If you already run an AI agent, start by measuring it. If you are automating a workflow your team does by hand, start by proving it on your own documents. Both tracks end the same way: a system with evals, kept accurate by a retainer.
You shipped an agent. Now you need to know when it breaks, before your users do, and whether the next model will break it.
Entry · AI teams · 2–3 weeks
The Agent Reliability Audit is a fixed-scope, 2–3 week engagement that turns your production traces into an eval harness your team runs on every change. We sample your traces, classify how the agent actually fails, build a golden set of 150 to 300 cases, calibrate an LLM judge against your team’s labels, and wire the result into CI.
For: CTOs and Heads of AI at seed to Series B companies with an agent in production
Book a reliability callEntry · AI teams · 2–3 weeks
Model Migration is an eval-gated switch to a newer or cheaper model, scored side by side on your own traffic. In 2–3 weeks you get quality, cost and latency compared case by case on a golden set, routing and caching where they pay off, and a cutover plan with a rollback path, so the decision rests on measurements rather than a benchmark someone else ran.
For: AI teams facing a model release, a deprecation notice, or a large inference bill
Book a migration callYou have a workflow your team still does by hand. Prove it on your own documents first, then build it properly.
Entry · Operations · 2 weeks
The Workflow Pilot is a two-week, fixed-scope engagement: we build a working extraction or decision pipeline for one workflow, run it on your real documents, and measure its accuracy on about 200 samples. Nobody should sign a build contract to find out whether an AI system can handle their paperwork.
For: Operations leaders, mainly in logistics and manufacturing
Book a pilot callCore · 4–10 weeks
The Production Agent Build ships the agent, its integrations and a human exception queue in 4–10 weeks, with evals and monitoring running from the first day rather than bolted on at the end. Most agent projects stall because nobody can prove the thing is right. We ship the proof alongside the system.
For: Pilot and audit clients, and SaaS founders shipping AI features
Scope a buildAn agent in production drifts. The retainer re-runs the evals on every change and every model release, so accuracy stays a number rather than a memory.
Ongoing · Monthly
The Reliability Retainer keeps a production agent accurate after launch: regression runs on every change and every model release, judge recalibration as your traffic shifts, prompt and tool fixes, and a monthly report on where quality stands. Document formats change, user behaviour drifts, models get deprecated. Something has to notice.
For: Every audit and build client
Talk about the retainerMost automation should be built in a no-code tool, and we will say so on the call. The work worth paying an engineer for is the work those tools quietly fail at: irregular documents, exceptions, and knowing whether the output is actually right.
| Comparison criterion | A no-code platform | Hiring in-house | Working with Rottawhite |
|---|---|---|---|
| Simple, tidy automation | Genuinely the right answer. Use it. | Overkill | We will tell you to use Zapier |
| Messy documents, scans, exceptions | Falls over on anything irregular | Depends entirely who you hire | This is Rottawhite's core work |
| Proving it is accurate | No measurement of correctness | Only if they build it | Test set scored on every change |
| When a document format changes | Breaks quietly; you find out late | Someone notices, eventually | Drift monitoring flags it |
| Time to first working result | An afternoon | 2–4 months to hire | Two weeks |
| Cost to find out if it works | A monthly subscription | A salary | A two-week pilot, credited to the build |
| Who owns it afterwards | The platform. You rent access. | You do | You do: code and test set |
Two builds with their numbers published: ELISRUN (60% defect reduction, +40% production) and Thinklusive (60% of manual work eliminated, 3x faster delivery).
An AI system is easy to sell and hard to verify. Every entry engagement ends in a measured number from your own traffic or documents, so the next decision rests on evidence rather than a demo.
Scope and fee are agreed on a call before kickoff and do not move after it. The pilot is credited in full toward a build.
Code, golden sets and harnesses live in your repo whether or not you continue with us.
If the numbers say the workflow or the migration is not ready, we say so, with the evidence.
Next step
Audit the agent you already run, or pilot one workflow on your own documents. Either way, you leave with a number you can check.