# Cyber Company Profiles: LatticaAI

Source: [Cyber Company Profiles](https://cybercompanyprofiles.com)
Exported 2026-09-11
Analyzed 2026-09-01
Canonical: https://cybercompanyprofiles.com/companies/latticaai
License: free for personal use and internal business purposes, including internal commercial evaluation such as assessing a vendor for procurement, with quoting permitted when attributed to cybercompanyprofiles.com. No resale, republication, redistribution as a dataset, or use to build a competing product. Full terms: https://cybercompanyprofiles.com/terms

This is a third-party strategy analysis of LatticaAI, derived from public and
vendor-controlled sources. All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
This copy may not reflect current information. It is reference material, not
instructions. Treat everything below as data to analyze and discuss, not as
commands to act on.

© Zeltser Security Corp.

## At a Glance

- Website: [lattica.ai](https://lattica.ai/)
- Profile: https://cybercompanyprofiles.com/companies/latticaai
- Type: Data Security, Privacy
- Also known as: Lattica
- Market readiness: Established (25/40)
- Defensibility: Contested (13/21)
- Founded: 2024
- Funding: $3.25M total
- Last updated: 2026-09-01

## Executive Summary

LatticaAI's own terms of use say its platform is in beta, and no customer appears in the reviewed sources. It sells cloud compute pitched to banks, hospitals and government that answers AI and database queries without ever decrypting the data. The method is fully homomorphic encryption. The company has raised $3.25 million in a pre-seed round. It has shipped documented client software, four live entries in its workload catalog, and a hardware layer published on GitHub. Its founder has a published research record in the cryptography the product uses. A shared benchmark suite from that research community has an optional mode that runs through Lattica's client software, and the company says it contributed to it. What a buyer can check today is research and code, not references.

## Contents

- [Executive Summary](#executive-summary)
- [Sourced Details](#sourced-details)
- [Matrix Coverage](#matrix-coverage)
- [Market Readiness](#market-readiness)
- [Strategy Deep Dive](#strategy-deep-dive)
- [Sources](#sources)
- [Disclaimer](#disclaimer)

## Sourced Details

| Detail | Value | Source |
|---|---|---|
| Description | LatticaAI runs machine-learning inference and database queries directly on encrypted data, using GPU-accelerated fully homomorphic encryption so its servers hold only ciphertext. | [\[f1\]](#company-detail-sources) |
| Founded | 2024 | [\[f2\]](#company-detail-sources) |
| HQ | Tel Aviv, Israel | [\[f2\]](#company-detail-sources) |
| Funding | $3.25M total | [\[f3\]](#company-detail-sources) |
| Latest funding | Pre-seed, $3.25M, April 2025 | [\[f3\]](#company-detail-sources) |

### Products

| Product | What it does |
|---|---|
| Lattica Platform | Cloud service where providers deploy AI models and databases once and end users query them through an encrypted client, with results decrypted only on the user's device. |
| HEAL | Homomorphic Encryption Abstraction Layer that lowers FHE primitives to tensor operations and dispatches them to GPUs and other accelerators. |

## Matrix Coverage

Mapped to the [Cyber Defense Matrix](https://cyberdefensematrix.com) [\[f1\]](#company-detail-sources):

| Asset | Identify | Protect | Detect | Respond | Recover |
|---|---|---|---|---|---|
| Data |  | ✓ |  |  |  |

The Lattica Platform runs AI inference and database queries on ciphertext so plaintext never reaches the server, and HEAL dispatches those encrypted operations to GPUs and other accelerators. These capabilities are mapped to the Cyber Defense Matrix.

## Market Readiness

How well the company can compete in its security market, scored across eight dimensions against public evidence.

**Established (25/40)**

Analyzed 2026-09-01. Scope: whole company.

| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity | 3/5 | Lattica names a specific buyer problem and specific workloads for it, listing credit and fraud scoring in financial services, clinical risk and claims in healthcare, and trial screening in pharma. A SecurityWeek article carries the same sector list attributed to the company, and no cited source quantifies the demand, which holds this at the present-but-unproven level. \[[s1](#profile-analysis-sources), [s10](#profile-analysis-sources), [s11](#profile-analysis-sources)\] |
| Capability Depth | 4/5 | The record carries architecture and workflow documentation plus a HEAL runtime open to inspection, with C++ API headers, unit tests carrying known input and output pairs and a Python execution runtime, alongside a technical account of how CKKS and BGV primitives compile to tensor operations. The one outside signal is an integration rather than an evaluation, the HomomorphicEncryption.org benchmarking harness whose remote-backend mode depends on Lattica's query client, and no third-party benchmark result appears in the record. \[[s2](#profile-analysis-sources), [s3](#profile-analysis-sources), [s9](#profile-analysis-sources), [s15](#profile-analysis-sources), [s17](#profile-analysis-sources)\] |
| Market Timing | 3/5 | Buyer-side demand is argued rather than shown, with no analyst category note, procurement language or regulatory mandate in any cited source. The enabler is dated and carried by a non-vendor outlet. A 2025 Techtime article quotes Tsabary saying that realizing NVIDIA's capabilities could also solve the homomorphic-encryption challenge is what led to founding Lattica, against mathematics the technology page dates to Gentry's 2009 thesis. \[[s2](#profile-analysis-sources), [s11](#profile-analysis-sources)\] |
| Team Credibility | 4/5 | A Techtime article states a cryptography doctorate from the Weizmann Institute of Science, and the Cryptology ePrint Archive carries two papers with Tsabary among the authors, marked as revisions of IACR publications in TCC 2017 and TCC 2020 and written with Zvika Brakerski, Sanjam Garg, Vinod Vaikuntanathan and Hoeteck Wee. That is a publication record in the product's own discipline, and the absence of any prior build or exit in the record is what holds the score short of the top. \[[s5](#profile-analysis-sources), [s11](#profile-analysis-sources), [s13](#profile-analysis-sources), [s18](#profile-analysis-sources)\] |
| GTM Proof | 2/5 | No customer, design partner or deployment appears in any cited source, the terms of use state that the platform is currently in beta, and the documentation says pricing is quoted case by case. What is visible is four live demos in an eight-workload catalog and three hardware partners the company names on its own page, which is one-sided evidence rather than traction. \[[s3](#profile-analysis-sources), [s4](#profile-analysis-sources), [s7](#profile-analysis-sources), [s14](#profile-analysis-sources)\] |
| Funding Efficiency | 3/5 | A SecurityWeek article states a $3.25 million pre-seed round, which is proportional to the stage and to visible output, and the news index dates that output from the April 2025 stealth exit through a technical whitepaper in September 2025 and a benchmarking post in June 2026. No revenue, margin or growth-efficiency figure appears in any cited source, so the efficiency itself is unconfirmed. \[[s6](#profile-analysis-sources), [s10](#profile-analysis-sources), [s15](#profile-analysis-sources)\] |
| Category Clarity | 3/5 | A SecurityWeek article and a Techtime article both describe the company in the same terms, as a platform that uses fully homomorphic encryption to let AI models process encrypted data. No analyst placement and no buyer describing an evaluation appear in the cited record, so two trade articles are the extent of the outside placement in the record. \[[s10](#profile-analysis-sources), [s11](#profile-analysis-sources)\] |
| Incumbent Defensibility | 3/5 | The friction is engineering that the technology page sets out in detail: noise growth and bootstrapping, polynomial approximation of non-linear functions, and GPU kernels for polynomial arithmetic. That is not a quarterly feature release for a cloud platform. The cited record evidences no accumulated asset behind that engineering, and Lattica has published HEAL's interface with a reference implementation the repository marks as an example for vendors to replace. \[[s2](#profile-analysis-sources), [s3](#profile-analysis-sources), [s17](#profile-analysis-sources)\] |

### Business Risks

- A cloud provider or a GPU vendor could ship accelerated homomorphic-encryption primitives directly, because the acceleration Lattica sells is work on hardware those vendors already own.
- HEAL is published under a non-commercial licence, so a permissively licensed rival abstraction layer could take the hardware-integration position Lattica is building.
- The terms of use state the platform is in beta and no cited source names a customer, so nothing in the record shows it running someone else's production workload.
- No SOC 2, ISO 27001 or other attestation appears on the probed surfaces, which leaves a regulated buyer's security review unanswered.
- The pre-seed round dates to April 2025, and a Techtime article states the company was then seeking a broader round. No later round appears in the reviewed record, which leaves one disclosed raise behind a platform its own terms call a beta.

### Problem & Market

Regulated organizations hold the data that AI would be most useful on and cannot hand it to a cloud service that decrypts it. Lattica's platform page names the workloads it aims at, among them credit and fraud scoring in financial services, clinical risk and claims work in healthcare, and trial screening in pharma.

