01Most teams start here
Diagnostic Sprint
2 weeks · fixed price
Best for: You need a clear picture and a roadmap before committing budget.
- Architecture, cost, and security review
- AI / ML readiness score
- Prioritised roadmap with ROI estimates
What we do
Built to ship.Yours to keep.
We take AI, cloud, data, and infrastructure to production standards, down to the hardware it runs on, with success measured in numbers you agree before we start. Then we hand it over, and your team owns it.
30–60%cloud spend removed on optimised workloads
2 weeksto a prioritised roadmap with ROI estimates
11disciplines, from model to rack, one accountable team
100%yours: code, accounts, runbooks, and the know-how
Start with the problem
Where are you stuck?
Pick the sentence that sounds like your team. Each one leads to the service that solves it and the engineers who have solved it before.
Three ways to start
Low-risk first step. Real outcomes after.
Every engagement begins with numbers, not slideware. Start small, see the roadmap, then decide how far to go.
02
Build & Ship
6–10 weeks · milestone scoped
Best for: You know the initiative and want it in production, hardened and documented.
- Production infrastructure and CI/CD
- Security review and observability
- Runbooks, handover, 30-day hypercare
03
Embedded Partnership
Monthly retainer
Best for: You want senior engineers on your team without hiring a bench.
- Dedicated senior engineers
- Weekly syncs, quarterly reviews
- No change orders, priority SLA
Full detail on scope and pricing on the pricing page →
01
"Build Production AI That Actually Ships"
We take AI from a promising prototype to hardened, observable production. Agents, retrieval pipelines, and the MLOps backbone your system needs to stay accurate, cost-controlled, and reliable as usage compounds.
- From proof-of-concept to production, with success metrics defined before we write a line of code
- Bedrock Agents for single & multi-agent architectures with custom tools and guardrails
- Advanced RAG using Snowflake Cortex, OpenSearch, and Kendra, with chunking, re-ranking, and eval harnesses
- Pipelines via LangChain, LlamaIndex, Haystack, and Strands
- MLOps with MLflow, Airflow, Kubeflow, and SageMaker
- LLM evaluation, prompt & version management, and drift / hallucination monitoring
- Fine-tuning, distillation, and model routing tuned for price-performance
- Inference cost & latency observability per model and route, with budget alerts and automatic fallback
Outcome
"Faster time-to-value, lower inference costs, and AI you can audit and defend."
Good fit if you need
- Customer-facing assistants and copilots grounded in your own data
- Multi-agent workflows that take real actions behind guardrails
- Internal knowledge retrieval across documents, tickets, and wikis
Stack
- Amazon Bedrock
- SageMaker
- LangChain
- LlamaIndex
- Snowflake Cortex
- OpenSearch
- MLflow
- Airflow
- Kubeflow
02
"Intelligent Devices That Make Money"
Low-latency intelligence at the edge, with secure provisioning and fleet operations that scale from a prototype to thousands of devices in the field, resilient even when connectivity isn't.
- ML inference deployment via AWS IoT Greengrass and Azure IoT Edge
- Real-time, low-latency decision-making at the device
- Fleet management for thousands of devices with secure zero-touch provisioning
- Digital twins and over-the-air model & firmware updates
- Edge-to-cloud telemetry with offline-first resilience
- Purpose-built for manufacturing, healthcare, and logistics environments
Outcome
"Real-time decisions at the device, with the fleet infrastructure to scale them."
Good fit if you need
- Real-time quality inspection on a production line
- Predictive maintenance from on-device sensor data
- Connected medical and logistics devices with offline resilience
Stack
- AWS IoT Greengrass
- Azure IoT Edge
- Digital Twins
- OTA Updates
- MQTT
- Edge ML
03
"Cloud That Costs Less & Scales Infinitely"
Multi-cloud and hybrid architecture done right: resilient, sustainable, and continuously optimized so spend tracks value instead of waste.
