LLM Feature Evaluation & Quality Gate

A representative engagement: take one AI feature you're not yet confident shipping, build the evaluation and CI gate that keeps it honest, and hand you a repeatable quality system your team can run without me. This document shows exactly how I scope, build, and prove the work.

Parties & engagement

Provider	Sage Ideas LLC — Jason Teixeira (hello@sageideas.dev)
Client	[Client legal name]
Engagement type	Fixed-scope project · one AI feature · your repository
Estimated duration	~4 weeks from kickoff (see §5)
Working model	Remote · async-first with a weekly checkpoint call · your stack, your git

Objective & success criteria

The problem this solves. Most teams ship an AI feature and then hope it behaves. "It seems to work" is not a release criterion — and it fails quietly, in front of customers, the first time a prompt, a model version, or a retrieval index drifts.

What "done" means here. The engagement is successful when every one of these is true and demonstrable:

A golden evaluation set exists for the feature, with pass/fail scoring anyone on your team can read.
A CI gate blocks a merge when quality, safety, or cost regresses past a threshold you set.
The feature ships with a safety battery covering hallucination, prompt injection, PII leakage, and toxicity.
Your team can run and extend the whole system from a documented runbook — no dependency on me.

Scope of work

Delivered in four phases, each ending in something you can see and verify — not a status update.

01Discovery & risk map~week 1

Review the feature, its prompts, data flow, and failure modes. Agree the quality dimensions that matter (accuracy, grounding, safety, latency, cost) and the thresholds that define "shippable." Produce a one-page risk model so coverage tracks real risk, not guesswork.

DeliverableRisk model + evaluation plan (agreed thresholds)

02Build the evaluation harness~weeks 1–2

Author the golden set from real and adversarial inputs. Implement scoring (deterministic checks + LLM-as-judge where judgment is needed), the safety battery, and per-run quality/latency/cost measurement. Every check is version-controlled in your repo.

DeliverableGolden set + scoring harness + safety battery, in your repo

03Wire the CI gate~week 3

Connect the harness to your CI so a pull request that regresses quality, safety, or cost is blocked with a readable report. Add a ratchet so the bar only moves up. Prove it works by making it fail on purpose, then pass.

DeliverablePassing CI gate + a captured red→green run proving it fires

04Handoff & enablement~week 4

Write the runbook: how to add a case, read a failure, tune a threshold, and interpret the gate. Walk your team through it live. The goal is that the system outlives the engagement and needs no consultant to operate.

DeliverableRunbook + recorded walkthrough + handoff session

Deliverables summary

Deliverable	Form	Acceptance
Risk model & evaluation plan	Document, agreed thresholds	Signed off in the week-1 checkpoint
Golden evaluation set	Versioned data in your repo	Covers the agreed dimensions
Scoring harness + safety battery	Code in your repo	Runs locally & in CI, green
CI quality gate	CI workflow	Blocks a seeded regression; passes clean
Runbook + walkthrough	Docs + recording	Your team runs it unaided

Timeline

Week	Focus	Checkpoint
1	Discovery, risk map, evaluation plan; start the golden set	Plan sign-off
2	Harness, scoring, safety battery	Harness demo
3	CI gate + ratchet; prove it fires	Red→green captured
4	Runbook, enablement, handoff	Final acceptance

Timeline assumes timely access (repo, CI, a representative environment) and one client point of contact for decisions. Slippage in access shifts the schedule, not the scope.

Out of scope

To keep the engagement fixed and honest, the following are explicitly excluded unless added in writing: building the underlying feature itself; model training or fine-tuning; production infrastructure changes; ongoing on-call or monitoring (available separately as a retainer); and evaluation of features beyond the one named in §1.

Assumptions & client responsibilities

Timely access to the repository, CI system, and a representative test environment.
One empowered point of contact for scope and threshold decisions.
Existing feature is functional enough to evaluate (this engagement measures and gates it, it does not build it).
Any third-party API costs incurred during evaluation are the client's (typically minimal).

Commercials

This engagement is quoted as a fixed price after a scoping call — you approve the number before any work starts, in writing. Indicative range for a single-feature evaluation & gate:

Component	Model	Indicative
AI Quality Audit (entry point)	Fixed · credits into the build	$497
Full evaluation & CI gate build	Fixed-scope project	$8k–$18k
Ongoing quality retainer	Monthly · optional	$2.5k–$4k / mo

Indicative ranges for illustration only; not an offer. Deposit of 30% books the engagement; balance on final acceptance. Exact figures are set in the project SOW after a call.

Next step

One 15-minute call turns this sample into a real, fixed quote for your feature — with a plan you keep either way. agency.sageideas.dev/book.html

Jason Teixeira

Sage Ideas LLC · Provider

Signature & date

[Client signatory]

[Client legal name] · Client

Signature & date

This sample is provided for illustration and does not itself create a binding agreement. A project-specific Statement of Work, executed under a Master Services Agreement, governs any engagement.

Sample SOW — download PDF ↓ · ← portfolio