Questions buyers ask
What does forward deployment mean?
Forward deployment means engineers embed with your team and build the AI system inside your own cloud account, rather than selling a platform or a strategy deck. Solvren AI is an AI forward deployment company: we scope one production workflow, ship it in 6 to 8 weeks, and hand your team the code, infrastructure, and runbooks.
How do you deploy an LLM without sending data to external APIs?
Run the model and the retrieval stack inside your own cloud account, using a managed service your security team has already approved (such as Amazon Bedrock or Azure OpenAI in your tenancy) or an open-weight model on your own compute. Keep logs, vector stores, and prompts in the same account, and block outbound calls the system does not need. That is the default setup for every Solvren deployment.
How long does a secure RAG deployment take?
A scoped RAG system over one document set typically reaches production in 6 to 8 weeks: about two weeks for discovery and data access, three to four for building retrieval and evaluation, and the rest for security review and handover. The biggest variable is how quickly data access and security approvals happen, not the model work.
How long does an AI implementation take?
Our fixed-scope engagements run 6 to 8 weeks from kickoff to production. Larger programs are split into several 6 to 8 week deployments so each one ends with a working system.
How do you secure AI agent skills before agents use them?
Put every skill through a private registry that scans it before publish and on every new version, and block release when critical or high findings appear. We use NVIDIA SkillSpector for scanning and map findings to the OWASP Agentic Skills Top 10. See our AI Skills Registry case study for the full pipeline.
Can AI agents make payments safely?
Yes, if the application, not the model, owns approval. With Amazon Bedrock AgentCore Payments, we enforce approved recipients, a per-payment ceiling, and a session budget with an expiry, and check the agent’s answer against the actual receipt. Our agentic payments build ran on test networks with test-only funds.
What is an LLM router and why use one?
An LLM router is a small decision layer that sends each request to the right model, tool, or agent before any large model runs. It cuts the latency and cost of asking a large model to make that choice on every request.
Do you work with companies that have no AI experience?
Yes. Most clients are starting out, and our AI readiness audit identifies the one workflow worth deploying first.
What cloud platforms do you work with?
We build on AWS (Bedrock, SageMaker), Azure (OpenAI Service), and GCP (Vertex AI). Your data and systems stay in your cloud environment.
How do you handle data privacy and compliance?
All systems are built in your cloud environment, and your data never touches our infrastructure. We design to the requirements your auditors already use, such as HIPAA, SOC 2, and ITAR. Certification stays with your organization and its auditors; we give them the architecture, controls, and logs they need to review.
Do you offer ongoing support after launch?
Yes. Monthly retainers cover monitoring, prompt and model tuning, and new features. Scope and pricing depend on the system we support.
Related
- How we deploy
What forward deployment is and how a 6 to 8 week engagement runs.
- Securing agent skills
A scanned skills registry inside the client’s cloud.
- Controlled agent payments
Spending limits, receipts, and audit trails on AWS AgentCore.
- LLM routing
A fast router that decides before the large model runs.
Start with one workflow
Book a free 30-minute AI audit. We look at one workflow with you and tell you whether it can reach production in 6 to 8 weeks. No commitment, just 30 minutes with an engineer.
Book a free AI audit