AI teams moving from pilot to production
Products with working model behavior but missing evaluation, monitoring, security, capacity, or release controls.
AI Infrastructure & Deployment
AI infrastructure that makes quality, cost, and failure visible.
Automiq builds the platform layer between AI experiments and dependable operations: secure model access, retrieval and data pipelines, evaluations, tracing, cost controls, deployment, scaling, and recovery.
Cloud-flexible engineering across AWS, Azure, Google Cloud, containers, data platforms, and major model providers.
Business outcome
Users & workflow
Systems & constraints
AI Infrastructure & Deployment
Working capability
Production controls
Owned handover
- Engineering judgment
- Product-led
- Decisions account for adoption, support, and maintenance
- Delivery ownership
- Senior
- Product and architecture stay close to implementation
- Production behavior
- Observable
- Failures and quality signals remain visible
- Handover objective
- Portable
- Agreed code, access, documentation, and runbooks
Best fit
Who this service is for.
Fit depends on the business problem, access to decision-makers and representative data, and willingness to own the resulting product or workflow.
Companies standardizing multiple AI features
Teams that need shared model access, policy, observability, cost allocation, and provider governance.
Regulated or cost-sensitive workloads
Systems requiring specific deployment, data boundary, audit, performance, or unit-economics decisions.
The problem
Why otherwise promising initiatives stall.
These failure modes are resolved before scale amplifies them.
Nobody can explain AI production cost
Tokens, models, retrieval, storage, GPU, queues, retries, and tenant usage are not attributed to a workflow or customer.
Quality regressions arrive silently
Prompt, model, data, or retrieval changes reach users without a repeatable evaluation gate.
The application is coupled to one provider
Provider-specific behavior leaks through the product, yet no abstraction or switching evaluation exists.
Operational signals stop at HTTP status
Teams see uptime but not model, prompt, source, tool, latency, safety, quality, or business outcome.
What we build
A complete production capability, not an isolated technical demo.
The exact scope is discovered with the customer; these are representative systems within this service.
Model gateways and policy
Centralized provider access, credentials, routing, quotas, structured contracts, audit, and usage attribution.
Retrieval and data pipelines
Ingestion, parsing, permissions, indexing, freshness, deletion, quality, and source operations.
Evaluation and observability platforms
Datasets, runs, regression gates, traces, prompt and model metadata, latency, cost, and feedback.
AI deployment and scaling
Containers, managed services, GPU or CPU inference, queues, autoscaling, release, rollback, backup, and recovery.
Practical use cases
Where this service creates useful leverage.
Use cases are selected by measurable workflow or product value—not by how fashionable the technology sounds.
Shared enterprise AI platform
Give product teams governed model and retrieval capabilities without duplicating security and operations.
Self-hosted or private inference
Operate open models when data, latency, availability, or unit economics justify the ownership burden.
Production RAG foundation
Build source ingestion, permission-aware retrieval, evaluation, monitoring, and content lifecycle operations.
LLM observability and cost control
Trace behavior and attribute latency, usage, failure, and cost to products, tenants, and workflow outcomes.
Deliverables and ownership
What a production engagement should leave behind.
The engagement agreement defines exact ownership, but the delivery objective is an operable system and a practical path forward.
Workload and deployment assessment
Traffic, latency, quality, data, risk, provider, location, availability, recovery, and team capability.
Infrastructure and delivery platform
Cloud resources, networking, identities, secrets, containers or services, CI/CD, environments, and infrastructure configuration.
AI operations controls
Gateway, evaluation, tracing, usage attribution, quotas, alerts, dashboards, release policy, and incident signals.
Runbooks and handover
Architecture decisions, access, deployment, rollback, scaling, backup, incident, cost, and maintenance procedures.
Example architecture
A representative flow buyers can reason about.
This is an explanatory pattern, not a promise to force every project into the same components.