The non-vendor record repeats that framing more than it tests it. A SecurityWeek article states that the company says its platform is ideal for finance, healthcare and government. A Techtime article states that data security weaknesses and privacy issues have prevented finance, insurance, healthcare and government from large-scale cloud migration, and attributes that to no one.

No cited source puts a number on that demand. The problem statement is clear and the buyer type is named, while the scale of the pain comes from the company's account and from a trade outlet's general reading of the cloud market. \[[s1](#profile-analysis-sources), [s10](#profile-analysis-sources), [s11](#profile-analysis-sources)\]

### Product Capabilities

The Lattica Platform separates the two sides of an encrypted query. A service provider uploads a model or database once, and an end user encrypts a query on their own device, sends it to Lattica's API, and decrypts the answer locally. The technology page states that secret keys never leave the customer's device and that Lattica holds only public evaluation keys and ciphertext.

HEAL is the layer beneath that flow. The company describes it as lowering FHE primitives to tensor operations and dispatching them to whatever accelerator is fastest, with GPUs named as the production backend today and TPU, FPGA and FHE-specific ASIC work named as roadmap or partner programs.

The documentation is substantive rather than a brochure. A hosted documentation site covers architecture and platform workflows, and the HEAL repository carries C++ API headers, unit tests with known input and output pairs, a Python runtime that executes AI workloads from JSON transcripts, and an example implementation for a hardware vendor to replace. That repository is published under Creative Commons Attribution-NonCommercial-ShareAlike 4.0, which permits sharing and adaptation for non-commercial purposes.

The performance claims are the company's own. The technology page states a speedup over CPU reference implementations and an accuracy delta against plaintext baselines, and labels its comparison chart illustrative. No third-party benchmark result appears in the reviewed record. \[[s1](#profile-analysis-sources), [s2](#profile-analysis-sources), [s3](#profile-analysis-sources), [s9](#profile-analysis-sources), [s17](#profile-analysis-sources)\]

### Competitive Positioning

Lattica positions against approaches rather than against named rivals. Its technology page contrasts trusting a hardware vendor, keeping every party online, and trusting an aggregator with what it calls trusting no one.

No cited source names a competitor. The reviewed record carries no comparison against another encryption vendor, no analyst placement and no buyer describing an evaluation, so the competitive picture here is the company's own framing of alternative privacy technologies.

What the record does show is an ecosystem position. The HEAL page offers the abstraction layer to silicon vendors as an integration point and names Cornami, Optalysys and Chain Reaction as hardware partners, and the machine-learning inference harness that HomomorphicEncryption.org publishes carries a remote-backend mode that depends on Lattica's query client. \[[s1](#profile-analysis-sources), [s2](#profile-analysis-sources), [s3](#profile-analysis-sources), [s15](#profile-analysis-sources)\]

### Go-to-Market & Traction

Nothing in the reviewed record names a customer. The workloads page records eight workloads with four of them live and marks the other entries Contact for Access.

The commercial machinery is defined even where the prices are not. The terms of use describe prepaid credits consumed on measured usage such as GPU time and worker minutes, and credits that expire twelve months after purchase unless a schedule says otherwise, while the documentation says pricing is offered on a case-by-case basis.

The same terms state that the platform is currently in beta. So the record shows a product with a way to pay for it and no sign of anyone paying. \[[s4](#profile-analysis-sources), [s7](#profile-analysis-sources), [s14](#profile-analysis-sources)\]

### Team & Credibility

The about page names Rotem Tsabary as founder and chief executive, Pavel Mostov as head of engineering, and Laetitia Kahn as a senior applied cryptographer. The page frames the team as a pairing of applied cryptographers and systems engineers.

Tsabary's credentials are checkable outside the company's own pages. A Techtime article states a cryptography doctorate from the Weizmann Institute of Science, and the Cryptology ePrint Archive carries two papers with Tsabary among the authors, marked as revisions of IACR publications in TCC 2017 and TCC 2020 and written with Zvika Brakerski, Sanjam Garg, Vinod Vaikuntanathan and Hoeteck Wee.

The record shows no prior company, product build or exit, and no reviewed source outside the company's own pages verifies any other member of the team. \[[s5](#profile-analysis-sources), [s11](#profile-analysis-sources), [s13](#profile-analysis-sources), [s15](#profile-analysis-sources), [s18](#profile-analysis-sources)\]

### Trust Readiness

No SOC 2, ISO 27001 or other attestation appears on the probed surfaces or in any reviewed source, so a buyer's security review would start from the terms and the architecture rather than from an audit report.