What a landing zone looks like
Cloud accessAdd cloud account
-
AWSAccount connected
Permissions granted (3)
- Bedrock Invoke
- EKS Describe
- S3 Read
- AzureAccount connected
Permissions granted (2)
- Entra ID Read
- Azure OpenAI Access
- GCPAccount connected
Permissions granted (3)
- Vertex AI Invoke
- BigQuery Query
- GKE Deploy
OIDC · short-lived credentials · least privilege · no keys in CI
- Multi-cloud and hybrid environments (AWS, Azure, GCP)
- 30 to 60% cost reduction through FinOps discipline and right-sized architecture
- ECS, EKS, AKS, and GKE containerized applications
- Landing zones, Well-Architected reviews, and IaC with Terraform / Terragrunt
- Zero-downtime migrations and autoscaling reference architectures
- Disaster recovery planning: multi-region active/active or warm standby, with RPO / RTO targets and rehearsed failover
- Sustainability reporting and carbon-aware scheduling
Outcome
"Lower spend, predictable scaling, and infrastructure your team can manage without heroics."
Good fit if you need
- Re-platforming legacy applications onto containers and managed services
- Cutting 30 to 60% off cloud spend without losing capacity
- Building a compliant, repeatable landing zone for new workloads
Stack
- AWS
- Azure
- GCP
- Terraform
- Terragrunt
- EKS
- ECS
- AKS
- GKE
- Multi-region DR
04
"Insights at Petabyte Scale"
We build data platforms that turn raw events into decisions your team can trust. Streaming ingestion, governed transformations, and modernized BI in one production-hardened stack.
- Snowflake, dbt, and Airflow ETL platforms
- Kafka and Flink for real-time streaming
- Lakehouse architecture, data contracts, and governance / lineage
- Feature stores that feed production ML
- BI modernization and advanced visualization
- Petabyte-scale analytics and predictive modeling
Outcome
"Trusted data your whole organization can act on, from analyst laptops to petabyte scale."
Good fit if you need
- Real-time streaming analytics from operational systems
- A governed lakehouse with lineage and data contracts
- Feature stores that serve production ML in real time
Stack
- Snowflake
- dbt
- Apache Airflow
- Apache Kafka
- Apache Flink
- Lakehouse
05
"CI/CD That Never Breaks Production"
GitOps-driven delivery pipelines and deep observability so shipping to production is routine, fast, and reversible, measured against DORA and SLO targets.
- GitOps using ArgoCD and Terraform / Terragrunt
- Observability with Grafana, Prometheus, and OpenTelemetry
- Internal Developer Platforms (IDP) and paved golden paths
- Progressive delivery: canary, blue/green, and automated rollback
- DORA metrics and SLO-driven reliability engineering
- Automated remediation and deployment pipeline optimization
Outcome
"Ship with confidence. Recover in minutes. Engineers stop fearing deployments."
Good fit if you need
- Standing up an Internal Developer Platform with golden paths
- Progressive delivery with canary, blue/green, and auto-rollback
- End-to-end observability across services and infrastructure
Stack
- ArgoCD
- Terraform
- Terragrunt
- Grafana
- Prometheus
- OpenTelemetry
- Kubernetes
06
"Secure Your Models & Data Pipelines"
Security designed for AI: defending models and pipelines against modern adversarial threats while staying compliant by default across the whole supply chain.
- Defense against adversarial attacks and prompt injections
- AI red-teaming: jailbreak, data-exfiltration, and robustness testing
- GDPR, CCPA compliance via data privacy controls and zero-trust architectures
- Secrets management, SBOM supply-chain security, and policy-as-code
- Continuous monitoring and incident response for AI workloads
- Model & dataset provenance with signed artifacts, model registries, and audit-ready access logs
Outcome
"AI systems your legal team can sign off on and your security team can monitor."
Good fit if you need
- Red-teaming an LLM application before it ships
- Defending agents and RAG pipelines against prompt injection
- Provable model and dataset provenance with signed artifacts
Stack
- Zero-Trust
- AI Red-Teaming
- Policy-as-Code
- SBOM
- Model Registry
- GDPR / CCPA
07
"Your Models, Your Hardware, Your Data"
We design, source, rack, and tune local GPU infrastructure so you can run open models in-house, with full data residency, predictable cost, and no per-token cloud bill. From a single Mac Studio to a rack of NVIDIA accelerators.
