Let’s build secure AI that works inside your enterprise.
Start with a free 30-minute discovery call. We will walk through your workflows, data environment, infrastructure preferences, and automation priorities — then identify where AI creates real return and design a practical path to production.
The questions that decide the deal.
Reliability is an architecture problem before it is a model problem. We constrain agents with deterministic guardrails, scope every tool to a declared purpose, require explicit human approval for irreversible actions, and log every reasoning step immutably so a failure is diagnosable rather than mysterious. Anomaly detection runs over the reasoning trace, not only the final answer. Where a workflow cannot meet a reliability bar you need, we say so during the assessment — before the build starts.
Not yet, and we would rather tell you that than point at someone else’s numbers. Synthora AI Labs was incorporated in 2026 and we do not have published client case studies. What we do instead: scope one specific workflow with you, agree the measurement up front — hours, error rate, throughput, cost avoided — and hold the build to it. If the return is not there, you hear it during the assessment rather than after the invoice.
The model weights, your documents, the vector embeddings, the reasoning traces, and the audit logs all stay inside your boundary. No outbound network path is required for the system to function. Components authenticate to each other over mTLS, every interface is behind RBAC, and audit logging is immutable. Fully air-gapped deployment is supported. This is a property of the architecture, not a contractual promise about where a server happens to sit.
It depends on what the agent has to do. Most deployments end up as a mixture-of-experts configuration with several LLM and ML models working together behind a single interface, chosen against the requirements of that specific agent rather than a house preference. On the infrastructure side we standardize on Docker and Kubernetes, with LangGraph for agent orchestration.
Every engagement is scoped to the project, so we do not publish a rate card — the honest number depends on the workflows, the data, and the infrastructure you already run. What we will commit to is that there is no per-token meter. Token volume becomes a hardware sizing decision made once, which is what makes a three-year total cost of ownership predictable.
No. We start with a free 30-minute discovery call, and where there is a fit, a paid assessment that produces an operations audit and a prioritized roadmap you own outright — whether or not you continue with us for the build.
In our experience the champions are the CIO and CTO, and the hard questions come from the CFO and CEO — cost predictability and demonstrable business benefit. The security review is usually mandatory and is where most AI vendors stall, which is why our security architecture is written down and available on request rather than described in a sales call.