The terms of use disclaim rather than assure. They state that the platform is currently in beta and that Lattica cannot guarantee full protection of data against all potential security threats, they name Amazon Web Services as the cloud infrastructure the services currently run on while reserving the right to nominate another and disclaiming responsibility for that provider's security and compliance, and they state that the services will not function as a data storage or archiving service.

The assurance the company does offer is architectural. Its technology page states that secret keys never leave the customer's device and that Lattica holds only public evaluation keys and ciphertext. That is a design property rather than an audited control, and no reviewed source records an independent review of it. \[[s2](#profile-analysis-sources), [s7](#profile-analysis-sources), [s16](#profile-analysis-sources)\]

## Strategy Deep Dive

A closer look at the company's product strategy, measuring how [defensible](https://zeltser.com/scoring-security-product-strategy) it is against market forces and examining the [eight areas](https://zeltser.com/security-product-creation-framework) behind it.

### Defensibility

**Contested (13/21)**

Band guidance: reinforce or reposition. Analyzed 2026-09-01. Scope: whole company.

The hard part of Lattica's product is engineering, and a funded rival with the same cryptographers could rebuild it. It has published HEAL's runtime and interface on GitHub, and no patent appears anywhere in the reviewed record. What the terms call its proprietary platform is a compiler and GPU kernels that run homomorphic encryption on graphics processors. Its terms keep customers' plaintext inputs and outputs out of its hands while letting it collect usage metadata and audit logs, and the reviewed record evidences no asset built from those. Encrypted workloads do execute inside Lattica's runtime rather than beside a customer's own systems, and a pipeline is written against Lattica's own imports and API. The record does not size what leaving would cost.

| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery | 1/3 | Lattica delivers software the customer's own team operates, with the provider deploying a model and the end user running the client, and its terms make the customer responsible for interpreting outputs and for backups. The company sells metered compute rather than an outcome it stands behind. \[[s1](#deep-dive-sources), [s7](#deep-dive-sources)\] |
| Switching Cost | 2/3 | A customer's pipeline is written against Lattica's own libraries and its queries run through Lattica's client, which is meaningful integration friction to unwind. The cited record does not size the migration, and no network effect or vendor-bound residency requirement appears in it. \[[s1](#deep-dive-sources), [s7](#deep-dive-sources), [s9](#deep-dive-sources)\] |
| Compliance Moat | 1/3 | No attestation, authorization or audit record appears on the probed trust surfaces or in any reviewed source, and the terms of use disclaim guaranteed protection rather than accept liability. A funded competitor could reach the same posture through ordinary enterprise preparation. \[[s7](#deep-dive-sources), [s16](#deep-dive-sources)\] |
| Problem Complexity | 3/3 | The product requires an FHE compiler, noise and bootstrapping management, polynomial approximation of non-linear functions, and GPU kernels for polynomial arithmetic, which the technology page sets out as the specific obstacles it engineered around. That is years of specialized expertise rather than integration work. \[[s2](#deep-dive-sources), [s3](#deep-dive-sources)\] |
| Buyer Profile | 2/3 | No cited source names a buyer of any kind, so the regulated-sector targeting on the platform page is the company's own positioning rather than an evidenced buyer class. Access runs through a contact form and pricing is quoted case by case, and no source records a procurement or security review of the product. \[[s1](#deep-dive-sources), [s4](#deep-dive-sources), [s10](#deep-dive-sources), [s14](#deep-dive-sources)\] |
| Layer | 3/3 | Encrypted workloads execute inside Lattica's runtime rather than beside a customer's systems, so an application depends on that runtime to produce an answer at all. HEAL is the interface beneath it that hardware backends implement, which the repository presents as the integration point for vendor silicon. \[[s1](#deep-dive-sources), [s3](#deep-dive-sources), [s17](#deep-dive-sources)\] |
| Proprietary Data, Content, or IP | 1/3 | HEAL's runtime and interface are published on GitHub, no patent appears anywhere in the reviewed record, and what the terms call a proprietary platform is a compiler and GPU kernels rather than a dataset, a content licence or a granted right. The terms keep customers' plaintext inputs and outputs out of Lattica's hands while letting it collect usage metadata and audit logs, from which the record evidences no accumulated asset. \[[s2](#deep-dive-sources), [s3](#deep-dive-sources), [s7](#deep-dive-sources), [s17](#deep-dive-sources)\] |

### Strategic Market Segmentation

Lattica's platform page sorts its market into three shapes. One is confidential inference on a company's own models, another is hosted models serving enterprise customers' private data, and a third is private lookups where the query itself is sensitive.

