AI, Data & Intelligence
AI Solution Architecture
Architecture decisions for AI systems: which models to use and where they run, retrieval versus fine-tuning, data flows and permissions, latency and cost budgets, and how the whole system is evaluated.
Capability overview
What ai solution architecture involves
AI systems introduce architectural choices that teams have not faced before. Should a model be called through an API or hosted privately? Is retrieval-augmented generation enough, or does the task need fine-tuning? How are document permissions enforced when a model reads from a shared index? Each choice affects accuracy, cost, latency and compliance for years.
We document these choices as architecture decision records with the options considered and the evidence behind the chosen path. Designs cover the full system, from data ingestion and vector storage to model gateways, guardrails, observability and failure handling, and are reviewed with security and data protection teams early.
What is included
Architecture decisions we cover
Model selection and hosting
Hosted APIs, cloud model services such as Azure OpenAI, AWS Bedrock or Vertex AI, or self-hosted open-weight models compared on quality, cost, latency and data residency.
Knowledge strategy
Retrieval-augmented generation, fine-tuning, structured tool calls or a combination, chosen by testing against real tasks.
Data access and permissions
How source permissions carry through to indexes and answers, so users cannot retrieve content they could not open directly.
Non-functional budgets
Targets for response time, throughput, availability and cost per transaction, with the design sized to meet them.
Observability and evaluation design
Tracing of prompts, retrieved context and outputs, with offline and online evaluation built into the architecture.
How we work
How we deliver ai solution architecture
Requirements and constraints
Use cases, users, data classification, regulatory limits and integration points collected.
Spike experiments
Short technical experiments compare models and retrieval approaches on a representative sample of your tasks.
Reference architecture
Component diagram, data flows, trust boundaries and decision records produced for review.
Risk review
Security, privacy and failure scenarios walked through with the relevant teams, including prompt injection paths.
Build guidance
Architects support implementation teams and update the design as testing reveals new constraints.
Related capabilities
Related capabilities in Artificial Intelligence
AI Assistants
Rollout and governance of ready-made workplace AI assistants such as Microsoft 365 Copilot, Gemini for Google Workspace and ChatGPT Enterprise, so licences turn into real productivity rather than idle seats.
AI Agents
AI agents that complete defined tasks by calling your systems, such as updating a CRM record, triaging a ticket or reconciling a payment, with strict permissions, audit logs and human approval where it matters.
Agentic AI Systems
Multi-agent and long-running AI workflows that plan, delegate and coordinate across several systems and teams, with orchestration, shared state, checkpoints and governance designed for production.
Conversational AI
Customer-facing chat and voice assistants on your website, app, WhatsApp and phone lines that resolve common requests, speak your customers' languages and hand over to people with full context.
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Where this applies
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Questions & answers
Questions about AI Solution Architecture
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionRetrieval suits changing knowledge and cases where answers must cite sources. Fine-tuning suits consistent formats, specialised styles or narrow classification tasks. Many production systems use retrieval first and fine-tune smaller models later to reduce cost.
Often yes, with the right contractual and technical controls, such as enterprise agreements that exclude training on your data, India or regional hosting options and encryption. Some data may still require self-hosted models, which the architecture identifies.
Yes. An independent review typically takes one to two weeks and covers model choice, data access, security, evaluation and cost, with prioritised recommendations.
Architecture engagements are fixed price based on the number of use cases and integrations. Ongoing design authority during delivery can be provided on a monthly retainer.
Next step
Discuss ai solution architecture with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.