How to Run AI Evals at a 20-Person SMB (Without a Data Science Team)

TL;DR

You don’t need a dedicated ML team to run AI evals. A 20-person shop can maintain a golden dataset of 50-100 cases, run weekly batch checks, and get Slack alerts on regressions for under $30/month. The goal is catching prompt drift and model changes before your customers do.

Why SMBs Skip AI Evals (and Regret It)

Most small teams ship an AI workflow, it works great for a few weeks, and then quietly starts misbehaving. A prompt gets tweaked. Anthropic ships a model update. The customer data starts looking slightly different than it did in January. Nobody notices until a client complains or you catch a bad batch of outputs manually.

That’s a monitoring problem, and it’s completely solvable without a data science team or a $500/month observability platform. For a broader view of how AI rollouts play out at this company size, see our piece on 50 SMB AI Rollouts: What Actually Happened.

The Real Cost of Skipping Evals

Skipping evals feels like saving time. In practice, it means discovering quality degradation through customer complaints, manual output reviews, or a frustrated employee flagging something that has been broken for weeks. The hidden cost is not the monitoring infrastructure; it is the hours spent firefighting problems that a simple weekly batch run would have surfaced on day one.

Prompt drift is the most common culprit. A prompt that worked in March may behave differently in June if the underlying model has been updated, if your input data distribution has shifted, or if a well-intentioned prompt edit introduced an unintended side effect. LLM evals exist precisely to detect these changes before they reach production outputs that touch customers or revenue.

What Triggers Degradation in Practice

Three patterns account for most quality regressions at SMB scale. First, model updates from providers like Anthropic or OpenAI can shift output behavior even when your prompt is unchanged. Second, prompt edits made without regression testing introduce new failure modes that only appear on edge cases. Third, input data drift occurs when real-world inputs gradually diverge from the examples your prompt was designed around. A lightweight eval stack catches all three patterns with the same weekly batch run.

What You Are Actually Monitoring

At a 20-person company running 3 AI workflows, you probably have something like: a proposal draft generator, a customer support triage classifier, and maybe an invoice or document extraction pipeline. Each one has inputs, a prompt, and expected outputs.

Evals ask one question: are the outputs still good?

“Good” means something specific for each workflow. For a classifier, it is accuracy against known labels. For a document extractor, it is whether the right fields get pulled. For a draft generator, it might be format compliance plus a human spot-check score. You define this once, upfront, and it saves you hours of manual review later.

Defining Pass and Fail Criteria

Before you write a single line of eval code, write down what pass and fail mean for each workflow. This sounds obvious and almost nobody does it before they start building. A classifier pass might be: correct label assigned, confidence above 0.8. A document extractor pass might be: all required fields populated, no hallucinated values in numeric fields. A draft generator pass might be: correct structure, pricing table present, no placeholders left unfilled.

Document these criteria in a simple table next to your golden dataset. When your pass rate drops, you want to know immediately which criterion is failing, not just that something is wrong. Specific failure definitions make your alerts actionable rather than just alarming.

Scoring Approaches for Different Output Types

Structured outputs (JSON, classified labels, extracted fields) are the easiest to evaluate. You compare programmatically: did the schema validate, did the right label appear, did the numeric fields fall within expected ranges. No model call required for the evaluation itself, which keeps costs low.

Text-heavy outputs like proposal drafts require a different approach. You have two practical options at SMB scale. The first is LLM-as-judge: send the output and expected output to a model like Claude Haiku with a scoring rubric and ask for a pass/fail verdict with a brief reason. The second is template comparison: flag outputs that deviate from a structural template (missing sections, wrong heading order, absent pricing table) and queue the flagged cases for a 2-minute human review. Both approaches work; the right choice depends on how much structure your output is supposed to have.

Building the Golden Dataset

A golden dataset is a spreadsheet or a database table with real examples from your actual workflow: inputs on one side, correct outputs on the other.

Fifty cases is enough to start. One hundred is solid. You are not trying to cover every edge case in the universe; you are trying to catch the regressions that matter most to your business. Pull your examples from production data, pick a mix of easy cases and tricky ones, and have whoever owns the workflow sign off on the expected outputs.

Store these in Supabase. The free tier handles thousands of rows, it is queryable, and you can connect it to whatever runs your eval script. A simple table with columns for input, expected_output, workflow_id, and created_at is all you need to start.

