What this service solves
A recurring service for companies with AI agents, GPTs and automations already in production. We audit inference costs, output quality, latency, model drift, operational security and optimization opportunities, with monthly benchmarks and a continuous improvement roadmap.
Problems we solve
- Token costs spiraling out of control.
- Models that lose quality over time.
- A lack of proactive monitoring of AI flows.
- Security vulnerabilities in agents and APIs.
- No post-launch optimization roadmap.
What's included
- A technical audit of AI systems in production.
- Cost analysis by flow and model.
- Quality and accuracy benchmarking.
- Security testing (prompt injection, data leakage).
- A monthly optimization roadmap.
- Quarterly executive reporting.
How we work
- Onboarding on the current state.
- An initial deep audit.
- Implementing continuous monitoring.
- Monthly reviews with an action plan.
- Executive reporting.
How we work: service methodology
- Phase 1 · Consumption Profiling and Latency Diagnostics (Week 1) — Injecting observability tools (Langfuse / OpenTelemetry) to map the exact dollar cost and millisecond time of every step in the pipeline.
- Phase 2 · Prompt Optimization and Prompt Caching (Weeks 1-2) — Restructuring system prompts to take advantage of Anthropic and OpenAI caching, reducing read costs.
- Phase 3 · Intelligent Model Routing Architecture (Week 2) — Implementing routers (model cascading) that send simple tasks to fast, inexpensive models and reserve flagship models for complex reasoning only.
- Phase 4 · Vector Search and Streaming Optimization (Weeks 2-3) — Tuning vector indexes, reducing dimensions and configuring streaming responses for a sense of immediacy.
- Phase 5 · A/B Load Testing and Delivery of Results (Week 3) — A before-vs-after comparative demonstration (cost per call, response time and accuracy) with the report and optimized code delivered.
What we need from your team
- Read access to the AI pipeline's code repositories or a current architecture diagram.
- API billing history for the last 3 months and daily usage metrics.
- Access to OpenAI / Anthropic developer dashboards / backend servers.
Deliverables
- An initial audit report.
- Active monitoring dashboards.
- A monthly optimization roadmap.
- Quarterly executive reports.
- A continuous improvement plan.
Who it's for
- Companies with AI already in production.
- Technology teams with a significant AI budget.
- Organizations that require periodic external audits.
- Businesses where AI flows are critical to operations.
Signs you need it
- The monthly AI API bill has risen noticeably and nobody has reviewed why so many tokens are being consumed.
- Users complain that assistant or agent responses take several seconds to arrive.
- Nobody proactively monitors whether response quality has degraded over time.
- There's no visibility into possible security vulnerabilities in the AI flows already in production.
Measurable KPIs
- Cost per AI interaction.
- Quality and accuracy by flow.
- Latency and availability.
- Security incidents avoided.
Expected outcome
The company turns its AI investment into a governed, monitored system optimized month over month: controlled costs, sustained quality, active security and a continuous improvement roadmap that keeps the organization at the operational frontier of applied AI.
Technology stack
- LangSmith
- Helicone
- Datadog
- OpenAI Usage API
- Anthropic Console
- Prometheus
- Grafana
- n8n