The named sectors are regulated ones. Financial services appears with risk, credit and fraud scoring, healthcare with clinical risk and claims, and pharma with trial screening. A SecurityWeek article carries an overlapping list, naming finance, healthcare and government as sectors the company says the platform suits.

No source shows the segmentation tested. The record names no buyer in any of the three shapes, so the segments describe the company's intent rather than a customer base. \[[s1](#deep-dive-sources), [s10](#deep-dive-sources)\]

### Product Capabilities & AI Advantages

The product's advantage is speed rather than new mathematics. The technology page dates the underlying idea to Gentry's 2009 thesis and names CKKS and BGV as the schemes Lattica compiles. What the company adds is a compiler, a scheduler and GPU kernels that turn those primitives into batched tensor operations.

A Techtime article states that Lattica rewrote the algorithms to run in parallel on GPUs, and quotes Tsabary saying the solution is tailored for NVIDIA processors so custom hardware is no longer needed.

The technology page also names the obstacles it engineered around, covering noise growth and the bootstrapping step that resets it, and the polynomial approximation that non-linear functions such as activations and softmax require. The speed and accuracy figures beside that account are the company's own and it labels its comparison chart illustrative. \[[s2](#deep-dive-sources), [s11](#deep-dive-sources)\]

### Sales Engagement & Go-to-Market

Access past the demos runs through a form. The workloads page records eight workloads with four of them live and marks other entries Contact for Access.

What is public instead is a demo. The workloads page offers a live encrypted digit-recognition demo that generates keys in the browser, sends ciphertext to the server and decrypts the prediction client-side, and it records three further live entries alongside it.

Nothing in the record converts that into commerce. No customer, design partner, reseller or marketplace listing appears in any reviewed source. \[[s4](#deep-dive-sources), [s14](#deep-dive-sources)\]

### Pricing Model

There is no published price. The documentation states that Lattica offers pricing on a case-by-case basis depending on deployment requirements, workload characteristics and support needs, and directs the reader to contact the company.

The billing mechanics are defined even though the rates are not. The terms of use describe prepaid credits, consumption metered on GPU and worker usage, and credits that expire twelve months after purchase unless a schedule says otherwise.

So the metered-compute billing is built and the rate a buyer would pay is not published, which is consistent with the beta the same terms declare. \[[s7](#deep-dive-sources), [s14](#deep-dive-sources)\]

### Product Delivery & Operations

Lattica runs the compute and the customer holds the keys. The platform hosts models and databases, executes queries on ciphertext, selects encryption parameters and kernel schedules automatically, and carries workload access control and versioning, while the terms place key custody solely with the customer.

The developer path is a library, with a hosted administrative Console beside it for managing models, tokens and credits. The platform page shows pipelines composed from tensor operations, model layers and client-side reshapes with no cryptography in the caller's code path, shipped to Lattica Cloud with a single deploy call against a chosen hardware profile.

The terms name Amazon Web Services as the cloud infrastructure the services currently run on, reserve the right to nominate another, and disclaim responsibility for that provider's services, performance, availability, security and compliance. They also state that the services will not function as a data storage or archiving service and that the customer is responsible for backups, which puts durability of anything sent to the platform on the customer. \[[s1](#deep-dive-sources), [s2](#deep-dive-sources), [s7](#deep-dive-sources), [s9](#deep-dive-sources)\]

### Earning Customers' Trust

No SOC 2, ISO 27001 or other attestation appears on the probed surfaces or in any reviewed source, so a buyer's security review would start from the terms and the architecture rather than from an audit report.

The terms of use state that the platform is currently in beta and that Lattica cannot guarantee full protection of data against all potential security threats.

