Applied AI & LLM Solutions
Retrieval pipelines, copilots and agentic automation built on your data — with evaluation harnesses, not vibes.
8Purple · Intelligent infrastructure
Intelligent infrastructure
Intelligent infrastructure
for the AI era
8purple builds and operates the stack beneath modern products — from GPU clusters and hybrid clouds to LLM pipelines and the software your customers touch.
01
Everything between the rack and the product
Six disciplines, one team — so the hand-offs that usually burn months simply don’t exist.
Data Center & GPU Infrastructure
High-density compute: power, liquid cooling, networking and capacity planning.
Software Development
Product engineering with trunk-based development, preview environments and CI that fails fast.
Blockchain Engineering
Settlement rails, tokenized assets and verifiable provenance — where a shared ledger earns its cost.
DevSecOps & Platform
Paved-road pipelines: secret scanning, dependency audits, least-privilege deploys, reproducible builds.
Cloud & Hybrid Operations
Own the baseline, rent the burst. Kubernetes-first workloads that run the same on metal and in the cloud.
02
From assessment to operations
A short, reversible path from idea to a system you can rely on.
01. Assess
We map your workloads, data and constraints, then pick the smallest architecture that does the job.
02. Build
Short iterations behind a CI/CD pipeline — every change tested, previewed and reversible.
03. Operate
We run what we ship: monitoring, SLOs, capacity and cost reviews — or hand over a paved road to your team.
03
What teams say after we ship
“8purple took us from a clever prototype to a system our customers actually rely on. The evaluation harness alone saved us months.”
— Engineering Lead, Fintech scale-up
“They embedded with our team, brought the platform and the practices, and left us owning it. Exactly the hand-off we wanted.”
— CTO, Healthtech
“Our GPU bill dropped by a third without touching model quality. Utilisation, batching and the right hardware — no magic, just rigor.”
— Head of Data, AI platform
04
Start small, scale when it works
Three ways to work with us — escalate only when the previous step has earned it.
Assess — Fixed price / 1–2 weeks
A concrete architecture and roadmap you can execute with or without us.
- Workload & data mapping
- Architecture proposal
- Cost & risk analysis
- Actionable roadmap
Build — Engagement / per sprint
We design and ship the system behind a CI/CD pipeline — tested, previewed, reversible.
- Everything in Assess
- Applied AI / platform build
- Preview environments & CI
- Evaluation & observability
- Embedded with your team
Operate — Retainer / monthly
We run what we ship — or hand over a paved road and keep it healthy.
- Everything in Build
- Monitoring, SLOs & on-call
- Capacity & cost reviews
- Security & dependency audits
05
Frequently asked questions
What does 8purple actually do?
We design, build and operate intelligent infrastructure: AI products, GPU compute, cloud and hybrid platforms, and the software on top of them.
Do you work with existing teams?
Yes. Most engagements embed with your engineers — we bring the platform and practices, your team keeps the ownership and knowledge.
Can you host workloads on-premises?
We run hybrid setups daily: steady-state services on owned hardware, burst and edge traffic in the cloud, one set of manifests for both.
How do projects start?
With a short assessment — one to two weeks, a fixed price, and a concrete architecture plus a roadmap you can execute with or without us.
How do you keep AI features trustworthy?
Evaluation before features: golden datasets, faithfulness checks, citations on every answer, and monitoring that catches drift before users do.
From the blog
All posts Field notes, freshly shipped
How to Deploy AI Models at Scale Without Infra
Tired of babysitting GPUs and dashboards every time you ship a model? This guide shows how to turn experiments into reliable AI services using managed platforms, smart LLM runtimes, and ML-aware CI/CD. Ship faster, spend less time on clusters, and keep your team focused on users.
Read
Building RAG Systems That Don't Hallucinate
Retrieval-augmented generation is easy to demo and hard to trust. Here is what separates a toy from a system you can put in front of customers.
Read
LLM Agents Beyond the Demo: What Production Actually Looks Like
Agent demos run a perfect path once. Production agents face the other 200 paths. Here is how to design for the ones the demo never showed.
Read
Vector Databases, Explained Without the Hype
Do you need a dedicated vector database, or is your existing one enough? A practical look at what these systems actually do and when they earn their keep.
Read
Fine-Tuning vs Prompting: Choosing the Cheaper Path
Fine-tuning feels like the serious option. Most of the time it is the expensive answer to a question prompting already solved. Here is how to tell them apart.
Read
How to Actually Evaluate an LLM Feature
You cannot ship what you cannot measure. Evaluating generative systems is harder than traditional software testing — and skipping it is how good demos become bad products.
Read
Have a workload in mind?
Tell us what you’re building — we’ll reply with an honest take on the smallest thing that could work.