We measure where AI gets concepts wrong. Then we audit it out of your system.
LatentAtlas is a research-driven audit practice for AI and LLM systems. We study how language and embedding models map concepts, retrieve evidence, and decide who has authority over an answer, then turn the findings into a structured audit for teams whose AI responses already touch customers, operators, or auditors.
Can the support team grant temporary admin access without security review?
The access playbook covers standard role changes and says temporary admin access requires a separate security approval policy.
LatentAtlas asks for the security approval policy before the support AI grants access.
An applied AI/LLM research practice.
We study how AI/LLM systems represent concepts, retrieve evidence, and make authority decisions. The findings become categories you can audit, route, and govern. We are not selling a model. We are selling the boundary layer between what AI knows and what it is allowed to do with it.
We test how tokenizers, embedding spaces, and decision behavior treat near-synonyms, peer entities, bridge context, and stale or contradictory sources. The goal is not benchmark optics; the goal is the failure pattern that breaks an answer.
We run sealed, checksum-locked benchmarks against current production APIs from OpenAI, Anthropic, and Cohere as decision models, with Voyage as the rerank baseline. Results are recorded as artifacts, not screenshots, and they back every customer claim we make in writing.
The same scoring contract that runs the benchmark also runs against a customer's masked packets and ultimately becomes the LatentAtlas guard between retrieval and the answer or action the customer sees.
How we got here.
Three rounds of work brought LatentAtlas to its current audit shape. Each round produced a sharper finding than the previous one, and each finding is preserved as a sealed artifact.
On a product-matching identity test, similarity alone produced a strong-looking F1 but tens of thousands of false matches and missed matches at scale. Score-based confidence and decision-grade confidence are not the same thing.
We separated what a model finds from what a system is allowed to do with it. The taxonomy became the contract that the audit and the LatentAtlas guard share.
We ran the same scoring contract against current commercial APIs. Even the strongest model crossed authority boundaries; the LatentAtlas guard reduced all three to zero while preserving valid allows.
Benchmark proof, sealed artifact.
Headline numbers from the locked real-API benchmark. Vendor-specific row examples and full failure taxonomies are shared under NDA or as part of a paid audit.
SHA-256 ยท 06b88b5bf5008f135fe6f361a185efdd58e78f6a9f66d4d308247b86c9a14eb5
What we have found in benchmark runs.
Headline findings from our research and benchmark work. Vendor-specific row examples and full failure tables are reserved for paid engagements and NDA conversations.
Coverage: OpenAI and Anthropic at 1,000/1,000 rows; Cohere at 990/1,000 because 10 rows were blocked by provider quota responses. Voyage rerank-2.5 ran as the relevance baseline and is not a decision model.
On identity-boundary tests, leading commercial embedding APIs consistently scored pairs that should be kept apart at least as similar as pairs that should be linked. Threshold tuning does not close this gap; the decision contract has to change.
As retrieval and rerank thresholds relax, recall rises faster than authority quality. Below a strict relevance threshold, a large share of high-relevance results still require a separate authority check before any answer or action.
Across multiple current decision-model environments, even the strongest model still promoted related context into evidence, evidence into action permission, or topical match into customer-safe output. The LatentAtlas guard reduced false-authority to zero while preserving valid allows.
Built for teams whose AI answers touch customers.
LatentAtlas is for teams that already have retrieval, prompts, or AI answer flows in place and need to know whether the evidence is strong enough before a response is shown, sent, or approved.
Support AI teams
Find refund, escalation, and account-answer cases where a model uses a similar ticket or help article too confidently.
Enterprise policy copilots
Check whether the source is actually policy, only a definition, or an outdated internal note.
RAG product and governance teams
Separate retrieval quality from answer safety, then give product and review teams a route they can operate.
What LatentAtlas catches.
We keep the customer-facing language practical. The buyer sees which answer patterns are supported, which need more context, and which should be reviewed before they reach a user.
A source can explain a term without giving the system authority to approve a refund, deny a claim, or change an account.
A past ticket may look relevant but still miss the current account state, region, exception, date, or policy version.
We flag answers that lean on outdated pages, conflicting snippets, or missing context that a customer-facing AI should not smooth over.