- Stage 01
Applications
- Product and workflow clients
- Identity and tenant context
- Typed AI service contracts
- Stage 02
AI platform
- Gateway, routing, policy, and quota
- Retrieval, tools, and queues
- Caching and structured validation
- Stage 03
Providers & data
- Managed or self-hosted models
- Databases, object stores, indexes
- Authorized business systems
- Stage 04
Operations
- Evals, traces, latency, and cost
- Autoscaling and capacity
- Release, rollback, backup, incident
Build, buy, or integrate
When custom engineering makes sense—and when it does not.
A useful partner should help reject unnecessary custom work as clearly as it scopes justified work.
| Option | Best when | Main tradeoff |
|---|---|---|
| Direct managed model APIs | Speed, frontier capability, and low infrastructure ownership matter most. | Provider policy, availability, pricing, and data terms shape the system. |
| Cloud AI platforms | Enterprise identity, networking, governance, and consolidated cloud operations are priorities. | Stronger platform integration with cloud-specific complexity and cost. |
| Self-hosted models | Volume, latency, availability, customization, or data requirements justify dedicated ML operations. | Maximum control with capacity planning, model serving, patching, and quality ownership. |
Automiq is probably not the right fit when:
- A single low-risk feature works well through a direct provider API and has no shared-platform need.
- The team wants self-hosting for optics without a data, latency, cost, or availability case.
- There is no application owner or quality definition for the AI workloads the platform would serve.
- The organization cannot own ongoing cloud, security, model, data, and incident operations.
Delivery method
From evidence to production in reviewable increments.
The method scales to the work. A bounded integration uses a lighter version than a multi-workflow platform, but the control points remain visible.
- 01
Scope & discovery
Map users, workflows, constraints, success measures, and the smallest valuable production milestone.
Outcome: Prioritized scope and delivery plan
- 02
Data & architecture
Audit systems, integrations, data quality, security boundaries, and the architecture the future team can maintain.
Outcome: Architecture and risk register
- 03
Prototype & evaluate
Test the riskiest assumptions against representative data, measurable acceptance criteria, and real user feedback.
Outcome: Evidence-based go or adjust decision
- 04
Build & integrate
Ship in reviewable increments with testing, access controls, observability, documentation, and clear ownership.
Outcome: Production-ready software
- 05
Deploy & hand over
Release progressively, monitor real usage, train operators, and transfer repositories, infrastructure, and runbooks.
Outcome: Controlled launch and clean handover
- 06
Support & grow
Maintain reliability, refine workflows, manage dependencies, and keep shipping as the product and business evolve.
Outcome: A stable platform that keeps improving
Production safeguards
Failure handling is part of the feature.
Safeguards are selected by consequence and operating environment, then tested before broad release.
Identity and secret boundaries
Workloads use scoped roles, private connectivity where needed, managed secrets, and auditable access.
Evaluation release gates
Changes to models, prompts, retrieval, or policy must pass representative tests before broader exposure.
Capacity and cost protection
Quotas, budgets, caching, queues, timeouts, rate limits, autoscaling, and attribution contain runaway use.
Recovery engineering
Provider fallback where justified, backups, versioned configuration, staged deployment, rollback, and incident runbooks.
Technology
Tools selected for this workload—not a mandatory agency stack.
These technologies are relevant to the service. Final architecture depends on the customer’s existing environment, risk, team, and handover needs.
cloud data
AWS
AWS software and production AI development
Explore AWScloud data
Microsoft Azure
Azure cloud and production AI development
Explore Microsoft Azurecloud data
Google Cloud
Google Cloud software and AI deployment
Explore Google Clouddelivery
Docker
Docker application containerization and production delivery
Explore Dockerdelivery
Kubernetes
Kubernetes deployment and platform engineering
Explore Kubernetescloud data
PostgreSQL
PostgreSQL architecture, migration, and application development
Explore PostgreSQLai
OpenAI
Custom OpenAI development for production systems
Explore OpenAIai
Anthropic Claude
Anthropic Claude development and production integration
Explore Anthropic ClaudeInternational delivery
AI Infrastructure & Deployment across regions and operating markets.
