Managed AI platforms
Enterprise AI with clear data-handling controls
Approved model access, documented processing and retention, and AI-use governance for financial firms. Begin with a bounded use case and evaluate it before scaling.
Enterprise AI with documented data and access controls
Investment teams want AI in their research and operations workflow without handing proprietary data to a public model. Option One Technologies runs managed enterprise AI platforms, primarily Amazon Bedrock, with documented access, processing and retention arrangements, governance, and an acceptable-use policy for your compliance team to review.
Controlled model access
Configure approved models and AWS regions through Amazon Bedrock, with private connectivity where scoped. Processing locations, retention and data handling follow the selected service and provider terms, rather than an assumption that all computation takes place inside your VPC. We document those arrangements before client data is introduced.
Retention & records controls
Configurable prompt and output retention, automated export of AI interactions into your archive of record, and a retention design that distinguishes vendor-side retention from your own capture obligations.
Governance & acceptable use
Acceptable AI Use Policy, approved use-case register, role-based access to models and data sources, and vendor due-diligence documentation for every AI platform before it touches client data.
Assistants over your own data
Retrieval over approved document sets such as research libraries, IC memos, and policy repositories, with per-source permissions inherited from your identity platform.
Monitoring & cost governance
Usage, cost, and consumption dashboards by team and use case, with guardrails and budget alerts so AI spend stays predictable.
Operations & support
Platform build, model enablement, integration with M365 and internal systems, and ongoing management by the same team that runs your cloud.
Start with one team and one approved use case
Agree a pilot scope and deliverables before committing to a wider platform rollout.
Choose one use case
Start with a research library, internal policy assistant, or an operations workflow. Agree the users, approved documents and exclusions before building.
Define the controls
Document model and region selection, provider data handling, access permissions, retention, usage limits and the approval process with your compliance team.
Evaluate before expanding
Review answer quality, source attribution, access boundaries, operating cost and user feedback against agreed success criteria. Wider rollout follows a separate decision.
The proposed scope identifies the platform configuration, access and retention documentation, evaluation results, support responsibilities and next-step recommendation. Management fees and model consumption are priced separately.
Talk to our teamGetting AI past your compliance team
Where your data actually goes, which models are in scope, and how a pilot stays bounded.
Where does our data actually go?
Requests are processed through the selected Amazon Bedrock models and AWS regions. Private connectivity governs access, but does not mean the model computes inside your own VPC. We document processing locations, provider terms, retention settings and any archive exports in the agreed design; your firm determines its capture and supervision obligations.
Which models can we use?
Leading foundation models available through Amazon Bedrock, enabled selectively rather than opening everything by default, with role-based access to models and data sources per team and use case.
How do you keep AI usage defensible in an exam?
An Acceptable AI Use Policy, an approved use-case register, role-based access, retrieval restricted to approved document sets with permissions inherited from your identity platform, and vendor due-diligence documentation for every AI platform in scope.
What will this cost?
Platform management is billed as a predictable monthly fee and model consumption is passed through at cost, with usage, cost and consumption dashboards by team and use case plus budget alerts so spend does not drift.
Can we start small?
Yes, and we recommend it: a bounded first use case with one team and an approved document set, reviewed against your policy before wider rollout.
Next step
Talk through your firm's next technology decision.
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