One architecture, applied as advisory and as engineering.

From a two-week diagnostic to an ongoing build-out.

• 01

  AMEA Growth Audit

  A structured gap-analysis of your current operating model against the AMEA framework — where headcount is standing in for orchestration, where verification is missing, and what growth path actually exists. For an established business, that's usually a 3–4x path; for a startup or early-stage company with no legacy process to unwind, it can be considerably higher — 50–100x isn't unusual. The audit is the fastest way to find out which applies to you.

• 02

  Transformation Architecture Design

  We translate the audit into a practical operating blueprint: which functions move to agent orchestration, what data and retrieval layer they need, and what governance has to sit in front of them before anything goes live.

• 03

  Implementation & fractional advisory

  Ongoing engagement where we help build out the agreed architecture — using the same open-source, self-hostable stack described below — and stay on as a fractional advisor as the system, and the organization around it, matures.

Engagement scope and duration are set per client.

15 minutes — we'll tell you honestly if this is a fit before anything else.

A model call is the expensive option — we treat it that way.

Replacing headcount with agent orchestration only works if the new cost is genuinely lower and bounded. If every step in the process routes through a large model regardless of whether it needs to, the business hasn't cut cost — it's just swapped a labor bill for a token bill, and one that can grow just as unpredictably. Our architecture is built specifically to avoid that.

• Deterministic logic first, the model last

  In the IVT plugin, for example, a risk verdict is decided entirely by rule-based scoring blended with a lightweight anomaly-detection model — no LLM call in that path at all. A model is only invoked afterward, optionally, to turn an already-decided verdict into a human-readable explanation. If that call fails or times out, the verdict still returns. The model never sits on the critical path where a cheaper method would do.

• Routing to the cheapest sufficient model

  LiteLLM picks a model per request based on what the task actually needs — a frontier model for genuinely hard reasoning, a smaller or self-hosted open-weight model (via vLLM) for high-volume, low-complexity calls. Most requests in a production system are the latter, not the former.

• Retrieval instead of bigger prompts

  pgvector fetches only the relevant context for a given request instead of stuffing everything into the prompt. Smaller, targeted context windows mean fewer tokens per call, consistently, not just when someone remembers to optimize it.

• Validation to avoid paying twice

  Guardrails AI catches malformed or failed outputs before they reach the client — the alternative is a silent re-ask loop that quietly doubles or triples the token spend on a single request without anyone noticing until the bill arrives.

• Cost visible on every request, not just the invoice

  Langfuse logs token count, cost, and latency per call. That means compute spend is a monitored, bounded line item from day one — not something discovered after it's already scaled past the point of being fixable.

This is also part of what we check for in an AMEA Growth Audit: whether a proposed AI system is actually cheaper to run than the process it replaces, once every model call is accounted for — not just cheaper in headcount.

AI-first, not AdTech-first.

Hands-on experience running real-time DSP/SSP and OpenRTB infrastructure gives us an unusually direct proving ground for agentic AI specifically — not tooling in general. Three AI-native plugins are already live in production, with two more completing testing and shipping within weeks — all built on the same architecture above, and available through our engineering practice at reviveadservermod.com. Our third working paper benchmarks this approach directly against traditional ad-operations workflows — see Insights for the results.

• IVT / Fraud Detection

  Real-time invalid-traffic scoring on the bid path — rule-based weighted signals blended with an unsupervised anomaly layer, returning a Clean / Suspicious / Blocked verdict fast enough not to add latency to serving.

• AI Analytics Copilot

  A conversational front door to campaign data — plain-English questions answered from live metrics and campaign documents together, with every answer traceable back to its source.

• RTB Intelligence Engine

  Analysis across bid requests and responses — win rate, latency, timeout rate, and floor-price impact — surfaced as a plugin rather than a separate reporting stack.

• Banner Ad Generator

  Plain-English creative briefs turned into finished, pixel-exact banner ads across every standard IAB size, with instant revisions.

In focus: the MCP natural-language database agent

This is our clearest example of what "AI-first" means in practice. Instead of a person writing SQL, or a chart dashboard someone has to learn, a team member simply asks a question in plain English — the agent discovers the actual database schema itself, generates a read-only query against it, and answers in plain English. It never receives write access or database credentials, and every query is logged for audit.

Built on MCP (Model Context Protocol), with Gemini or Groq as the reasoning layer — the same request-validation and audit logging used across our production architecture applies here too.