System / evaluation layer online
Your AI agents aren't improving because you can't measure them.
We turn AI automation into a measurable, reliable system.
01 / THE PROBLEM
We'd never accept this for normal software.
How software ships
How AI agents ship
AI is measurable. The missing layer is evaluation.
02 / WHAT WE BUILD
The infrastructure behind reliable AI.
AI Automation
Deploy reliable workflows across support, documents, sales, and operations.
Evaluation Infrastructure
Measure every run with traces, scorecards, and regression gates.
The Improvement Loop
Find failures, ship targeted fixes, and prove each gain.
Cost Optimization
Route each task to the lowest-cost model that meets your quality bar.
Typical result: 40–70% lower API spend.
Cloud, hybrid, or fully on-premise — see deployment options.03 / WHAT THIS LOOKS LIKE
Sample artifacts from an engagement
Measure the baseline.
$ Prompt change #47 — golden dataset run
running 300 test cases...
✓ 284 pass / 16 fail
BLOCKED
⚠ Regression: refund edge-cases −12%
Catch breakage before release.
→ input: customer email #4821
→ model call: claude-sonnet (1.2s, 840 tok)
→ tool call: lookup_order(#59317) ✓
→ judge: PASS (0.94)
Record every run.
Projected spend: $8,400/mo → $3,100/mo — quality floor held on all categories.
Spend less without losing quality.
04 / HOW IT WORKS
Eight weeks from guesswork to evidence.
Week 1
Audit
Baseline quality and cost.
Weeks 2–3
Score
Build and validate the test set.
Weeks 4–5
Gate
Block regressions before release.
Weeks 6–8
Improve
Raise quality. Reduce spend.
05 / WHY THIS WORKS
Every vendor sells automation. We make it measurable.
Know your baseline — measure reality.
Gate every change — stop regressions.
Upgrade with evidence — not hope.
Cut costs safely — protect quality.
06 / WHO IT'S FOR
Built for the gap after the demo.
Companies with AI agents in production and no success metrics
Teams whose AI project stalled after the demo
Ops leaders who need reliability before scaling AI
07 / DEPLOYMENT OPTIONS
Runs where your data needs to live.
Same evaluation infrastructure, same quality gates — three ways to deploy the models behind it.
Cloud APIs
Frontier and open-weight models via hosted APIs (Anthropic, OpenAI, Groq, Together). Fastest to deploy, cheapest to operate — routing alone typically cuts spend 40–70%.
Best for: Most teams. No infrastructure, immediate savings.
Hybrid / Managed Routing
We run the routing layer: trivial tasks go to low-cost open-weight providers, deep reasoning stays on frontier models. Every route is validated against your golden dataset before it ships.
Best for: Teams that want the savings and quality guarantee without touching model ops.
On-Premise / Private
Open-weight models self-hosted on your GPUs or private cloud (vLLM), with the same evaluation harness and regression gates. Your data never leaves your environment.
Best for: Regulated data, strict residency requirements, or very high sustained volume.
We'll tell you which one the math actually supports — self-hosting only wins at high volume or hard data-residency constraints, and we'll show you the numbers either way.
08 / START HERE
Book an AI Audit
One week to measure quality, cost, and what to fix next.
Prefer email?
hello@cognosystechnologies.com