Remote delivery is scoped around the customer's jurisdiction and operating language rather than assuming one global configuration.
Regional system terms
Align the names used by ai product teams and ctos for roles, records, states, dates, addresses, currencies, taxes, units, and exceptions.
Data and provider geography
Confirm hosting and model regions, data residency and transfers, subprocessors, customer access, retention, deletion, and recovery objectives.
Working model
Agree time-zone overlap, decision owners, language, procurement, release windows, incident escalation, support responsibility, and handover location.
Timeline
A sequence defined by evidence, dependencies, and risk.
Automiq does not publish one universal duration. Discovery establishes a bounded milestone and confirms the decisions required to reach it.
- Profile · 01
Measure workloads and constraints
Capture traffic, latency, quality, data, availability, risk, cost, and team requirements.
- Design · 02
Select deployment and control boundaries
Choose managed, cloud-platform, or self-hosted components and define identity, networking, evaluation, and operations.
- Implement · 03
Build platform and migration path
Provision environments, delivery, gateways, data pipelines, observability, cost controls, and application integration.
- Prove · 04
Load, fail, recover, and hand over
Test capacity, provider or component failure, rollback, backup, alerts, runbooks, and ownership before broad release.
Investment context
What changes the size of the engagement.
A credible estimate follows workflow, architecture, integration, data, risk, and release discovery—not a generic page-based package.
Avoid infrastructure before workload evidence
Start with the simplest secure managed architecture and add platform layers when repeated needs justify them.
Total cost includes the team
Cloud or GPU cost is only one part; model operations, security, incident response, evaluation, and maintenance matter.
Design for attributable unit economics
Measure cost per tenant, feature, workflow, or completed job so infrastructure decisions connect to product value.
No price or timeline on this page is a quote. Commercial scope is documented after discovery and depends on the agreed milestone and responsibilities.
Relevant experience
Product context behind the engineering approach.
ATZ CRM provides adjacent founder experience operating a multi-tenant SaaS product with automation, integrations, international use, and the need for continuing reliability and cost control.
Product visual
founded
ATZ CRM
Recruitment · B2B SaaS experience involving AI, Web app, Workflow automation, CRM integrations.
- Recruitment
- B2B SaaS
Questions, answered
AI Infrastructure & Deployment questions, answered
Direct answers about fit, architecture, ownership, risk, and delivery.
What is AI infrastructure?
AI infrastructure is the cloud, compute, data, model access, retrieval, evaluation, observability, security, deployment, and recovery foundation used to operate AI products and workflows reliably.
Do we need Kubernetes for an AI product?
Not automatically. Managed services, serverless functions, or simpler container platforms often fit early and moderate workloads. Kubernetes is justified when scale, workload diversity, portability, or internal operating capability outweigh its complexity.
When should a company self-host an AI model?
Self-hosting can make sense for high sustained volume, strict latency or availability, specific data controls, model customization, or unit economics. It also creates responsibility for serving, scaling, patching, monitoring, and quality.
What should AI observability capture?
Useful observability includes model and prompt version, retrieved sources, tool calls, structured output, errors, retries, latency, token or compute usage, user feedback, evaluation results, and the downstream workflow outcome.
How do you control AI infrastructure cost?
Use workload profiling, model routing, caching, batch processing, quotas, budgets, rate limits, token limits, autoscaling, scale-to-zero where appropriate, and cost attribution to products, tenants, and completed jobs.
Can Automiq deploy to our cloud account?
Yes, when access and responsibilities are agreed. Delivery can target customer-controlled AWS, Azure, Google Cloud, or another suitable environment, with infrastructure access, deployment documentation, and handover defined in scope.
Talk to the engineering team
Discuss a ai infrastructure & deployment requirement with the team.
Bring the current workflow, product, systems, constraints, and desired outcome. We will help define the first useful production milestone.