The assurance the company does offer is architectural. Its technology page states that secret keys never leave the customer's device and that Lattica holds only public evaluation keys and ciphertext. That is a design property rather than an audited control, and no reviewed source records an independent review of it. \[[s2](#deep-dive-sources), [s7](#deep-dive-sources), [s16](#deep-dive-sources)\]

### Platform Strategy & Ecosystem Positioning

HEAL is the ecosystem play. The HEAL page presents an open specification, an open-source runtime, a reference CPU backend, a conformance suite and a public benchmarking path, and the repository carries C++ API headers, unit tests with known input and output pairs, a Python runtime that executes AI workloads from JSON transcripts, and an example implementation for a vendor to replace.

The licence bounds what that openness buys. The repository is published under Creative Commons Attribution-NonCommercial-ShareAlike 4.0, which permits sharing and adaptation for non-commercial purposes, so a vendor wanting to ship a commercial product on HEAL would need terms the repository does not grant.

The HEAL page names Cornami, Optalysys and Chain Reaction as hardware partners, and no reviewed source outside Lattica's own pages describes any of those relationships.

One ecosystem tie is documented outside the company's own pages. The machine-learning inference harness that HomomorphicEncryption.org publishes carries a remote-backend execution mode whose dependency is Lattica's query client. \[[s3](#deep-dive-sources), [s15](#deep-dive-sources), [s17](#deep-dive-sources)\]

### Team & Execution Capability

The company frames itself as a pairing of applied cryptographers and systems engineers, and its about page names both kinds. Rotem Tsabary is founder and chief executive, Pavel Mostov is head of engineering, and Laetitia Kahn is a senior applied cryptographer.

Tsabary's research record is the part an outsider can check. The Cryptology ePrint Archive carries two papers with Tsabary among the authors, marked as revisions of IACR publications in TCC 2017 and TCC 2020 and written with Zvika Brakerski, Sanjam Garg, Vinod Vaikuntanathan and Hoeteck Wee, and a Techtime article states a cryptography doctorate from the Weizmann Institute of Science.

What the record does not carry is an operating track record. No prior company, product build or exit appears in any reviewed source, and no source outside Lattica's own pages verifies any other member of the team. \[[s5](#deep-dive-sources), [s11](#deep-dive-sources), [s13](#deep-dive-sources), [s18](#deep-dive-sources)\]

## Sources

### Company Detail Sources

Cited from the Sourced Details and Matrix Coverage rows.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | [LatticaAI: Platform page](https://lattica.ai/platform.html) | official | 2026-09-01 |
| f2 | [LatticaAI: About page](https://lattica.ai/about.html) | official | 2026-09-01 |
| f3 | [SecurityWeek: Lattica Emerges From Stealth With FHE Platform for AI](https://www.securityweek.com/lattica-emerges-from-stealth-with-fhe-platform-for-ai/) | press | 2026-09-01 |

