the fewest attempts on real tasks.
Learned from your company
Built from your own work. Public and in-house skill libraries are only starting material.
Compounds over time
Each task type you master makes the next one cheaper to master. A fresh competitor can’t copy that.
Select a step, or let it play.
Mastered one task? The next related one starts ahead.
Recipes are stored as building blocks that each worked on their own. A new task borrows the blocks that fit, so only the domain-specific part has to be learned.
Drag a node, or hover it to see its match.
One block to learn instead of five.
4 of 5 building blocks reused Worked example Match scores illustrative
Works across related tasks. NeoAI shows which building blocks it reused and how confident it is.
Every step gets the right model and the right effort.
Pillar 2, smart delegation. The simplification agent splits a task into sub-steps and scores how hard each one is. Each step then gets its own model tier, reasoning effort, context and work budget.
When a cheap model is unsure, the step moves up a tier.
A step that fails a confidence or quality check escalates automatically. The user gets a fallback, never an error.
One endpoint change. No workflow change.
Already have a gateway or router? Those choose one model per request. NeoAI sits behind them and tunes the model and the settings for each sub-step, using recipes that already define the steps.
Measured on your work, not ours.
Every claim above is tested in a pilot on an evaluation set your team can audit.
- Your evaluation setReal tasks and your acceptance criteria
- Baseline periodYour current setup, measured
- NeoAI periodSame tasks, same bar
- Metrics$, attempts, hours, quality pass rate
What the pilot report will show Pilot: to measure
Case-study card appears here after the first pilot. Pilot: to measure
Simulated runs support the method. They are not the proof.
In a replay of three open-ended agent tasks, the NeoAI-optimised agent used 65–72% fewer tokens and cost 62–69% less. Simulated These are not live model calls, and quality was not measured.
| Task | Agent steps | Baseline cost | NeoAI, cache cold | NeoAI, cache warm |
|---|---|---|---|---|
| Market-entry research brief | 10 → 7 | $0.0499 | $0.0189 (−62%) | $0.0155 (−69%) |
| Bug fix in an unfamiliar codebase | 10 → 7 | $0.0489 | $0.0191 (−61%) | $0.0157 (−68%) |
| Churn analysis memo from 3 data exports | 9 → 8 | $0.0470 | $0.0210 (−55%) | $0.0176 (−62%) |
Simulated · token counts via tiktoken o200k_base · 3 tasks · 21 Oct 2026. Full method and assumptions to move into a linked methodology page.
- In your VPCBehind your existing AI gateway
- No training for othersPrompts never train anything for other customers
- Recipes you ownOwned and exportable
- Every change loggedEach recipe version is auditable
- Security packDPA and certification plan [status to confirm]
Your cloud, your data, your recipes.
Pillar 3, safe to adopt. Adopting NeoAI adds no new data, integration or financial risk.
Quality is tested before go-live on your own prompts against your acceptance criteria, and the evaluation method is open to your team. Automatic fallback means a user never sees an error.
Every executive gets a straight answer.
| Role | Their question | Our answer |
|---|---|---|
| CEO | Is this strategic? | It captures your best people’s know-how and applies it to every AI task, so it compounds. |
| CTO | How is this different from our gateway or router? | It sits behind your gateway and tunes the model and settings per step, using recipes learned from your own usage. |
| CIO | Is it safe? | It runs in your cloud, your data never leaves, you own the recipes, and every change is logged. |
| CFO | What’s the ROI, and my downside? | We’re paid a share of savings we measure, so there’s no downside. |
You only pay from what we save you.
Free audit, then a pilot measured on your evaluation set, then a gainshare of [X]% of measured savings. No savings, no fee. Commercial
Proportions are illustrative. Illustrative
Savings measured before you commit.
What would this be worth to you?
and about — of staff time back.
Illustrative. Estimate The pilot replaces these with measured numbers.
See your own numbers in 2 weeks, free.
Tell us about your AI usage. We’ll scope a free audit and a pilot measured on an evaluation set you can audit.
- Where spend and rework go today
- Which tasks have recipes worth learning
- What you’d save, sized on your data