About Deploint
Deploint engineers AI, software, cloud, data, cybersecurity and digital systems for organizations operating at scale. We focus on systems that have to work: in production, under load and under scrutiny.
One engineering team
06 disciplines
Production system
Secure, observable and operable
01Mission & vision
Engineer the systems modern enterprises run on, from architecture to production.
We take on technology problems where the cost of getting it wrong is high: AI that has to be accurate and governed, platforms that have to scale, and estates that have to change without stopping. Our job is to design, build, secure and operate those systems, and to leave the organizations that own them able to run them.
Enterprise systems that are secure, observable and operable by default.
Reliability, security and measurability should be the starting point of enterprise technology, not the outcome of a remediation project. Every system we deliver should make the next change easier and safer than the last one.
02Engineering philosophy
We test the assumptions most likely to fail before committing to an architecture, using thin, instrumented prototypes instead of slideware.
Significant decisions are recorded with the options considered and the reasons for the choice, so they can be revisited when the facts change.
Most of a system's life is spent in operation. Systems ship with telemetry, runbooks and documentation, not just features.
We choose established technology by default, and when a problem genuinely needs something newer, we say why and how we will measure it.
03Engineering principles
Design for the load, data volume and team size the system will reach, not only the one it launches with.
In practice
Capacity models, load tests and horizontal scaling paths defined before launch.
Threats are modeled with the architecture, and controls are built into identity, data and delivery from the first sprint.
In practice
Threat models, least-privilege access and security tests in every pipeline.
Anything done twice is a candidate for automation: builds, tests, infrastructure, releases and recovery.
In practice
Infrastructure as code, CI/CD and scripted operational runbooks.
A system you cannot see is a system you cannot operate. Telemetry is a requirement, not an afterthought.
In practice
Traces, metrics, structured logs and SLO-based alerting.
Capabilities are exposed through stable, versioned contracts, so systems and teams can evolve independently.
In practice
Contract-first design, a versioning policy and contract tests.
Workloads are elastic, disposable and reproducible across environments, whichever provider runs them.
In practice
Containers, managed services and immutable deployments.
Decisions about systems and models rest on measurement, from performance baselines to evaluation results.
In practice
Baselines, evaluation suites and decision metrics agreed up front.
Technology serves the people who use and run it. Interfaces, workflows and AI behavior are designed around them.
In practice
User research, accessible interfaces and human checkpoints in AI workflows.
04Technical excellence
Every change is peer reviewed before it merges, including infrastructure, configuration and prompts.
Unit, integration and contract tests run on every change. Critical paths get end-to-end coverage.
Environments are defined in version control and can be rebuilt from it.
Architecture decision records, diagrams and runbooks are part of the deliverable, not an extra.
Latency, throughput and cost targets are set early and measured continuously.
Client engineers are involved throughout, so ownership transfers along with the code.
05Security mindset
Built into every engagement
05 controls
06Innovation
From new idea to adopted practice
05 stages
01
New technology is tested in time-boxed experiments with success criteria agreed before they start.
02
Accuracy, latency, cost and failure modes are measured on representative data before anything reaches production.
03
What we learn feeds our reference architectures and is shared in Insights, including what did not work.
Start an engineering conversation
Bring us the problem. We'll help architect the system.