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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchLattica is building a cloud platform that lets AI services compute on encrypted queries without decrypting the data. A client encrypts a query and keeps the key; the service processes the ciphertext and returns an encrypted result for the client to decrypt. Lattica announced its emergence from stealth on April 23, 2025, alongside a $3.25 million pre-seed round.
What Lattica announced
Lattica says it is developing production infrastructure for fully homomorphic encryption (FHE) in cloud AI workloads. Its pre-seed round was led by Konstantin Lomashuk’s Cyber Fund, with participation from Sandeep Nailwal and other angel investors. The announcement describes a company building a platform—not evidence that its system is already broadly deployed or independently validated in production.
The intended applications include encrypted diagnostics, analytics and financial workflows. Lattica and independent coverage have also identified healthcare, finance and government as sectors where sensitive data can make cloud AI difficult to use.
How FHE lets a service compute without seeing a query
- Encrypt on the client. The person or organization sending data encrypts the query locally. The encryption key remains with the client.
- Compute on ciphertext. The cloud service runs supported operations on the encrypted input without turning it into plaintext. Lattica says providers can deploy a model or database once and serve encrypted traffic through an API.
- Return an encrypted result. The service sends the result back as ciphertext. The client decrypts it with its key.
The intended privacy benefit is that the AI provider does not need to see the query’s plaintext during inference. FHE does not, by itself, establish that every part of a service is private: the model and operations must be compatible with the encryption scheme, and deployment details determine what other information—such as request timing or traffic volume—may be visible.
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What HEAL does in Lattica’s platform
Lattica calls its integration layer HEAL, short for Homomorphic Encryption Abstraction Layer. It is meant to give applications and hardware teams a common contract: application developers work against a stable interface, while accelerator teams can connect GPU, FPGA or ASIC backends through that layer.
This separation matters because encrypted computation is not a drop-in replacement for ordinary inference. FHE naturally supports arithmetic, but neural networks also rely on operations that are not straightforward under common FHE schemes. Making those workloads practical involves compiler work, batching, accelerator kernels and approximations for nonlinear functions. Lattica’s explainer names CKKS and BGV as primitives its stack targets.
How FHE compares with other ways to protect cloud AI data
These approaches protect data at different points and make different trade-offs. FHE’s defining property is that computation can happen on ciphertext; it does not mean the deployment has no other trust or performance considerations.
| Approach | What happens to input data during computation | Main trust or utility trade-off | Deployment considerations |
|---|---|---|---|
| Fully homomorphic encryption | The service computes on encrypted data and returns an encrypted result for client-side decryption. | The service need not decrypt the input for supported computations. Model operations must be adapted to the encryption scheme, and performance is a central challenge. | Requires compatible algorithms, encryption-aware software and, for demanding workloads, optimization such as batching and accelerator support. |
| Confidential computing | Data is processed in a protected hardware environment rather than remaining encrypted throughout computation. | Protection depends on the hardware security model and deployment; it is not the same as asking the service to compute directly on ciphertext. | Requires compatible hardware and software and an assessment of the relevant hardware and platform trust assumptions. |
| Anonymization | Identifiers or other identifying details are removed or transformed before data is used. | Can make conventional data processing easier, but it changes the input and does not provide FHE’s encrypted-computation model. | Requires deciding what to remove or transform; whether the resulting data remains useful depends on the task. |
For a workload that must preserve the original sensitive values while they are processed by a cloud service, FHE is distinct because the computation itself is performed on ciphertext. That property comes with additional engineering and performance demands. Confidential computing and anonymization may suit different threat models or workloads; they should not be treated as interchangeable guarantees.
Why accelerators matter—and what Lattica’s numbers establish
FHE can require far more computation than ordinary inference, so CPU-only reference implementations and accelerator-backed systems are not equivalent performance propositions. An accelerator stack can target higher throughput, but the useful result depends on which neural-network operations it supports, how well it batches requests, how portable the backend is, and whether the reported benchmark is independently reproducible.
Lattica’s own technical materials report a speedup of 10,000× or more over CPU reference implementations and an accuracy difference of less than 1% versus plaintext baselines. These are vendor-reported figures, not an independent audit. The available claim does not establish a universal speedup for all models, hardware, or workloads, nor does it specify a general latency guarantee. The company’s 2025 launch material also cites a finding that 71% of respondents believed practical FHE adoption would come from combining hardware and software; the cited figure alone does not establish the survey’s sample or representativeness.
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What the announcement does—and does not—tell prospective users
The launch is evidence of Lattica’s product direction: cloud inference and database queries on encrypted traffic, with HEAL intended to connect applications to accelerator backends. It is not enough on its own to decide whether a particular regulated workload is ready to migrate. A technical evaluation would need to establish at least the model and operations supported, end-to-end latency and throughput under the intended traffic pattern, hardware requirements, key handling, and what request metadata remains visible.
For healthcare, financial or government use, the key question is not simply whether a platform uses FHE. It is whether the exact workload can be expressed efficiently, whether the privacy boundary matches the organization’s threat model, and whether the implementation’s performance and accuracy are demonstrated under conditions relevant to deployment.
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