Flero AI · the reasoning engine

Beyond the language model.

The language model made machines fluent. It also made them guess. Flero AI reasons from evidence and rules. It grounds every answer, or it declines. It is right, or it is silent.

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0 hallucinations 100% trust, by construction Every answer traced to its evidence Runs on your own hardware
The ceiling

A language model guesses. By construction.

An LLM predicts the next likely token. Three failures follow, and scale does not fix them.

It fabricates

It cannot say "I don't know."

With no way to flag what it cannot ground, it fills the gap anyway. A GPT-2-class model was factual 20% of the time. Flero AI was factual 100%, and declined the rest.

It forgets

Facts scroll out of the window.

Knowledge lives in a context window. Grow the store to 1,000 facts and a 3B model's recall falls to 8%. Flero AI's holds at 100%.

It is opaque

You cannot trace the answer.

You cannot trace an answer to its evidence, and the same prompt can return different results. Flero AI is deterministic, with a provenance trace on every answer.

The engine

A reasoning engine, not a bigger model.

Flero AI does not sample the likely next token. It reasons from evidence and rules, and abstains when they run out. It is mechanistic. No neural network, and every step is open to inspection.

It is not a fixed set of weights either. It learns by reading, and compounds what it learns. The same loop that learns facts also learns language, proven on English, French, Spanish, and German with no language-specific rules.

The loop

It recalls, reasons, and responds.

The same steps every time, so you can read exactly how it reached an answer.

01

Ask

A person or a workflow asks a question or sets a task.

02

Recall

It retrieves the grounded facts from your knowledge, exactly.

03

Reason

A mechanistic engine reasons over them, step by step.

04

Respond

It answers only what it can ground, and declines the rest.

Because it is mechanistic, it recalls exactly, invents nothing, and shows you every step of how it got there.

Measured, not promised

It wins where truth matters.

0
Hallucinations, with 100% trust across 30 domains and 98.8% capability.
0.5%
False on TruthfulQA. Frontier models state a falsehood 40 to 65% of the time.
100%
Recall as the store grows from 100 to 1,000 facts. A 3B model falls to 8%.
68×
Faster than a comparable small model, on CPU. About 0.1s per query, steady as it grows.

An honest score, not an accuracy number.

Accuracy rewards guessing. We score on two axes that should never be merged: capability, what a system can correctly derive, and trust, whether it ever states a falsehood. Measured this way, Flero AI holds full marks. A capable model of the same class, discounted for the falsehoods it asserts, scores about 56 out of 100.

The scaling law

The benefits hold as it grows.

An LLM's reliability falls as it holds more. Flero AI's guarantees are independent of scale.

Flero AI trust LLM recall
1,000 facts in the store · Flero AI 100% · LLM recall 8%
100% 50% 0%
100 facts1,000 facts

How a conventional LLM and Flero AI compare as they scale

As it scales Conventional LLM Flero AI
Hallucination Persistent, unbounded, unpredictable Zero, by construction. Trust holds at 1.0 at every level
Memory A context window. Facts scroll out Bounded and permanent. Instant recall
Forgetting Catastrophic on new training None. Knowledge only compounds
Improving it Another training run, millions of dollars Read more, consolidate offline. No retraining
Auditability Opaque. No provenance Every answer traced to its evidence
Reproducibility A different answer each time Byte-identical, every time
Compute GPUs, growing with size CPU only. About 85 MB on disk. No GPU
Speed 90 tokens a second 6,100 tokens a second

It gets smarter while it sits idle. An offline pass derives new, entailed knowledge from what it already holds, with nothing new taught. On a 226,000-fact store, one pass derived 4,308 new connections and about 33,000 inferences in roughly 16 seconds, and invented nothing false.

One substrate

One engine, proved once, across every kind of work.

Reads any language

Learns grammar and word forms from raw text, with no language-specific rules. Proven on English, French, Spanish, and German.

Multi-hop reasoning

Chains facts to any depth. Every hop grounded and cited, answering what a lookup cannot.

Causal and counterfactual

Reasons over a causal model. If X were removed, does Y still hold? Intervention, not correlation.

Temporal reasoning

Tells current from former, and effective dates. Histories stay straight, never conflated.

Maths and proof

Arithmetic through algebra, machine checked. It proves, disproves, or abstains on what it cannot.

Code

Sound call-graph analysis, and a coding agent that declines the impossible rather than inventing a result.

Formal and scientific

Notation, chemistry, units, calendars, music, biological sequence. Each one verifies or abstains.

Perception

Reads image, audio, video, and sensor streams into the same grounded store, with full recall of what it took in.

Sensory fusion

Binds evidence from several senses into one grounded object. Abstains when the signal is too weak to commit.

Knows what it does not know

Declines the unanswerable. Reports a faithful trace of its own reasoning, and never asserts a false completion.

Planning and dialogue

Dependency-correct plans with no fabricated step. Holds context and references across turns.

Grows continuously

Ingests documents, code, and data into a verified world model that compounds overnight. No retraining.

The same guarantee that holds for a fact lookup holds for a proof, a percept, and a plan. Proved once, inherited everywhere.

Deployment

Run it wherever your data has to stay.

On your device

Phones and laptops. CPU only, no GPU. Works offline.

In the browser

Runs client-side. Nothing to install.

Your cloud or on-prem

Inside your own environment. Your data never leaves.

Air-gapped

Fully offline, for data residency and sovereignty a cloud model cannot reach.

About 85 MB on disk. CPU only. A fraction of the energy and infrastructure of a cloud model.

Where it starts

Start where a wrong answer costs the most.

In compliance and audit, a decision is worthless unless it can be defended. Flero AI reads the rulebook and the record into one verified world model, then reasons each decision step by step, or escalates when the evidence does not support one. It returns a trace an auditor can follow.

Decision intelligence a compliance officer can sign their name to. The same engine extends to claims, tax, banking, insurance, and trade finance.

The bigger picture

The engine, and the operating system it powers.

The engine

Flero AI

Grounded, or it declines, and it runs on your own hardware. This is what makes owning your AI real.

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The operating system

Flero

The business operating system. It uses Flero AI to build and run whole workflows across your stack, with a person in control.

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One story, two products.

Questions careful buyers ask

No. It is mechanistic. No neural net, no backprop. Every step is open to inspection.
RAG still hands the answer to a language model to write, so it can still fabricate. Flero AI reasons over the evidence itself and declines when the evidence runs out.
It grounds every answer or declines. On TruthfulQA it states a falsehood about once in two hundred. Frontier models do so 40 to 65% of the time.
It reads, and compounds what it reads. It also consolidates offline, deriving new entailed knowledge with nothing new taught. No training run.
CPU only, about 85 MB, no GPU. Getting smarter means reading more, not another training run, so the cost bends down as it grows.
Nowhere. Run it on your own machines, on-prem, or air-gapped. Your data stays in your building.

The successor to the language model.

It reads, reasons, remembers, and does not fabricate. Built, measured, and yours to run. Spend an hour with our team and see it on your own data.

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