Eval-driven AI & data engineering studio

You shipped AI features. The cost, the quality, and the ROI are still guesswork.

Synthorna wires evaluation, cost attribution, and regression tests into the AI you already run in production — so quality and spend stop being a guess.

Let's talk Read our open toolkit

Two senior data engineers EU-based, fully remote Async-first Fixed-fee

eval-report.ci — illustrative

gates: your bar

faithfulness vs. your thresholdmeets bar ✓

spend on requests needing full modelattributable

regression tests wired into CIgreen ✓

// schematic. thresholds are yours; we enforce them in CI. real numbers come from your stack.

What we make measurable

faithfulness cost-per-call answer quality retrieval drift spend per route latency regression gates PII coverage faithfulness cost-per-call answer quality retrieval drift spend per route latency regression gates PII coverage

01 / What we do

Three ways we turn AI guesswork into numbers

Each one is a fixed scope with a number attached. We instrument what you already run, not what we wish you ran.

LLM Cost & Quality Audit

We trace your live LLM traffic, attribute spend per route and model, and surface the requests that don't need the full model — then quantify the quality trade-off before you change anything.

your bill, mapped

RAG & AI evaluation suites

An eval harness for retrieval and generation — faithfulness, answer quality — wired into CI so regressions fail the build, not reach your users. You set the thresholds; we enforce them.

regressions fail the build

Data & PII pipeline engineering

Lakehouse pipelines with lineage you can read, and PII redaction, detection, and retention built into the flow — not bolted on after a scare. Eight years of this is the boring part we're good at.

lineage you can read

02 / Who this is for

B2B SaaS that shipped AI — and can't put numbers on it

Series A–C SaaS and capable bootstrappers with AI features in production but no honest read on quality or cost. There are two doors in.

The LLM bill keeps climbing and nobody can say why.

You're shipping AI to customers, the invoice grows every month, and "is it worth it?" has no answer. We trace it, attribute it, and show you what you can cut without the quality dropping.

Start with the LLM Cost & Quality Audit

We're not even sure where AI fits yet.

You suspect there's leverage in AI but you don't want a six-month moonshot to find out. A lighter AI Opportunity Audit maps where it pays off — and, just as usefully, where it won't.

Start with a lighter AI Opportunity Audit

Rough routing signal: if your LLM bill is climbing past ~$20k/month, the Cost & Quality Audit is the sharp tool. Earlier than that, the Opportunity Audit or our open toolkit is the better first step — not a gate, just a pointer.

03 / How we work

Four principles, no fine print

The way we engage is the product as much as the code is. Here's the whole contract in plain terms.

Productized & fixed-fee

One scope, one price. No hourly meter, no standups-by-the-hour, no scope that quietly inflates. You know the number before we start.

Eval-driven acceptance

Done is a number, not a feeling. Faithfulness thresholds, cost-per-call attribution, and regression gates are agreed up front — so "finished" is something we can both point at.

You keep everything

Code, harnesses, dashboards, and docs land in your repos. No black boxes, no lock-in, no dependency on us to read your own metrics next quarter.

Senior, async, remote

Two senior engineers do the work — the people who pitch are the people who build. Video calls when they move things forward; otherwise we stay out of your calendar.

04 / Open toolkit

Read the code before you read a pitch.

Our LLM cost & quality audit toolkit is public. The methodology, the eval harness, the cost-attribution approach — all of it open, so you can judge how we think before a single call.

View the toolkit on GitHub

30–65%

is what audits typically surface — LLM spend sitting on requests that don't need the full model. It's what the audit reveals, not a guaranteed result; your stack will vary.

05 / About

A two-person studio, not a staffing shop

Two senior data engineers, eight years building Databricks Lakehouse platforms and, more recently, evaluating GenAI in production.

That last part is the unglamorous one — the work where you actually find out whether the AI is good and what it really costs, instead of assuming. It's where most teams are flying blind, and it's exactly what we're here for.

EU-based, fully remote, async-first. The people who pitch you are the people who write the code. Your legal and compliance team owns regulatory interpretation; we own the engineering.

8 yrs

Databricks Lakehouse + production GenAI eval

2 engineers

Senior. No middle layer, no handoff.

Fixed-fee

Outcomes, not hours. One scope, one price.

06 / Let's talk

If your AI is in production and you're flying blind on quality or cost — that's the conversation.

Send a line about what you shipped and what you can't currently measure. We'll tell you whether we're the right people, and which door fits.

Async-first · we reply within a couple of working days