Why AI cost is more than the price per token
AI cost per task is people time plus attempts multiplied by cost per attempt. Most teams only watch the second half of that. If the same task takes four or five attempts because quality depends on who wrote the prompt, cheaper tokens do not fix the bill. NeoAI reduces all three terms: staff time spent fixing, the number of attempts, and the cost of each attempt.
The equation. AI cost = people time + (attempts × cost per attempt). Recipes cut attempts and fixing time. Smart delegation cuts the cost of each attempt.
Part 1: distilled recipes, so work is right first time
A recipe is the shortest path to an accepted output for one type of task. It records the steps, the context to include, the tools to call and the checks the output must pass. NeoAI builds recipes from how your own tasks play out, and the next similar task receives the recipe automatically.
- Watch. NeoAI observes a task run and whether the output was accepted or sent back.
- Distil. It keeps the shortest path that reached an accepted output and drops dead ends.
- Apply. The next similar task starts from the recipe, with the right context and checks already in place.
- Update. The recipe is revised after every run, and each version is logged.
What a recipe contains
Example recipe, resume screening Illustrative: steps (job description to rubric, score evidence, calibrate and bias-check, shortlist note); context (role brief, scoring rubric, the last three accepted shortlists); tools (CV parser, ATS lookup); check (every score cites evidence and no protected attributes are mentioned).
How a recipe earns its place
A recipe is kept only if it passes your quality bar in the fewest attempts on real tasks. Public and in-house skill libraries are starting material, not the product: they are not scored by results and they are not learned from your company.
Why learning carries over between related tasks
Recipes are stored as building blocks that each worked on their own. A related task borrows the blocks that fit, so only the domain-specific part has to be learned. NeoAI shows which blocks it reused and how confident it is.
Worked example Worked example: moving from resume screening to performance review reuses 4 of 5 building blocks (rubric, scoring, calibration, summary), leaving one domain-specific block to learn. Learning carries over across related tasks only, not across unrelated ones.
Part 2: smart delegation, so each step costs what it should
A simplification agent splits each task into sub-steps and scores how hard each one is. Each step then gets its own model tier (small, mid or frontier), reasoning effort, context and work budget. Simple steps do not need the most expensive model, and public list prices show why it matters: the gap between frontier and small models is about 20x Public list prices.
| Step | Why | Tier |
|---|---|---|
| 1. Turn job description into rubric | Low complexity, short context | Mid |
| 2. Parse each CV to structured fields | Routine, cached instructions | Small |
| 3. Score evidence against rubric | Moderate, only the relevant CV sections | Small |
| 4. Calibrate and bias-check the shortlist | High judgment, capped review loops | Frontier |
| 5. Write the shortlist note | Routine drafting | Small |
Illustrative
Escalation: when a cheap model is unsure, the step moves up a tier
If a step fails a confidence or quality check, NeoAI escalates it to a higher tier automatically. In the example above, a confidence check fails on 6 borderline CVs at step 3 and those move to the mid tier. The user gets a result, never an error. Quality is tested before go-live on your own prompts against your acceptance criteria.
Where NeoAI sits in your stack
Your apps call your existing AI gateway as they do today. Behind it, NeoAI runs in your VPC with a recipe store you own, an evaluation harness and an audit log, and calls your model providers across all three tiers. Adoption is one endpoint change with no change to how teams work. See how this differs from gateways and routers and what stays inside your boundary.
How we prove it on your work
Every claim is tested in a pilot on an evaluation set your team can audit: a baseline period on your current setup, then a NeoAI period on the same tasks and the same quality bar. The pilot report shows cost per accepted task, attempts per task, staff hours on rework, first-pass acceptance and quality pass rate Pilot: to measure. Simulated runs support the method but are not the proof: read how we ran them.