AI Model & Architecture Selection
Compare practical AI architecture options — hosted or private models, RAG or direct prompting, workflow automation or agentic behaviour, and the surrounding data and integration design — against your real requirements.
The problem
Teams often start with whichever AI model or framework is easiest to demo, then discover later that quality, privacy, latency, cost, or maintainability does not fit production use.
Who it's for
Companies planning a meaningful AI build, replacing an unstable prototype, or deciding between hosted AI providers, open-source models, private deployment, RAG, and more deterministic workflow designs.
The outcome
A documented architecture decision with the important trade-offs visible: quality, privacy, latency, operating cost, implementation complexity, maintenance, and vendor dependence.
What STYD delivers
STYD gathers the use-case and non-functional requirements, reviews the available data and integrations, compares the realistic architecture and model options, runs focused tests where needed, and recommends a primary design plus sensible fallbacks or boundaries.
Pricing
Typical starting point — final scope and price are confirmed after a short review of your setup.
This service is available — tell us about your setup and we'll confirm scope, hosting, and pricing.
Available as a practical technical audit with optional roadmap and implementation support.
FAQ
No. The recommendation is based on the workload and constraints. Hosted and open-source options can both be considered where they are realistic.
When quality differences matter, focused tests can be run on representative examples. The scope is agreed first so the comparison measures the client's real task rather than generic benchmarks.
No. It is also useful when an existing AI prototype is too expensive, unreliable, slow, difficult to maintain, or uncomfortable from a privacy perspective.
No by default. It produces the architecture decision and implementation direction; the build can then be scoped separately.
Related STYD services
Find the AI opportunities worth pursuing first — ranked by business value, data readiness, implementation effort, and risk — before spending on a build.
Find out why the numbers never match: where each report comes from, which are trusted, which are manual — and what must be cleaned or connected before reporting can be believed.
A practical review of how your company actually runs on its tools — workflows, reports, automations, integrations, hosting — ending in a prioritised list of what to fix first.
Tell us about your setup and we'll confirm fit, scope, and pricing.