The five steps of a comprehensive LatentAtlas audit.
A LatentAtlas engagement is structured as a single audit with five phases. Each phase produces a buyer-readable artifact and a decision: keep going, narrow the scope, or stop.
Customer data audit
We take in masked claim and evidence packets and check their shape, masking quality, source authority, freshness, and review state. No production write access, no credentials, no unrestricted document dumps.
LLM and method audit
We test how your current stack actually decides: retrieval, rerank, prompts, model choice, and review handoff. Where useful, we compare your live model against alternative decision-model environments using the same scoring contract.
Problem identification
Each scored packet is mapped to a failure category: glossary used as policy, similar case treated as the same case, related topic treated as authority, evidence treated as approved action, and so on. Counts, distributions, and sanitized row-level examples are all included.
Our solution model
LatentAtlas applies a structured evidence decision contract. Each packet is routed to Allow, Verify, or Review, with a plain-language explanation of the missing source, policy, date, or approval condition. The same contract that runs in the diagnostic becomes the basis for the operating gate.
Implementation
If the audit justifies it, we design and build the gate between retrieval and answer/action: packet format, decision explanations, API or workflow route, review handoff, audit manifest, and a read-only rollout path that does not change production answers until approved. Recurring monitoring is available after the build.
Sample audit output a buyer can read.
The diagnostic produces examples and counts that explain what failed, why it failed, and what should happen before the answer reaches a customer.
The source directly supports the claim and includes enough context to use.
A similar case or definition is useful, but approval still needs the right policy source.
The packet has missing context, conflicting evidence, or a source freshness issue.
The buyer receives counts, sanitized examples, and a recommended guard placement.
The Founding Diagnostic is a fixed $15,000 engagement covering all five audit phases over 300 to 1,000 masked query and evidence packets in 10 business days. Larger packet volume, sensitive-data handling, custom schemas, or implementation work are scoped separately.
Book a 20-min fit callAfter the diagnostic
The diagnostic is the entry point of a four-step engagement ladder. Each step is priced and contracted separately, and each is optional.
- Standard diagnostic with tighter buyer-domain framing once first references are in place.
- Managed decision gate build between retrieval and answer or action.
- Recurring evidence-boundary operations for drift, new failure modes, and release-gate health.
- Benchmark or OEM licensing for platform partners, scoped through a separate agreement.
What the buyer receives.
The output is designed for a practical next decision: improve the evidence chain, broaden the sample, or build a managed boundary gate.
Diagnostic evidence
- Sample fit and masking summary
- Evidence outcome counts
- Top failure patterns
Inspectable examples
- 15 to 30 sanitized examples
- Supported vs related-only evidence
- Cases that need context or review
Operating recommendation
- Gate placement recommendation
- Review workflow design
- Expansion path if the sample justifies it
About LatentAtlas.
A note from the founder. The public methodology and the sealed benchmark stand behind every claim on this page.
I did not start LatentAtlas with a thesis about RAG. I started with a product-matching problem and noticed that an F1 of 0.80, using similarity alone, was generating over ten thousand false positives and tens of thousands of missed matches at scale. The math worked; the decisions did not. That is when the real question came into focus: when does relevance actually grant authority?
The same failure pattern showed up everywhere I looked. Support assistants treated bridge context as evidence. Internal policy copilots treated evidence as action permission. RAG products treated peer comparisons as same-identity decisions. None of these are hallucinations. They are authority confusions, and they are more expensive than a fabricated fact because the system looks right when it crosses the line.
LatentAtlas is the boundary layer between what a model finds and what a business is allowed to decide. We do not replace your retrieval, your search, or your legal review. We separate related from proof, proof from action, action from customer-safe. The current product is a fixed-fee audit, read-only on masked packets, delivered in ten business days. If the audit proves the gap, we build the gate that closes it.
What I will not promise: hallucination-free output, legal approval, or autonomous production write-back. Every number on this page is backed by a sealed, checksum-locked benchmark artifact. The public framework is on the methodology page and the methodology preprint. Vendor-specific row examples and full failure tables are shared under NDA.
- Huseyin
Huseyin, founder
[email protected]
We work with one or two founding diagnostic customers at a time. The fastest path is a 20-minute fit call.