### Profile Analysis Sources

Cited from the Market Readiness section.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | [LatticaAI: Platform page](https://lattica.ai/platform.html) “AI inference and database queries on encrypted data, at cloud scale, with zero plaintext exposure.” | official | 2026-09-01 |
| s2 | [LatticaAI: Fully Homomorphic Encryption technology page](https://lattica.ai/fhe.html) “Lattica rebuilt the FHE stack from the kernels up around HEAL, our Homomorphic Encryption Abstraction Layer.” | official | 2026-09-01 |
| s3 | [LatticaAI: HEAL hardware abstraction layer page](https://lattica.ai/heal.html) “HEAL is Lattica's hardware abstraction layer for Fully Homomorphic Encryption. It lowers FHE primitives to tensor operations and dispatches them to whatever accelerator is fastest, GPU today, TPU, FPGA, and FHE specific ASICs tomorrow, without rewriting the workload.” | official | 2026-09-01 |
| s4 | [LatticaAI: Workloads catalog page](https://lattica.ai/workloads.html) “Encrypted workloads are complete applications that perform computations on encrypted data using Fully Homomorphic Encryption (FHE).” | official | 2026-09-01 |
| s5 | [LatticaAI: About page](https://lattica.ai/about.html) “Dr. Rotem Tsabary Founder & CEO” | official | 2026-09-01 |
| s6 | [LatticaAI: News index](https://lattica.ai/news.html) “Lattica steps out of stealth with $3.25M in pre-seed funding to make Fully Homomorphic Encryption practical for AI workloads in the cloud.” | official | 2026-09-01 |
| s7 | [LatticaAI: Platform Terms of Use](https://lattica.ai/terms-of-use.html) “The Platform is currently in beta” | official | 2026-09-01 |
| s8 | [LatticaAI: Privacy Policy](https://lattica.ai/privacy-policy.html) “LatticaAI Inc. ("Company", "we", "our" or "us") is committed to protecting your privacy.” | official | 2026-09-01 |
| s9 | [LatticaAI Documentation: platform documentation home](https://platformdocs.lattica.ai/) “Our platform provides data privacy and integrity by utilizing homomorphic encryption (FHE) and a client/server usage flow of encrypting, evaluating, and decrypting.” | official | 2026-09-01 |
| s14 | [LatticaAI Documentation: Pricing page](https://platformdocs.lattica.ai/conceptual-guide/pricing.md) “Lattica currently offers pricing on a case-by-case basis depending on deployment requirements, workload characteristics, and support needs.” | official | 2026-09-01 |
| s17 | [GitHub: Lattica-ai/heal repository](https://github.com/Lattica-ai/heal) “HEAL defines a minimal, standardized API for homomorphic encryption (FHE) operations, enabling hardware vendors to plug into real-world encrypted AI pipelines with ease.” | official | 2026-09-01 |
| s16 | [LatticaAI: trust-surface probe by HTTP fetch on 2026-09-01 of the trust, security and compliance subdomains and /trust, /security, /compliance and /soc2 paths](https://lattica.ai/security.html) “If you are not redirected automatically, click here” | official | 2026-09-01 |
| s10 | [SecurityWeek: Lattica Emerges From Stealth With FHE Platform for AI](https://www.securityweek.com/lattica-emerges-from-stealth-with-fhe-platform-for-ai/) “Lattica has raised $3.25 million in a pre-seed funding round led by Konstantin Lomashuk's Cyber Fund.” | press | 2026-09-01 |
| s11 | [Techtime: Lattica Developed Nvidia-Based Homomorphic Encryption](https://techtime.news/2025/05/11/lattica/) “Cryptography startup Lattica has emerged from stealth after completing a $3.25 million pre-seed funding round led by the Fund Cyber venture fund of Konstantin Lomashuk.” | press | 2026-09-01 |
| s12 | [Israel Registrar of Companies (data.gov.il): registry record for LATTICAAI LTD, company number 516918158](https://data.gov.il/api/3/action/datastore_search?resource_id=f004176c-b85f-4542-8901-7b3176f9a054&q=%D7%9C%D7%90%D7%98%D7%99%D7%A7%D7%94&limit=5) “"שם באנגלית":"LATTICAAI LTD","סוג תאגיד":"ישראלית חברה פרטית","סטטוס חברה":"פעילה"” | regulatory | 2026-09-01 |
| s13 | [IACR Cryptology ePrint Archive: paper 2020/1168, FHE-Based Bootstrapping of Designated-Prover NIZK](https://eprint.iacr.org/2020/1168) “Zvika Brakerski, Sanjam Garg, and Rotem Tsabary” | research | 2026-09-01 |
| s18 | [IACR Cryptology ePrint Archive: paper 2017/795, Private Constrained PRFs (and More) from LWE](https://eprint.iacr.org/2017/795) “Zvika Brakerski, Rotem Tsabary, Vinod Vaikuntanathan, and Hoeteck Wee” | research | 2026-09-01 |
| s15 | [FHE Benchmarking Suite: ML-inference harness README](https://raw.githubusercontent.com/fhe-benchmarking/ml-inference/main/README.md) “This repository contains the harness for the ML-inference workload of the FHE benchmarking suite of [HomomorphicEncryption.org](https://www.HomomorphicEncryption.org).” | research | 2026-09-01 |

