Data infrastructurethat knows how toPROTECT.
Their team won't find out for six months. By then the SSN, the policy number, the K-1 income figure — whatever it is — is in a chat log on a server somewhere, indexed into another company's training corpus, and sitting in three places no one can reach to delete it from.
Specific penalties vary by statute, jurisdiction, and circumstances. A single incident can trigger multiple regulators at once.
Vault, then extract. Or extract, then vault. Either way — your secrets stay yours.
Logikol pairs a privacy-first data vault with a high-precision document extraction engine. Build AI features and data pipelines on top of either — or both — without ever shipping plaintext PII to a model provider.
Sensitive data, isolated.
Models see only surrogates.
Detect PII as it enters your stack. Tokenize each field into a format-preserving surrogate. Encrypt the original under keys you control. Plaintext never leaves your tenancy — not to OpenAI, not to your analytics warehouse, not to your offshore partner.
- Six-layer detection across structured + unstructured inputs
- Bring-your-own-key (BYOK) — revoke access in one click
- Format-preserving tokens that look real to downstream tools
- Signed audit trail for every authorization and access event
Any document.
Structured, in one call.
A computer-vision + vision-language pipeline that turns scanned PDFs, images, and messy real-world documents into clean, field-level structured output. Markdown-ready for LLMs. JSON-ready for your warehouse. Sub-second per page.
- Field-level extraction with full confidence scoring
- Layout-aware: tables, multi-column, handwritten, stamped, rotated
- Single sync API call — no polling, no multi-step workflow
- Markdown + JSON outputs ready for downstream pipelines
Plaintext goes in.
Surrogates go out.
Every document, record, or message that touches Logikol passes through a four-stage gateway before reaching any downstream consumer. Reversible only inside your tenancy, with your keys.
Six detection layers run in parallel. Format-preserving tokens look real to your downstream tooling. Original ciphertext sits behind customer-held keys — split-storage, separate from the surrogates.
Same task. Same answer.
Different exposure.
Your team uses ChatGPT, Claude, or your in-house model exactly the way they do today. The Vault sits in front. Sensitive fields are tokenized inline. The model sees structure but no secrets — and answers the same question.
Six layers. One document. Nothing slips through.
No single detector finds everything. Run together, they leave nothing for an LLM to leak — across structured fields, free text, scanned images, and inferred identifiers.
Your data, your keys. Yours to revoke.
Documents and tokenized mirrors live in split storage. Originals are encrypted with keys you hold in yourKMS — not ours. Revoke access at any time and even Logikol can't open the locker.
Every field. Every number.
Every checkbox. Right.
A purpose-built extraction engine — not a general-purpose LLM squinting at a PDF. Layout-aware computer vision plus vision-language correction, designed to win on the documents general models break on: scanned, stamped, multi-column, handwritten, rotated, half-readable.
curl -X POST https://api.logikol.com/v1/extract \
-H "Authorization: Bearer $LOGIKOL_KEY" \
-F "file=@statement.pdf" \
-F 'options={
"output": "markdown",
"schema": "auto",
"tokenize_pii": true
}'{
"markdown": "# Account Statement\n\n**Holder:** [name_a3f]\n...",
"fields": {
"account_number": { "value": "[acct_4de]", "confidence": 0.99 },
"balance": { "value": 48200.00, "confidence": 1.00 },
"statement_date": { "value": "2026-04-30", "confidence": 1.00 }
},
"tokens": { "[name_a3f]": "vault_ref://...", "[acct_4de]": "vault_ref://..." },
"pages": 4,
"ms": 1840
}On the documents general models actually fail on.
- Hallucinated values on multi-column tables
- Drops checkboxes, stamps, signatures
- Re-orders rows on rotated scans
- Token cost balloons with page count
- No field-level confidence scores
- Layout-grounded reading order, every page
- Vision pass catches stamps, marks, handwriting
- Field-level confidence on every value
- Flat per-page price — no token surprises
- Built-in PII tokenization (works with the Vault)
Six places this lands the day you turn it on.
Logikol is industry-agnostic data infrastructure. Wherever sensitive data crosses a trust boundary — into a model, a partner, an analyst, an agent — the Vault and Document Intelligence handle the boundary, so you don't have to.
Safe LLM ingestion
Pipe customer records, support tickets, and documents into any model — internal or third-party — without ever shipping a name, ID, or account number in plaintext.
Bulk document intake
Turn statements, claims, contracts, applications, or invoices into clean structured data. Field-level confidence, reviewer queues, no hand correction.
Regulated PII redaction
Detect and tokenize sensitive identifiers across structured rows and unstructured text — for analytics, partner sharing, support exports, anything that crosses a trust boundary.
Agent + workflow guardrails
Sit Logikol in front of multi-step agents. Inputs and outputs both pass through tokenization, with policy enforcement at each call.
Warehouse de-identification
Backfill or stream tokenized columns into Snowflake, Redshift, or BigQuery — analysts query surrogates, only authorized roles re-identify.
Vendor + partner data sharing
Share documents and records with offshore teams, processors, or third-party tools. They see structure and surrogates. Originals stay in your tenancy.
Run it on our infra. Or yours.
Three deployment models. Same platform, same APIs, same SLAs — you choose where the compute and the data live. Sensitive data stays inside whichever perimeter you already trust.
Logikol Cloud
We run it. Spin up in minutes, hit the API, keep your KMS keys in your account. The default for teams that want to ship before the security review finishes.
- Hosted in our cloud, regional residency options
- Customer-held keys (BYOK) by default
- SLA-backed uptime
Dedicated VPC
Your own dedicated Logikol stack inside our cloud. No shared compute, no shared data plane. The right answer for regulated workloads with isolation requirements.
- Single-tenant infrastructure end-to-end
- Private networking · VPC peering
- Customer-controlled key rotation
Self-hosted
Deploy Logikol inside your own AWS, GCP, Azure, or on-prem environment. We ship the platform; your team operates it. Data never leaves your perimeter, period.
- Helm charts · Terraform · air-gapped supported
- Your VPC, your KMS, your audit pipeline
- Zero data egress to Logikol
Same platform, same API contract across all three. Switch later without rewriting integrations — workloads can even straddle deployment modes (e.g. self-hosted Vault + Logikol Cloud Document Intelligence).
Logikol vs. stitching it together yourself.
The alternatives aren't bad — they're just incomplete. A privacy vault alone can't parse your documents. A document extractor alone leaks PII to whatever model you point it at. And building the whole thing in-house is a year-long project that ages out the moment a regulator updates a checklist.
| Capability | Logikol | Vault-only vendor | Extract-only vendor | DIY in-house |
|---|---|---|---|---|
| Privacy Vault — tokenize, detokenize, BYOK | ||||
| Document Intelligence — layout-aware extraction | ||||
| One API contract for both pillars | ||||
| Inline tokenization on every extraction | ||||
| Customer-held keys (BYOK) end-to-end | ||||
| Self-hosted / on-prem deployment | ||||
| Signed audit trail across both pillars | ||||
| Time-to-first-call | Hours | Days | Days | Quarters |
| Cost relative to vault-only + extract-only stack | 1x | 1.4x+ | 1.6x+ | Unknown |
Buyer questions, answered without dancing around them.
Stop pasting plaintext.Start shipping AI.
Bring your hardest document, your strictest data class, and your existing model setup. We'll show you Logikol running against your stack in a 30-minute call.
hello@logikol.com