Models guess.
We compute.

Every AI model gets math wrong. Not sometimes — structurally. DATAFLOW gives your model exact computation with full provenance. One API call. Zero hallucination.

Same prompt. Different math. Every time.

Thermal R_si = thickness / (k × A) — same prompt, three runs
LLM OUTPUT
1st pass: 0.079
2nd pass: ""
3rd pass: 0.07917
Same prompt. Three different answers — including a refusal to compute. No units. No provenance. Which run do you ship?
DATAFLOW
1st pass: 0.07917 K/W
2nd pass: 0.07917 K/W
3rd pass: 0.07917 K/W
Verified source. Peer-reviewed correlation. 3/3 passes verified identical. Full provenance on every number.
Fluid Pressure drop — Darcy vs Fanning friction factor
LLM OUTPUT
1st pass: 2,196 Pa
2nd pass: 8,200 Pa
3rd pass: 2,196 Pa
Uses Fanning factor where Darcy is needed. Off by exactly 4×. Formula correct, constant wrong. You'd never know.
DATAFLOW
1st pass: 8,783 Pa
2nd pass: 8,783 Pa
3rd pass: 8,783 Pa
Verified Darcy friction factor. f = 0.0220. 3/3 passes verified. Provenance traces to verified source. No ambiguity.
Financial Component costs: $19.99 + $14.50 + $8.75 = ?
LLM OUTPUT
1st pass: $43.239999...
2nd pass: $43.24
3rd pass: $43.240000001
Float64 arithmetic. The answer is $43.24. That 0.000001 error compounds across 100K units into real money.
DATAFLOW
1st pass: $43.24
2nd pass: $43.24
3rd pass: $43.24
Decimal mode for money. 3/3 passes verified. Not float64. Not "close enough." Exact to the penny, every time.

How it works

1

Add the function definition

Register dataflow_compute as a tool in your model configuration. One JSON schema. Works with OpenAI, Anthropic, Google, Ollama — any platform that supports function calling.

2

The model calls us when it needs to compute

When a user asks a numerical question, the model sends structured events instead of guessing. Formulas, variables, units, provenance — all specified by the model.

3

We compute exactly and return results

Deterministic math engine. Every result has a source. No approximation, no hallucination, no bell curve. The model receives exact numbers and uses them in its answer.

# Your code calls us when the model invokes the function result = requests.post("https://dataflow.infill.systems/v1/compute", headers={"Authorization": "Bearer sk-dataflow-xxxxx"}, json={ "episode": "thermal", "events": [{ "formula": "R_si = thickness / (k * A)", "expression": "0.775e-3 / (149.0 * 65.7e-6)", "variables": { "thickness": {"value": 0.000775, "unit": "m"}, "k": {"value": 149.0, "unit": "W/(m*K)"}, "A": {"value": 6.57e-5, "unit": "m²"} }, "result_unit": "K/W", "provenance": "Verified source" }] } ) # {"result": 0.07917, "unit": "K/W", "provenance": "Verified source"} # Exact. Deterministic. Every time.

Deterministic. Provable. Auditable.

SAME RESULT

Same input, same output — always

No randomness. No temperature parameter. No bell curve. 2+2=4 every single time, down to the last decimal place.

PROVENANCE

Every number has a source

Thermal conductivity of silicon? Verified source. Heat transfer? Peer-reviewed. Every constant traced, every formula cited.

UNIVERSAL

Works with any model

GPT-4, Claude, Gemini, Llama, Mistral — any function calling platform. Local or cloud. We don't replace your model. We make it accurate.

SELF-HOSTED

Cloud or self-hosted

Use our API or run on your own hardware. Same contract, same results. Defense, medical, regulated — data never leaves your network.

Insurance for your AI workflow

You use AI to move fast. DATAFLOW makes sure it didn't slip a decimal.

PRE-SUBMIT CHECK

Before you push, before you present

Run the same computation through DATAFLOW before you ship code, stamp a design, or walk into a review. If the numbers don't match, stop. Catch it before your stakeholders do.

SOLO VALIDATION

The second set of eyes

No senior engineer to bounce numbers off of? Team doesn't catch the math? DATAFLOW validates assumptions and eliminates poor decisions based on wrong numbers — independently, every time.

ASSUMPTION AUDIT

Your model made an assumption. Did it pick the right one?

Darcy vs Fanning. float64 vs decimal. mm² vs m². Models pick constants and conversions silently. DATAFLOW verifies each one with a cited source — not a confidence score.

EMBARRASSMENT INSURANCE

Don't learn about the error in a meeting

The worst time to find out your model was wrong is when someone questions your numbers. DATAFLOW catches it first — privately, before it leaves your desk.

Simple pricing

Less than your model subscription. More valuable than any of them.

Starter
$15/mo
10,000 events / month
  • All episodes & precision modes
  • Full provenance on every result
  • HTTPS API
  • Community support
API REFERENCE FOR MODELS →