What a private model rack looks like
Rack accessAdd rack
- Mac Studio clusterRack connected
Permissions granted (3)
- Ollama Pull
- EXO Cluster
- Model Registry Read
- NVIDIA H100 rackRack connected
Permissions granted (3)
- vLLM Serve
- TGI Serve
- GPU Metrics Read
Private network · your hardware · no per-token bill · no data leaves
- Apple Silicon builds: Mac Studio M3 Ultra and unified-memory clustering (EXO) for large-context open models
- NVIDIA workstation & rackmount servers: RTX 5090, L40S, H100/H200 in 1U to 5U chassis (BIZON / Premio-class)
- Right-sizing to your models: VRAM / unified memory, throughput (tokens/s), and concurrency targets
- Power, cooling, airflow, and short-depth rack layout for office or colocation
- On-prem inference stack: Ollama, vLLM, and TGI behind OpenAI-compatible APIs
- Air-gapped options with compliance-grade audit, access control, and data residency
Outcome
"Private, owned AI capacity at a fraction of recurring cloud spend"
Good fit if you need
- Private inference for sensitive or regulated data
- Replacing a growing per-token cloud bill with owned capacity
- Air-gapped deployments with full data residency
Stack
- Mac Studio M3 Ultra
- NVIDIA H100 / H200
- RTX 5090 / L40S
- Ollama
- vLLM
- TGI
- EXO
08
"Automate the Busywork, Keep the Control"
We stand up n8n (self-hosted or cloud) and wire AI into your real operations with rule-based guardrails and human-in-the-loop approvals, so automation is reliable, auditable, and genuinely yours.
- Self-hosted or cloud n8n setup, hardening, and CI/CD for workflows
- AI combined with rule-based logic, input sanitization, and human-in-the-loop approvals
- Lead capture, AI scoring and intent detection, then CRM routing and alerting
- Integrations across CRMs, databases, Slack/email, and internal APIs
- Custom JavaScript / Python nodes and reusable workflow libraries
- Execution-based cost model: complex workflows without per-task bill shock
Outcome
"Teams scale output without scaling headcount"
Good fit if you need
- Lead capture, AI scoring, and CRM routing end to end
- Document and email triage with human approval gates
- Connecting internal APIs, databases, and SaaS tools
Stack
- n8n
- Webhooks
- CRM Integrations
- Slack / Email
- Custom JS / Python
- Human-in-the-loop
09
"Persistent Agents That Work For You"
We deploy and operate self-hosted autonomous agents (Hermes Agent by Nous Research, OpenClaw, and custom stacks) with persistent memory and your choice of model backend, fully under your control and private by default.
- Hermes Agent & OpenClaw deployment with self-improving skills and persistent memory
- Model-agnostic backends: Nous Portal, OpenRouter, NVIDIA NIM, Hugging Face, OpenAI, or your own endpoint
- Multi-channel gateways: Slack, Telegram, WhatsApp, Discord, Signal, email, and CLI
- Runs anywhere, from a low-cost VPS to your on-prem GPU rack
- Guardrails, tool & permission scoping, and full audit logging
- Private by default: no telemetry, no cloud lock-in
Outcome
"Always-on agents that compound knowledge over time"
Good fit if you need
- An always-on assistant in Slack, Telegram, or WhatsApp
- Agents that compound knowledge with persistent memory
- Private deployments with no telemetry or cloud lock-in
Stack
- Hermes Agent
- OpenClaw
- OpenRouter
- NVIDIA NIM
- Hugging Face
- Persistent Memory
10
"Every Machine Specced, Racked, and Working"
End-to-end hardware support: we spec and source workstations, servers, networking, and storage, build and deploy them, then keep them running with monitoring, patching, and responsive on-site or remote help.
- Specification, procurement, and trade-channel sourcing for workstations, servers, GPUs, laptops, and peripherals
- Custom builds, imaging, and standardised deployment, including high-end AI, render, and video-editing workstations
- Racking, cabling, power, cooling, and network layout for offices, studios, and colocation
- Networking and connectivity: switching, structured cabling, Wi-Fi surveys, firewalls, VPN, and UPS
- Managed support: asset inventory, warranty tracking, monitoring, patching, firmware, and tested backups
- On-site and remote break/fix with defined response times, RMA handling, and refresh planning
Outcome
"Hardware that just works, with one number to call when it doesn't"
Good fit if you need
- Kitting out a new office, studio, or lab from empty room to working desks
- Replacing ad hoc laptop buying with a standard, supported fleet
- Specifying high-end workstations for AI, render, or video work
Stack
- Apple / Mac
- Windows & Linux
- NVIDIA GPUs
- Dell / HP / Lenovo
- UniFi / Cisco
- NAS & Backup
- UPS & Racking
11
"Go Live on Twitch and Kick, Built Properly"
Complete streaming stations for creators, studios, and brands: PC and capture hardware, cameras, lighting, and audio, plus OBS installed, configured, and tuned, with overlays, alerts, and multistreaming to Twitch, Kick, YouTube, and X.