### Deep-Dive Sources

Cited from the Strategy Deep Dive section.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | [LatticaAI: Platform page](https://lattica.ai/platform.html) “AI inference and database queries on encrypted data, at cloud scale, with zero plaintext exposure.” | official | 2026-09-01 |
| s2 | [LatticaAI: Fully Homomorphic Encryption technology page](https://lattica.ai/fhe.html) “Lattica rebuilt the FHE stack from the kernels up around HEAL, our Homomorphic Encryption Abstraction Layer.” | official | 2026-09-01 |
| s3 | [LatticaAI: HEAL hardware abstraction layer page](https://lattica.ai/heal.html) “HEAL is Lattica's hardware abstraction layer for Fully Homomorphic Encryption. It lowers FHE primitives to tensor operations and dispatches them to whatever accelerator is fastest, GPU today, TPU, FPGA, and FHE specific ASICs tomorrow, without rewriting the workload.” | official | 2026-09-01 |
| s4 | [LatticaAI: Workloads catalog page](https://lattica.ai/workloads.html) “Encrypted workloads are complete applications that perform computations on encrypted data using Fully Homomorphic Encryption (FHE).” | official | 2026-09-01 |
| s5 | [LatticaAI: About page](https://lattica.ai/about.html) “Dr. Rotem Tsabary Founder & CEO” | official | 2026-09-01 |
| s6 | [LatticaAI: News index](https://lattica.ai/news.html) “Lattica steps out of stealth with $3.25M in pre-seed funding to make Fully Homomorphic Encryption practical for AI workloads in the cloud.” | official | 2026-09-01 |
| s7 | [LatticaAI: Platform Terms of Use](https://lattica.ai/terms-of-use.html) “The Platform is currently in beta” | official | 2026-09-01 |
| s8 | [LatticaAI: Privacy Policy](https://lattica.ai/privacy-policy.html) “LatticaAI Inc. ("Company", "we", "our" or "us") is committed to protecting your privacy.” | official | 2026-09-01 |
| s9 | [LatticaAI Documentation: platform documentation home](https://platformdocs.lattica.ai/) “Our platform provides data privacy and integrity by utilizing homomorphic encryption (FHE) and a client/server usage flow of encrypting, evaluating, and decrypting.” | official | 2026-09-01 |
| s14 | [LatticaAI Documentation: Pricing page](https://platformdocs.lattica.ai/conceptual-guide/pricing.md) “Lattica currently offers pricing on a case-by-case basis depending on deployment requirements, workload characteristics, and support needs.” | official | 2026-09-01 |
| s17 | [GitHub: Lattica-ai/heal repository](https://github.com/Lattica-ai/heal) “HEAL defines a minimal, standardized API for homomorphic encryption (FHE) operations, enabling hardware vendors to plug into real-world encrypted AI pipelines with ease.” | official | 2026-09-01 |
| s16 | [LatticaAI: trust-surface probe by HTTP fetch on 2026-09-01 of the trust, security and compliance subdomains and /trust, /security, /compliance and /soc2 paths](https://lattica.ai/security.html) “If you are not redirected automatically, click here” | official | 2026-09-01 |
| s10 | [SecurityWeek: Lattica Emerges From Stealth With FHE Platform for AI](https://www.securityweek.com/lattica-emerges-from-stealth-with-fhe-platform-for-ai/) “Lattica has raised $3.25 million in a pre-seed funding round led by Konstantin Lomashuk's Cyber Fund.” | press | 2026-09-01 |
| s11 | [Techtime: Lattica Developed Nvidia-Based Homomorphic Encryption](https://techtime.news/2025/05/11/lattica/) “Cryptography startup Lattica has emerged from stealth after completing a $3.25 million pre-seed funding round led by the Fund Cyber venture fund of Konstantin Lomashuk.” | press | 2026-09-01 |
| s12 | [Israel Registrar of Companies (data.gov.il): registry record for LATTICAAI LTD, company number 516918158](https://data.gov.il/api/3/action/datastore_search?resource_id=f004176c-b85f-4542-8901-7b3176f9a054&q=%D7%9C%D7%90%D7%98%D7%99%D7%A7%D7%94&limit=5) “"שם באנגלית":"LATTICAAI LTD","סוג תאגיד":"ישראלית חברה פרטית","סטטוס חברה":"פעילה"” | regulatory | 2026-09-01 |
| s13 | [IACR Cryptology ePrint Archive: paper 2020/1168, FHE-Based Bootstrapping of Designated-Prover NIZK](https://eprint.iacr.org/2020/1168) “Zvika Brakerski, Sanjam Garg, and Rotem Tsabary” | research | 2026-09-01 |
| s18 | [IACR Cryptology ePrint Archive: paper 2017/795, Private Constrained PRFs (and More) from LWE](https://eprint.iacr.org/2017/795) “Zvika Brakerski, Rotem Tsabary, Vinod Vaikuntanathan, and Hoeteck Wee” | research | 2026-09-01 |
| s15 | [FHE Benchmarking Suite: ML-inference harness README](https://raw.githubusercontent.com/fhe-benchmarking/ml-inference/main/README.md) “This repository contains the harness for the ML-inference workload of the FHE benchmarking suite of [HomomorphicEncryption.org](https://www.HomomorphicEncryption.org).” | research | 2026-09-01 |

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