Book a call

AI consulting

AI that satisfies. Finally.

We build the layer between human intent and model behavior — evals, system prompt architecture, agent loops, context budgeting. The unglamorous part nobody puts in the tutorial, because it's hard and unsexy and completely load-bearing.

Book a call Send an email

For teams that have already shipped something AI-adjacent and are staring at failure modes they didn't anticipate.

The problem

  1. Your demo works. Your product doesn't.

    The model aced your test cases. It's the cases you didn't write tests for that are currently opening tickets in your support queue. Real users have a talent for finding the edge of whatever envelope you built.

  2. You're prompting, not engineering.

    Tweaking system prompts until something works is vibes-based engineering. It holds until it doesn't, and when it breaks you won't know which of seventeen recent changes caused it — because you have no evals. You can't iterate safely on a system you can't measure.

  3. Your team is good. This layer is just new.

    The gap between LLM capability and LLM reliability is a real engineering discipline — it's just a very young one. Nobody's team has ten years of eval-harness experience, because eval harnesses for LLMs didn't exist ten years ago. You're not behind. You're just learning it the expensive way, on real users.

What we do

  • Audit

    We look at your existing AI system with fresh eyes and something you probably don't have: an eval suite built against your actual failure cases. Two weeks, fixed scope. You get a prioritised list of what's broken, why, and what to do about it.

  • Embedded

    One of us joins your team for a sprint or a quarter. We build the reliability layer from inside — eval harness, prompt versioning, agent loop design, context budgeting. We leave you with a system that doesn't require us to keep working.

  • Cohort

    A small group of engineering leads working through the same problems together. Not a course — a workshop, with your actual production incidents as the case studies. Cohorts run quarterly. Application required.

Capability

Case 01 / Document intelligence

From 62% to 94% extraction accuracy

A fintech team had an LLM-powered document parser that passed QA in staging. In production, edge-case documents caused silent misclassifications that made it downstream before anyone noticed. (Silent failures are the worst kind — they're polite enough not to alert you while they're breaking things.) We built a behavioural eval suite against 800 real documents, redesigned the extraction prompt architecture with explicit fallback contracts, and shipped a monitoring harness that catches drift before users do.

+32pp accuracy  /  0 silent failures in 6 months

Case 02 / Agent reliability

Taming a multi-step agent that worked 70% of the time

An enterprise SaaS team had an internal agent for data analysis tasks. Performed brilliantly in demos. Failed unpredictably on real workloads — which is a very specific kind of awful, because you've already shown customers the demo. We audited the agent loop, identified seven distinct failure modes in the tool-use patterns, and redesigned the context handoff and error recovery architecture. "Good enough to demo" became "good enough to bill customers for."

70% → 97% task completion rate

About

We've shipped AI in production. Not consulted on it — built it, deployed it, watched it fail in ways the demo absolutely did not predict, and fixed it. That's the relevant experience here.

Satisfies is a small practice. We keep a short client list intentionally — this work requires actual attention, and we're not interested in spreading it thin. We don't do strategy decks. We do evals, architecture, and code. The name is from formal logic: m ⊧ α — a model satisfies a specification. That's the goal. Systems that actually do what they're specified to do. It's a higher bar than it sounds.

Ready to talk?

If your AI works in staging and fails in production, we should talk.

No pitch deck, no sales call. Thirty minutes, your actual system, and an honest answer — whether that answer is "here's what to fix" or "here's what you should do instead of calling us."

Book a 30-min call satisfies@crafton.dev