AI · Analytics · LLM Inference

The Encryption-in-use platform for software teams working with sensitive data.

Ship the projects that stalled at legal review. Queries return in under a second — and we never see plaintext.

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SOC 2 Type 2 · HIPAA · GDPR · FIPS 140-3 · AES-GCM-256 · NIST-validated

Blind Insight security assistant — always watching, never reading your data
Not
homomorphic encryption. Production speed. Not four-hour operations. hardware enclaves. Pure software. Any CPU. No deprecated silicon. vendor lock-in. Database-agnostic. Any structured source. proprietary crypto. AES-GCM-256. NIST-approved. FIPS-compliant.

Production speed on encrypted data.

Benchmarked on our internal test clusters against 100K–1M encrypted records.

0.00smedian query on 1M encrypted rows
0 queriesthree ML models, zero decrypts
0 F1 deltavs. plaintext baseline, HIPAA k=11
Geological strata cross-section — encrypted data layers flowing through Blind Insight

At every layer

Your sensitive data moves through. The exposure doesn’t.

Sensitive data doesn’t need to be exposed to be useful. It enters encrypted, passes through every layer of your stack—AI models, analytics queries, dashboards—and what comes out the other side is the result, not the source. We never see plaintext. Neither does anyone else.

The encrypted data platform

Sensitive data.
Back on the table.

Real-time searchable encryption

=, <, >, sum, avg, count, min, max, fuzzy match. Every operation runs on encrypted fields, at millisecond latency across millions of records.

Your database answers questions about data it never decrypts. Ranges, aggregates, fuzzy matches — executed on ciphertext, accurate to plaintext, in under a second. 

blind record list --schema UDarTJg3K6 \
  --filter "risk_level:count(50~100)" \
  --filter "account_jurisdiction:JP"
# → 247  (aggregate only — no records returned)
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Fine-grained access controls

Field-level grants by identity, checked on every query and every decrypt. Proxy-backed key segregation keeps cross-border data in its jurisdiction. 

blind grants create --data '{
  "name": "analyst-read",
  "field_names": {"risk_level": true, "account_id": false},
  "can_create_records": false
}'

Audit-ready logs and monitoring

An immutable, cryptographically signed audit trail of every query, grant, and decrypt — exportable to your compliance stack without a follow-up ticket.

Real-time searchable encryption

=, <, >, sum, avg, count, min, max, fuzzy match. Every operation runs on encrypted fields, at millisecond latency across millions of records.

Fine-grained access controls

Field-level grants by identity, checked on every query and every decrypt. Proxy-backed key segregation keeps cross-border data in its jurisdiction.

Audit-ready logs and monitoring

An immutable, cryptographically signed audit trail of every query, grant, and decrypt — exportable to your compliance stack without a follow-up ticket.

The query layer

How a query crosses the boundary without crossing the line.

Query-layer architecture: client SDK encrypts requests, the Blind Proxy forwards encrypted queries to the encrypted store, and aggregate results return without revealing records.

Blind(L)LM™

Let any LLM operate on encrypted data.

Translates prompts into encrypted queries or BlindML model runs, assigns the minimum key set an agent needs, and revokes access at session end. The model never sees a raw customer record.

blind_llm 
from blind_llm import BlindInsightOrchestrator

orc = BlindInsightOrchestrator(backend=proxy_backend)
r = orc.run("Cancer rate for women 60+ with dense breast tissue?")
# LLM sees only aggregate counts — no raw records
print(r.answer)
Learn more about Blind(L)LM™

BlindML

scikit-learn for encrypted data.

Naive Bayes, decision trees, logistic regression—trained on aggregate counts from encrypted data. Bring your own scikit-learn model. 0.0 F1 delta vs. plaintext baseline on 600K records.

blind_ml 
from blind_ml import NaiveBayesModel

model = NaiveBayesModel().fit(marginals, n_pos=3201, n_neg=76402)
pred, risk = model.predict({"fraud_type": "card_fraud"})
# 0.0 F1 delta vs. plaintext — 600K records
Learn more about BlindML

The numbers.

Sublinear scaling
2× data, <2× latency
0.7× storage footprint
No plaintext shadow
SOC 2 Type 2
Compliant
HIPAA
k=11 suppression built in
FIPS 140-3
AES-GCM-256 · NIST-validated

“I put Blind Insight on the roadmap because it’s the first time I’ve seen query performance on encrypted data that’s actually usable in production. The approach is spot-on.”

Patrick McKinney, CISO, Invisible Technologies

Start on your schema. 72 hours. Nothing to deploy.

Get started at the Build tier, $9/month.

Start building — $9/mo Let's talk