AI, Data & Intelligence
AI Deployment
Taking an AI system from a successful pilot to dependable production: hosting, scaling, security review, monitoring, rollback and the operational handover that pilots usually skip.
Capability overview
What ai deployment involves
Many AI pilots succeed in a demo and stall before production. The obstacles are operational: the prototype runs on a data scientist's account, has no access controls, costs are unknown at real volumes, nobody is on call and the security team has not reviewed it. AI deployment closes those gaps.
We harden the system for production, choose hosting that fits usage, whether managed model services, serverless inference or GPU instances running engines such as vLLM for self-hosted models, and put monitoring for quality, latency, errors and spend in place before launch. Rollback plans are tested, not assumed.
What is included
Deployment workstreams
Production hardening
Authentication, secrets management, input validation, rate limiting and structured logging added to pilot code.
Hosting and capacity
Throughput and latency targets translated into infrastructure sizing, including GPU type and count for self-hosted models.
Security and privacy review
Threat model, data protection impact assessment inputs and penetration testing of exposed endpoints before go-live.
Release strategy
Shadow, canary or staged rollout with feature flags so the AI can be switched off without redeploying.
Operational handover
Runbooks, dashboards, alert routing and support ownership agreed with the team that will run the system.
How we work
How we deliver ai deployment
Pilot assessment
Code, data flows, dependencies and results of the pilot reviewed to list production gaps.
Readiness plan
Gaps prioritised into must-fix items for launch and improvements for later releases.
Hardening sprint
Engineering work completed and infrastructure built as code in production-like environments.
Load and failure tests
Realistic traffic simulated and model provider outages or slow responses injected to confirm graceful handling.
Go-live and hypercare
Staged release with daily reviews of quality and cost for the first weeks, then transition to steady-state support.
Related capabilities
Related capabilities in Artificial Intelligence
AI Proofs of Concept
Time-boxed experiments, usually four to six weeks, that test whether an AI idea works on your real data and processes, with success criteria agreed up front and an honest go or no-go recommendation.
AI Strategy & Consulting
A clear-eyed plan for where AI will create value in your organisation, what data and governance it needs, and which use cases to fund first, without chasing every new model release.
Enterprise AI Solutions
AI applied to recurring enterprise problems such as document-heavy processes, service desks, sales forecasting and knowledge retrieval, rolled out across departments on a shared, governed platform.
Custom AI Development
Bespoke machine learning, computer vision and language models trained on your proprietary data for problems that off-the-shelf AI products and general-purpose models cannot solve well enough.
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Questions & answers
Questions about AI Deployment
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionManaged services are simpler and scale automatically, which suits most organisations. Self-hosting makes sense for strict data residency, very high steady volumes where GPUs stay busy, or specialised open models.
We measure token or compute usage per request during load testing, multiply by forecast volumes and add infrastructure and monitoring costs, giving a range for different adoption scenarios.
AI deployment is a project to bring a specific system into production. MLOps is the ongoing platform and practice for training, deploying and monitoring many models over their lifetime.
For a pilot with reasonable code quality, four to eight weeks is typical. Pilots built as throwaway prototypes may need partial rebuilding, which the assessment identifies up front.
Next step
Discuss ai deployment with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.