The ForthClover Reliability Method

Production AI is a systems problem, not a model problem.

Every offer on our shelf — the $3,000 pilot, the audit, the builds, the operations retainer — is delivered on the same architecture: three layers and a learning loop, each one aimed at a documented reason AI projects fail. This page is the only place we talk architecture. Everywhere else, you'll see prices and results.

The Reliability Method

The Flight Recorder

Evals · tracing · guardrails · cost alerts

Working Agents

One real workflow, in your real systems

Ground Truth

Data your AI can trust, permissioned

The Learning Loop — feedback from month six flows back into layer one. The system improves; it doesn't decay.

Layer 1

Ground Truth

Your AI is only as good as the data it can trust — so we build the single source of truth first.

Why it exists: Industry cause #1 of abandoned AI projects: poor data quality. Practitioners call data engineering “80% of the real work” of AI.

Under the hood

Ingestion pipelines, entity resolution, document processing, vector search (pgvector, Pinecone, Qdrant), knowledge graphs and GraphRAG where the data volume justifies them, quality gates and permissioning.

The artifact you receive

Data Readiness Scorecard — red / yellow / green, per source

Layer 2

Working Agents

AI teammates that execute one real workflow in your real systems — with your people in the loop.

Why it exists: The demo-to-production gap: the average organization scraps nearly half its AI proofs-of-concept before they ever reach production.

Under the hood

LangGraph and DSPy orchestration, Bedrock-first on AWS (vendor-neutral by request), tool calling into your CRM/ERP/APIs, human-in-the-loop escalation paths, graceful failure modes.

The artifact you receive

Workflow Blueprint (before/after) + Agent Runbook

Layer 3

The Flight Recorder

Proof it works today, and evidence it still works in month six — on a scoreboard you can read.

Why it exists: Industry cause #2: inadequate risk controls — and ~60% of companies deploying AI can't show measurable improvement because nobody measured a baseline.

Under the hood

Evaluation suites that run as CI gates, Langfuse/LangSmith tracing, guardrails, drift detection, cost dashboards and budget alerts, audit trails.

The artifact you receive

Live Scoreboard — baseline vs. actual, updated weekly

The Loop That Connects Them

The Learning Loop

MIT's widely-reported research on why ~95% of GenAI pilots show no P&L impact points at one barrier above all: generic tools don't retain feedback, adapt to context, or improve over time. Our systems do — your team's corrections are captured, fed into the eval suite, and turned into monthly, measurable improvement. That's the structural answer to “why not just build it ourselves with AI coding tools?” A weekend build can write the code. It can't make the system learn your business.

The Capabilities Underneath

The engineering disciplines behind every rung.

These aren't things you buy off a menu — they're the skills we bring to whichever offer you start with. Deep-dives for the technically curious:

AI Agents RAG & Embeddings Document Processing Fine-Tuning & Inference Cloud Migration DevOps & MLOps AI Strategy

Delivery gravity: AWS and Amazon Bedrock, where nearly all of our production systems run — with Azure and GCP by request. Everything ships as infrastructure-as-code (Terraform/CDK) in your cloud account, with your repo as the only home the code ever has.

The method is the same on every engagement. Pick your door.

$3,000 Workflow Pilot $7,500 Readiness Audit Book a Fit Call