Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.
vLLM / TGI / SGLang for open-weight models; gateway + caching layer for hosted-API models. Both behind a single OpenAI-compatible interface.
Per-request traces, token spend, latency histograms, output-quality flags. Wired into Datadog / Grafana / your existing observability.
Every model upgrade or prompt change runs the eval suite before promotion. Regressions block the deploy. No silent quality drops.
Per-team / per-feature budgets, alerts at 80%, hard caps at 100%, weekly cost-by-feature breakdowns to your finance team.
Map your current AI surfaces, models, data flows, and ops runbooks. Identify the gaps that'd burn you in an audit.
Set up serving, observability, eval CI, secrets, ACLs. Migrate one workload as the reference implementation.
Monthly retainer: model upgrades, drift monitoring, cost optimisation, on-call escalation, quarterly DR drills.
Best-in-class where it matters; boring and battle-tested everywhere else.
Cross-functional pod (1 platform eng, 1 ML eng, 1 SRE, 1 PM). Roll on/off monthly. Cloud spend passthrough at cost. Long-term clients see 25–40% cost-per-call reduction within 6 months.
Both, depending on workload. Hosted (OpenAI / Bedrock / Vertex) for low-volume / high-quality. Self-hosted (vLLM) for high-volume / cost-sensitive / strict-data-residency. The gateway makes the choice transparent to the app.
Yes. We've deployed in BFSI on-prem datacenters with GPU clusters, behind air-gapped networks. The MLOps layer is the same; just heavier auth and patch management.
We engineer to those standards by default — audit logs, access controls, encryption at rest and in transit, evidence collection. Auditor-ready out of the box.
Yes, but as an upgrade — base retainer covers business hours. 24/7 adds a second pod with rotational coverage.