- Streaming PC builds and two-PC encode rigs, capture cards, and NVENC / AV1 encoder tuning to your real upload
- Cameras, lenses, and lighting: key, fill, and back lighting, colour matching, and green screen or keyed backdrops
- Audio done right: microphones, interfaces and mixers, monitor mixes, noise suppression, and acoustic treatment
- OBS Studio installation and configuration, scene collections, hotkeys, sources, and local recording
- Overlays, alerts, chat and moderation, plus bot and Stream Deck integration
- Multistreaming to Twitch, Kick, YouTube, and X, with network, bonding, and failover so a drop is not a dead stream
Outcome
"A studio you can go live from in one click, and keep running"
Good fit if you need
- A creator going full-time who needs a reliable, repeatable setup
- Building an in-office studio for podcasts, webinars, or brand streams
- Fixing an existing rig that drops frames, desyncs audio, or overheats
Stack
- OBS Studio
- Streamlabs
- Twitch / Kick
- YouTube Live
- Elgato Capture
- Stream Deck
- NVENC / AV1
Why AzeniQ
What you get that a slide deck can't promise.
01
Metrics before code
Accuracy, latency, cost per request, and hallucination rate are defined and measured before the first production commit.
02
You own everything
We build on your accounts and repos. Code, infrastructure, runbooks, and the know-how stay with you when we leave.
03
Senior engineers only
No bench, no juniors learning on your budget. The people on the intro call are the people doing the work.
04
A fixed-scope first step
The Diagnostic Sprint is fixed price and two weeks. You get a roadmap either way, and no obligation to continue.
05
Security by default
Short-lived credentials, policy as code, SBOMs, and an audit trail, so GDPR and CCPA reviews start from evidence.
06
From model to rack
Cloud, on-prem GPU, edge, and the physical hardware underneath. One partner is accountable for the whole system.
How an engagement runs
Designed to leave you stronger.
De-risk fast, prove it with numbers, engineer for production, then hand over. Every phase ends with something you keep.
- 01Weeks 1–2
Discover
Architecture, cost, and security review. Success metrics agreed in numbers.
You keepRoadmap with ROI estimates
- 02Weeks 2–4
Prove
A focused proof-of-concept against the metrics, with an evaluation harness that scores every change.
You keepGo / no-go on evidence
- 03Weeks 4–10
Build
Hardened infrastructure, CI/CD, observability, and a security review, on your accounts.
You keepProduction system, documented
- 04Ongoing
Own
Runbooks, handover sessions, and 30 days of hypercare. Stay embedded only if you want to.
You keepYour team runs it
Before the first call
The questions every buyer asks.
How do we start?
A 30-minute intro call, then a two-week fixed-price Diagnostic Sprint. You leave with an architecture, cost, and readiness review plus a prioritised roadmap, whether or not we work together afterwards.
Who owns what you build?
You do, from the first commit. We work in your cloud accounts and repositories, and every engagement ends with runbooks and handover sessions so your team can run and extend it.
Do you work alongside our engineers?
Always. Your engineers are in the room for design decisions and pairing, because the goal is a capability that stays in-house, not a dependency on us.
Which clouds and models do you support?
AWS, Azure, and GCP, plus on-prem GPU and Mac Studio clusters and edge devices. We are model-agnostic: Bedrock, SageMaker, open models through Ollama and vLLM, or a mix routed by cost and accuracy.
How do you handle security and compliance?
Short-lived credentials, least privilege, policy as code, SBOMs, and an audit trail are defaults. We sign NDAs before discovery and design for GDPR and CCPA from the start.
Do you handle disaster recovery and multi-region?
Yes. We design and build multi-region active/active and warm-standby architectures with RPO and RTO targets agreed up front, and we rehearse failover on a schedule so it works when it matters. DR planning is part of every Cloud-Native Transformation engagement.
How long until we see something working?
A measurable proof-of-concept usually lands within the first two to four weeks. Full production builds typically run six to ten weeks depending on scope.
Next step
Ready to Build The Future Together?
Tell us where you're headed. We'll map the fastest secure path from idea to production.