The problem
Nothing breaks on the point of loss. It breaks months later.
A renewal notice nobody knew about goes unanswered. A successor follows a procedure the last person knew was dangerous. By then, the institutional knowledge is unreachable at any price.
42%
of institutional knowledge is held exclusively by one individual
92%
of organisations fail to capture it before those people leave
50–200%
of annual salary is the cost of replacing an employee
Why the current handover cannot work
The standard artefact is a two-page document written by someone with one foot out of the door, reviewed by a manager who cannot know what is missing — because the missing thing is precisely what was never visible to them. Its completeness is unmeasurable by construction.
No function owns the question "did the knowledge actually move?" HR owns the offboarding checklist. Engineering owns the successor. Nobody owns the knowledge.
How it works
Four stages. Nothing installed.
No integrations, no OAuth, no access to your live systems at any point — which makes the first deployment practical for security-conscious organizations.
Ingest
Analyze approved company records — email, chat, tickets, documents and repository history — from exports your organization already controls.
Map
Reconstruct who knows what, identify critical knowledge concentrated around specific people, teams and systems, and rank each risk by business impact.
Interview
Generate targeted transfer sessions around the highest-risk gaps, using the evidence and previous answers to determine what must be asked next.
Verify
Reason across the underlying evidence to test transferred knowledge for coverage, specificity and contradiction. Unsupported or contradicted claims are returned with the source evidence attached.
The intelligence layer
Years of records become a map of operational knowledge risk.
ExitBrainSaver is not a chatbot wrapped around company documents. The engine reasons across the organization's historical work corpus to determine what critical knowledge exists, where it is concentrated, what is missing, and whether it was successfully transferred.
Corpus Intelligence
Extracts people, systems, decisions, procedures, exceptions and relationships from years of unstructured company records.
Knowledge Risk Mapping
Measures where operational knowledge is concentrated and ranks what becomes vulnerable when that concentration is not addressed.
Adaptive AI Interviewing
Builds each interview from the evidence and previous responses — pursuing unresolved knowledge instead of asking a fixed questionnaire.
Evidence-Grounded Verification
Tests transferred knowledge against the source corpus. Contradictions, incomplete answers and unresolved gaps are surfaced with their supporting evidence.
Enterprise AI architecture
Designed for large private corpora, retrieval, embedding, reranking and language-model reasoning, with single-tenant and in-environment deployment options for sensitive enterprise data.
What you receive
Four artefacts, before knowledge is lost
Delivered while the knowledge holder is still available — the only window in which unclear, missing or contradicted knowledge can still be fixed.
Knowledge Risk Map
Every area where critical knowledge is concentrated, ranked by business damage, with the evidence behind each finding.
Successor Handbook
How each area works, why it works that way, what breaks, who to call, and what never to do.
Verification Report
Every answer scored for specificity and coverage; contradicted claims flagged and cited.
Residual Gap List
What is still missing after the transfer — and what requires follow-up before the organization accepts the risk.
Proof
What the engine found in a blind test
A synthetic company: a nine-year senior engineer, 62 artefacts, six pieces of undocumented knowledge deliberately buried in ordinary noise. The engine saw none of the answers in advance.
6 of 6
planted knowledge areas surfaced and correctly ranked
3 of 3
evasive answers caught, each citing the artefacts that refute them
0
false alarms on planted noise
7 → 2
at-risk areas when the same knowledge was shared evenly — the signal tracks concentration, not noise
Synthetic data — no real company information. We report each client's own numbers rather than recycling these.
What this is not
We will not claim more than we can prove
The boundaries we hold, stated before you ask.
| The claim we don't make | What we actually do |
|---|---|
| Not a lie detector | We detect claims your records contradict, answers too vague to be usable, and topics never covered at all. A human decides what a flag means. |
| Not surveillance | We analyse work artefacts your company already owns and retains. No activity tracking, no screen capture, no observation of anyone working. The knowledge holder sees what was extracted and can redact it. |
| It does not invent findings | Every flag must cite specific artefacts by ID, author and date. A flag that cannot cite is suppressed and never shown. The system is tuned to miss a real gap rather than manufacture a false accusation. |
Security
You are handing us sensitive knowledge. We designed for that.
Single-tenant isolation
A dedicated environment per engagement. No shared datastore across clients.
Scheduled destruction
The raw corpus is destroyed on a contractual clock after the engagement closes, with a certificate.
Your keys, your endpoint
Encryption under customer-managed keys, and analysis can run through your own enterprise model deployment.
No model training
Your data is never used to train models and never crosses between engagements — contractually.
In-tenant option
For the strictest requirements, the processing environment deploys inside your own cloud.
Stated plainly
We are early-stage. SOC 2 Type II is in progress and not yet complete. Better you hear it now than in week three.
Questions
The questions people ask first
Is this really AI, or just search?
Both, deliberately. Retrieval finds the relevant evidence. Deterministic scoring keeps the risk ranking auditable. Language models perform the reasoning that conventional search cannot: understanding what a knowledge holder actually claimed, comparing that claim against years of company evidence, identifying contradictions and incompleteness, and determining what still needs to be asked. The objective isn't to generate an answer. It's to determine whether critical operational knowledge actually transferred.
Can it tell when someone is lying?
No, and we will never claim it can. It detects three things: claims your records contradict, answers too vague to be usable, and topics never covered at all. Each one arrives with the source attached, and a human decides what it means.
Is this employee surveillance?
No. We analyse work artefacts your company already owns and already retains — the same material an eDiscovery vendor would receive. No activity tracking, no screen capture, no observation of anyone working. The knowledge holder sees what was extracted and can redact it.
What if the AI invents a contradiction that is not real?
Every flag must cite specific artefacts by ID, author and date. A flag that cannot cite is suppressed and never reaches you. The system is tuned to miss a real gap rather than manufacture a false accusation.
Why not just run an exit interview with ChatGPT?
You can run an interview with a chatbot today. What you cannot do is rank what to ask about, or know whether the answer was true — both require holding the corpus as ground truth and reasoning against it. That grounding is the engineering, and it is what turns an exit interview into a verified handover.
Do we need to connect our systems?
No. You provide the offboarding export your IT team already produces for legal hold — mail, chat, tickets, documents, repository history. There are no integrations, no OAuth, and no access to your live environment at any point.
Who builds this
Two founders. No agency, no contractors.
The engine was written by the two of us and runs end to end today. We say so plainly, because you are deciding whether to hand us sensitive records and you should know exactly how small the team behind them is.
Samir Hanna Safar — Co-founder & CEO
Named inventor on 23 granted U.S. utility patents, with 31 filed and 3 more allowed. Founder of ViaTriAI and ViaDentAI. Sets the claim boundary this product refuses to cross, which is why the sections above tell you plainly what the system cannot do.
Jacob Safar — Co-founder & CTO
Built and ran technology companies through Grassy and Loak, and studied at Berkeley. Owns the architecture, writes the core engine, and hires the first engineers.
Disclosed up front: we are father and son, 51/49, and self-funded. No outside investors, and no third-party claim on the IP.
Get in touch
We are onboarding design partners now
If someone senior has recently given notice, that is the moment this is worth most. A first conversation takes twenty minutes.
Goes straight to the founders. We do not add you to a list, and there is nothing to unsubscribe from. Prefer plain email? .