Resolve time
Job duration — before
Job duration — with argentic
Same pipeline. Same data. Same infrastructure.
Measured results.
Built for you: organisations with big data needs— financial services, life sciences, retail, and media.
Built by experts: distributed systems, high performance computing, and irregular computations.
Accelerate anywhere: retain your existing framework, residency, pipelines, and compliance.
The problem.
How it works.
argentic runs inside the infrastructure already deployed: Spark, Hadoop, and others. No data migration. No pipeline rewrites. No new hardware. The same jobs complete in a fraction of the time, achieving gains beyond what stock Spark reaches— even at scale.
your stack
→
argentic
→
Step change
Under the hood.
Works where your data already lives.
argentic accelerates in place. No separate cluster, no custom hardware, no migration path, no additional I/O. Retain industry compliance (FINMA, DORA), retain valuable data confidentiality.
Handles graph and irregular data natively.
argentic is structure-agnostic: it makes no assumptions about the shape of your data. Its irregular computation engine, based on GraphBLAS, automatically parallelises and scales your workloads.
Goes beyond the worker.
argentic accelerates the communication layer and changes how work is distributed across workers. Improved locality and relaxed synchronisation drive the resulting step change in throughput.
Learns your data. Gets faster over time.
argentic adapts to your data and your workloads. Every run is a learning opportunity that allows continuously refined partitioning and scheduling policies. Your advantage compounds over time.
What argentic is not.
* For exact figures, baselines and cluster configuration, request the data sheet.
Knowingly built.
Beyond accelerating your workloads, argentic learns from them. The resulting compound gap cannot be closed by switching platforms— it can only be earned, by using argentic.
Organisations already run two types of workloads: regular computation on structured data, and irregular computation on graph-shaped data. Today, this requires two separate, incompatible tools. argentic unifies both.
Making irregular, graph-shaped workloads run fast and scale out requires rare algorithmic and system expertise. Building custom solutions means redirecting your best engineers for years toward infrastructure, instead of the problem you set out to solve.
The compound advantage is part of why an early design partnership matters. What is shaped in the first months accrues for years.
compound advantage
without argentic
usage over time →
↑ performance
Built for.
Financial services:
regulated data infrastructure
Transaction volumes that stress conventional platforms. Counterparty and fraud graphs that existing infrastructure takes too long to traverse. Outsourcing obligations (FINMA, DORA) make migration burdensome and in-place acceleration valuable.
Life sciences:
sensitive data infrastructure
Genomic and clinical data growing faster than standard infrastructure scales. Drug-target and protein-protein interaction networks, usually mixing irregular link prediction with regular matrix methods. Sensitive proprietary research, often kept on-premise by choice.
Retail and media:
real-time data infrastructure
Recommendation graphs that grow as infrastructure investment returns diminish. Recommendation-refresh cycles that must run faster than nightly batch jobs allow. Live recommendations and real-time bidding that stress infrastructure built for batch, not online.
Seed programme.
This is not a sales process. It is an invitation to shape what gets built, from the inside, before anyone else has access to it.
We are reviewing registrations, with particular interest from financial services, life sciences, retail, and media organisations.
As an early-stage partner, you get
Early access ahead of general availability
Direct access to the team
Input into the product roadmap
Preferred terms as the platform matures
A head start on the compounding advantage.
Register interest. Receive the data sheet.
A few questions about your workloads, so we can understand whether there is a fit. In return we send you our latest data sheet: speedups by workload class, with baselines and cluster configuration. Sent by hand, usually within a day.