How to Select Good Golden Cases

Not all production examples are equally useful for a golden dataset. Aim for roughly 60 percent straightforward cases that represent your most common inputs, and 40 percent edge cases that have caused problems before or represent unusual but valid inputs. If you have had a regression in the past, the inputs that triggered it belong in your golden dataset permanently.

Refresh your golden dataset quarterly. Add new cases when you encounter an input type that the existing dataset does not cover. Remove cases that are no longer representative of your current input distribution. The dataset is a living artifact, not a one-time deliverable.

Storing and Versioning Your Dataset

Supabase works well for storage because it gives you a queryable table you can filter by workflow, by date, and by pass/fail status. Add a dataset_version column so you can track which cases were added when. If you ever need to audit why your pass rate changed over a particular period, knowing which cases were in the dataset at that time is valuable.

Keep a CSV export of your golden dataset in your code repository as a backup. This also makes it easy to run evals locally during development without hitting your production database.

The Weekly Batch Run

Once a week (via a cron job, an n8n schedule, or a GitHub Action), you run every golden case through your live workflow and compare the output to the expected result.

For structured outputs, comparison is automatic: did the JSON match the schema, did the right fields populate, did the classifier pick the right label? For text-heavy outputs, use either the LLM-as-judge approach or the template comparison approach described above.

The whole batch run for 100 cases across 3 workflows costs roughly $8-15 in API calls per month, depending on output length and which model you are using. That is the full infrastructure cost for continuous AI quality monitoring.

Setting Up the Scheduler

GitHub Actions is the simplest starting point if your team already uses GitHub. A weekly cron trigger, a Python script that reads from Supabase, runs the eval cases, writes results back to Supabase, and posts a Slack message takes under 50 lines of code total. The workflow YAML file lives in your repository, version control tracks any changes to the schedule or logic, and you get free execution minutes on the GitHub Actions free tier for this kind of lightweight script.

n8n is a better choice if your team prefers a visual workflow builder or if you are already using n8n for other automations. The eval runner becomes a scheduled n8n workflow that calls your Python eval script or runs the logic natively using n8n’s HTTP request and code nodes.

Interpreting Batch Results

After each batch run, write the results to a Supabase table with columns for run_date, workflow_id, total_cases, passed_cases, pass_rate, and failure_details (a JSON array of the cases that failed and why). This gives you a time-series view of your pass rate for each workflow, which is more useful than any single run result. A pass rate that has been declining gradually over four weeks tells a different story than a pass rate that dropped sharply in a single run.

Regression Alerting via Slack

When your pass rate drops below a threshold, you want to know immediately. Not in a weekly report email you will skim on Friday afternoon. Immediately.

A Slack webhook call takes about 10 lines of Python. Set a threshold per workflow (90% pass rate for your classifier, 85% for your extractor) and post a message when you fall below it. Include the pass rate, the number of failures, and a link to the Supabase table filtered to failing cases.

The alert should be actionable, not just informational. “Proposal draft workflow: 78% pass rate this week, 11 failures, 8 related to missing pricing table formatting” tells you exactly where to look. “AI quality degraded” tells you nothing.

Writing Alerts That Drive Action

The most useful Slack alerts include three things: the metric that triggered the alert, the delta from the previous run or from the threshold, and a direct link to the failing cases. If your alert requires someone to go find additional context before they can act, it will get ignored or deprioritized.

Assign ownership for each workflow’s alerts before you launch the system. The person who owns the proposal draft workflow should be the one who receives and is responsible for investigating its alerts. Shared alerts that go to a general channel with no clear owner tend to be acknowledged and not acted on.

Alert Thresholds and Tuning

Start with conservative thresholds (80% pass rate as a trigger) and tighten them over time as you understand your workflow’s normal variance. Some workflows are inherently more stable than others. A structured data extractor might reliably run at 95%+ pass rate, making an 85% alert meaningful. A creative draft generator might have more natural variance, requiring a lower threshold to avoid alert fatigue.

Review your alert thresholds quarterly alongside your golden dataset refresh. If you are getting alerts every week and they are always benign, your threshold is too tight. If you have never received an alert and you have been running for three months, either your workflows are genuinely stable (possible) or your pass/fail criteria are too lenient (more likely).

