Keep your AI running, secure, and cost-efficient.
AI Ops services.
LLMOps & observability
Tracing, evaluation pipelines, prompt management, and dashboards. Know which prompt, which model, and which retrieval call answered every request.
Agent observability
Trace multi-agent runs end-to-end. See decisions, tool calls, handoffs, and failures — debug agents the same way you debug distributed systems.
AI FinOps
Token usage analytics, model routing, prompt caching, and quota optimization. We typically cut LLM bills 30–60% on first engagement.
Security & OWASP agentic Top 10
Adversarial testing, prompt-injection mitigations, Microsoft's open-source Agent Governance Toolkit (sub-millisecond policy enforcement), PII redaction, and Entra Agent ID identity governance for every agent.
Eval-driven regression
Continuous evaluation suites that catch model drift and prompt regressions before users do — built into your CI/CD.
Managed AI services
We operate your AI in production: monitoring, on-call, optimization, model upgrades, and quarterly business reviews.
What you walk away with.
- 30–60% reduction in LLM cost via Foundry Model Router and prompt caching
- End-to-end tracing across your agent and grounded-generation stack
- Continuous evaluation suite integrated into CI/CD
- Security posture aligned to Microsoft Responsible AI + OWASP agentic Top 10
- Quality and FinOps dashboards for leadership
- ISO 42001 / SOC 2 Type II / EU AI Act readiness pack
Audit. Instrument. Optimize. Operate.
- 01Week 1
Audit
Inspect what's running today. Token usage, latency, quality, security posture, and gaps in observability and evals.
Output
AI Ops audit report
- 02Weeks 2–3
Instrument
Tracing, evals, dashboards, and alerts. We instrument what's missing and wire signals into the tools your teams already use.
Output
Observability platform
- 03Weeks 4–5
Optimize
FinOps wins (model routing, caching, prompt compression), latency tuning, and security hardening.
Output
Optimization playbook
- 04ongoing
Operate
Managed services or knowledge transfer. We can run it for you or stay close as a strategic partner.
Output
Production runbook
Common questions.
- How do you typically cut LLM cost?
- Foundry Model Router as the spine — automatic routing across GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, Grok 4.2, and Microsoft's MAI models based on cost/quality skew. Plus Foundry Local + Phi-4 for sovereign / on-device workloads with zero per-token cost. The big wins come from architecture changes, not negotiation.
- What's different about agent observability vs. LLM observability?
- LLM tracing captures one model call. Agent observability captures the full decision tree — which agent, which tool, why a handoff happened, why an action was taken. We use OpenTelemetry-based tracing native to the Microsoft Agent Framework.
- Do you do red teaming?
- Yes — adversarial prompt-injection testing, jailbreak attempts, data exfiltration probes, and content safety stress tests. We deliver findings with prioritized remediation, not just a list of vulnerabilities.
- Can you operate our AI for us?
- Yes. Managed AI services are a separate engagement model — we cover monitoring, on-call, optimization, and quarterly reviews. Pricing scales with workload, not headcount.
AI in production, but bleeding budget?
A discovery brief is enough to scope the FinOps and ops wins on the table.