Comparing Your Options at SMB Scale {#eval-options-comparison}

If you are wondering whether to build this yourself or use a managed eval platform, here is the honest comparison:

Option	Monthly Cost	Setup Time	Best For
DIY (Supabase + Python + Slack)	Under $15	4-6 hours	Teams comfortable with basic scripting
LangSmith (LangChain)	$39+ per seat	2-3 hours	Teams already using LangChain in production
Braintrust	Free tier, then $150+/month	2-3 hours	Teams needing collaboration on eval scoring
Weights and Biases Weave	Free tier, usage-based	3-4 hours	Teams with existing W&B usage

For a 20-person SMB, the DIY stack wins on cost and control. LangSmith’s free tier is workable if you are already in that ecosystem; the full documentation is available at the LangSmith docs site. Braintrust’s paid tier prices out most SMBs before they get enough value to justify it. Weights and Biases Weave is worth considering if your team already uses W&B for other model tracking work, since the incremental setup cost is low.

When a Managed Platform Makes Sense

The DIY stack covers 90 percent of what a 20-person SMB needs. A managed platform starts to make sense when you have multiple people reviewing eval results and need shared annotation tools, when you want pre-built integrations with tracing and observability that go beyond pass/fail scoring, or when your eval complexity grows to the point where managing the infrastructure yourself becomes a meaningful time cost. None of those conditions typically apply in the first 12 months of running AI workflows at SMB scale.

Keeping It Under $30/Month

Here is the full cost breakdown for a realistic 3-workflow eval setup.

Weekly batch runs (100 cases x 3 workflows, Claude Haiku for judge calls) cost roughly $10-18 per month depending on output length. Supabase free tier covers this easily. Slack is free. GitHub Actions or n8n for scheduling are free tier or already part of your existing stack.

You can bring this under $20/month if you are thoughtful about which evals need LLM-as-judge versus simple schema matching. Save the expensive model calls for the workflows where output quality is hardest to check programmatically. A document extractor with clearly defined required fields can be evaluated with a schema check that costs a fraction of a cent per case. Reserve LLM-as-judge for your draft generator where quality is inherently more subjective.

Scaling Costs as You Grow

This cost structure scales predictably. Adding a fourth workflow adds roughly $3-5/month in API costs. Doubling your golden dataset from 100 to 200 cases per workflow roughly doubles the batch run cost, which is still well under $50/month for a comprehensive setup. The infrastructure cost (Supabase, Slack, GitHub Actions) stays flat regardless of how many workflows or cases you add. LLM API costs are the only meaningful variable, and they are easy to forecast once you know your average output length per workflow.

One Thing to Do This Week

Pull your 10 most recent outputs from whichever AI workflow matters most to your business. Label them pass or fail. That is the start of your golden dataset.

It takes an hour. You do not need a platform, a framework, or a dedicated engineer. You need a spreadsheet and an honest assessment of what “good” looks like for your specific workflow. Everything else (the batch runner, the alerts, the Supabase table) gets bolted on after you have that foundation.

If you want to move faster, pull 30 cases instead of 10. Add a column for failure reason next to your pass/fail label. By the time you have labeled 30 cases, you will have a clear picture of which failure modes are most common, and that picture will directly inform the pass/fail criteria you set for your automated eval.

The Bottom Line

AI evals at SMB scale are not a research project. They are a 4-hour build that runs on autopilot for $20/month. If you are running AI workflows that touch customers or revenue, you need to know when they degrade because of prompt drift, model updates, or data distribution shifts, and you need to know before someone complains.

Build the golden dataset first, automate the weekly run second, and add Slack alerting third. That sequence gets you from zero to monitored in a single week, with no ML team, no expensive platform, and no ongoing maintenance burden beyond a quarterly dataset refresh.

The teams that skip this step do not save time. They trade 4 hours of setup for hours of reactive firefighting spread across months of production issues. The teams that build it in week one spend those same hours shipping new workflows instead.

Kreante helps SMB owners replace expensive SaaS with custom AI tools. We have shipped 265+ projects (60% LowCode/AI, 70% B2B) for clients across the US, Europe, and LATAM. If you want help building an eval stack like this for your workflows, book a 30-minute consultation below.

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